1e:I[23489,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js","/_next/static/immutable/chunks/2bmdwv49ba1qx.js","/_next/static/immutable/chunks/3ld61_1ghpsxn.js"],"ShareMenu"] 1f:I[55078,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js","/_next/static/immutable/chunks/2bmdwv49ba1qx.js","/_next/static/immutable/chunks/3ld61_1ghpsxn.js"],"AuthorMetricsPanel"] 20:I[39060,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js","/_next/static/immutable/chunks/2bmdwv49ba1qx.js","/_next/static/immutable/chunks/3ld61_1ghpsxn.js"],"AuthorPapers"] 46:I[49310,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js"],"IconMark"] 21:T610,The main aim of this study is to investigate the opportunities, challenges, and barriers in implementing generative artificial intelligence (Gen AI) in personalized patient care plans (PPCPs). This systematic review paper provides a comprehensive analysis of the current state, potential applications, and opportunities of Gen AI in patient care settings. This review aims to serve as a key resource for various stakeholders such as researchers, medical professionals, and data governance. We adopted the PRISMA review methodology and screened a total of 247 articles. After considering the eligibility and selection criteria, we selected 13 articles published between 2021 and 2024 (inclusive). The selection criteria were based on the inclusion of studies that report on the opportunities and challenges in improving PPCPs using Gen AI. We found that a holistic approach is required involving strategy, communications, integrations, and collaboration between AI developers, healthcare professionals, regulatory bodies, and patients. Developing frameworks that prioritize ethical considerations, patient privacy, and model transparency is crucial for the responsible deployment of Gen AI in healthcare. Balancing these opportunities and challenges requires collaboration between wider stakeholders to create a robust framework that maximizes the benefits of Gen AI in healthcare while addressing the key challenges and barriers such as explainability of the models, validation, regulation, and privacy integration with the existing clinical workflows.22:T7dc,CONTEXT.—: Generative artificial intelligence (AI) has emerged as a transformative force in various fields, including anatomic pathology, where it offers the potential to significantly enhance diagnostic accuracy, workflow efficiency, and research capabilities. OBJECTIVE.—: To explore the applications, benefits, and challenges of generative AI in anatomic pathology, with a focus on its impact on diagnostic processes, workflow efficiency, education, and research. DATA SOURCES.—: A comprehensive review of current literature and recent advancements in the application of generative AI within anatomic pathology, categorized into unimodal and multimodal applications, and evaluated for clinical utility, ethical considerations, and future potential. CONCLUSIONS.—: Generative AI demonstrates significant promise in various domains of anatomic pathology, including diagnostic accuracy enhanced through AI-driven image analysis, virtual staining, and synthetic data generation; workflow efficiency, with potential for improvement by automating routine tasks, quality control, and reflex testing; education and research, facilitated by AI-generated educational content, synthetic histology images, and advanced data analysis methods; and clinical integration, with preliminary surveys indicating cautious optimism for nondiagnostic AI tasks and growing engagement in academic settings. Ethical and practical challenges require rigorous validation, prompt engineering, federated learning, and synthetic data generation to help ensure trustworthy, reliable, and unbiased AI applications. Generative AI can potentially revolutionize anatomic pathology, enhancing diagnostic accuracy, improving workflow efficiency, and advancing education and research. Successful integration into clinical practice will require continued interdisciplinary collaboration, careful validation, and adherence to ethical standards to ensure the benefits of AI are realized while maintaining the highest standards of patient care.23:T4ae,OBJECTIVES: The present study was designed to determine the serum vitamin D levels and their relation with demographic features and life style factors in young adults. METHODS: It was an analytical cross-sectional study on 88 subjects aged 18-40 years. Relevant information about physical activity, job place, duration of sun exposure, educational status and socioeconomic conditions was obtained. Serum levels of 25-OH vitamin D were measured by ELISA. Data was analyzed using SPSS 20. RESULTS: Mean serum vitamin D level was 8.44±0.49 (Range: 1.00-21.08) ng/ml in participants. Vitamin D deficiency was found in 98.86% of the population. Mean vitamin D levels were significantly lower in females (p=0.0001), physically less active (p=0.006), indoor job holders (p=0.0001), less sun exposed (p=0.001), highly educated (p=0.020) and high socioeconomic status (p=0.007) bearing and in subjects having relatively fair skin complexion (p=0.041). CONCLUSIONS: Serum vitamin D levels of study population were below normal and were associated with female gender, less physical activity, indoor jobs, less sun exposure, higher education and higher socioeconomic class and relatively fair skin complexion.24:T434,The emergence of New Data Sources (NDS) in healthcare is revolutionising traditional electronic health records in terms of data availability, storage, and access. Increasingly, clinicians are using NDS to build a virtual holistic image of a patients health condition. This research is focused on a review and analysis of the current legislation and privacy rules available for healthcare professionals. NDS in this project refers to and includes patient-generated health data, consumer device data, wearable health and fitness data, and data from social media. This project reviewed legal and regulatory requirements for New Zealand, Australia, the European Union, and the United States to establish the ground reality of existing mechanisms in place concerning the use of NDS. The outcome of our research is to recommend changes and enhancements required to better prepare for the ’tsunami’ of NDS and applications in the currently evolving data-driven healthcare area and precision or personalised health initiatives such as Precision Driven Health (PDH) in New Zealand.25:T48b,OBJECTIVES: To find out the risk factors of diabetic retinopathy in type 2 diabetes mellitus. METHODOLOGY: It was a cross-sectional study involving 300 patients of type 2 diabetes. Clinical history, relevant examination including fundoscopy and lab investigations were done. Data was analysed with SPSS 17.0. T-test and chi square/Fischer exact were applied to determine significance. RESULTS: Mean age of the patients was 49.04 ± 0.69 years with slight female predominance with male to female ratio of 3:4. Average duration of disease was 7.17 ± 0.38 years. Diabetic retinopathy was diagnosed in (74, 23.9%). Mean HbA1c was 8.15% in patients with retinopathy and 8.884% in those who had no retinopathy (p=0.08). However, duration of DM, age of patients, male gender, high total cholesterol, high LDL and microalbuminuria were significantly associated with the development of retinopathy. CONCLUSIONS: Diabetic retinopathy was found in 23.9% of type 2 diabetics. It was associated with duration of disease, age at presentation, male gender, high total cholesterol, high LDL and microalbuminuria. A single high level of HbA1c was not associated with retinopathy.26:T4e3,We performed FMEA on the existing RRS with the help of routine users of the RRS who acted as subject matter experts and evaluated the failures for their criticality using the Risk Priority Number approach based on their experience of the RRS. The FMEA found 35 potential failure modes and 101 failure mode effects across 13 process steps of the RRS. The afferent limb of RRS was found to be more prone to these failures (62, 61.4%) than the efferent limb of the RRS (39, 38.6%). Modification of calling criteria (12, 11.9%) and calculation of New Zealand Early Warning Scores (NZEWS) calculation (11, 10.9%) steps were found to potentially give rise to the highest number of these failures. Causes of these failures include human error and related factors (35, 34.7%), staff workload/staffing levels (30, 29.7%) and limitations due to paper-based charts and organisational factors (n = 30, 29.7%). The demonstrated electronic system was found to potentially eliminate or reduce the likelihood of 71 (70.2%) failures. The failures not eliminated by the electronic RRS require targeted corrective measures including scenario-based training and education, and revised calling criteria to include triggers for hypothermia and high systolic blood pressure.27:T615,INTRODUCTION: Dengue virus (DENV) affects over half the world's population in 112 countries, and dengue fever (DF) is the second largest arthropod borne infectious global hazard after malaria with complications like Dengue Hemorrhagic Fever (DHF) and Dengue Shock Syndrome (DSS), accounting for significant morbidity and mortality world-over. Pakistan is significantly affected with DENV infection and to-date no study identifying risk factors associated with development of severe complications of DF has been done. METHODS: 997 confirmed cases of DF were collected from a tertiary care hospital in Lahore, Pakistan and their clinical and biochemical data were collected. Univariate, multivariate and logistics regression analysis was performed to identify risk factors associated with development of DHF and DSS. RESULTS: Bleeding OR 70.7 (CI 38.4-129.9), deranged liver function test OR 1.9 (CI 0.97-0.99), presence of urinary red blood cells OR 1.4 (95%CI 0.179-0.900) and presence of urinary protein OR 1.1 (95%CI 0.191-0.974) were related to development of DHF and DSS. DISCUSSION: Severe Dengue, like DHF and DSS can be predicted by the presence of clinical and biochemical factors like signs of bleeding, deranged liver function test, presence of urinary red blood cells and urinary protein; so that the patients at high risk for complication be identified early and started on treatment timely. CONCLUSION: Predictors of severe dengue are identified in this study but further large scale multi-centered studies are needed for better interpretation.28:T5b8,BACKGROUND: Prediabetes and type 2 diabetes mellitus (T2DM) are one of the major long-term health conditions affecting global healthcare delivery. One of the few effective approaches is to actively manage diabetes via a healthy and active lifestyle. OBJECTIVES: This research is focused on early detection of prediabetes and T2DM using wearable technology and Internet-of-Things-based monitoring applications. METHODS: We developed an artificial intelligence model based on adaptive neuro-fuzzy inference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence, and calories). The data was collected using an advanced wearable body vest and combined with manual recordings of blood glucose, height, weight, age, and sex. The model analyzed the data alongside a clinical knowledgebase. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines, and protocols. RESULTS: The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%. CONCLUSION: We also present a 2-year follow-up observation from the prediction results of the original model. Moreover, the diabetic profile of a participant using M-health applications and a wearable vest (smart shirt) improved when compared to the traditional/routine practice.29:T73e,INTRODUCTION: Angiogenesis and mast cells affect the behavior of pulmonary adenocarcinoma. Measuring the angiogenesis and mast cell density (MCD) and their effect on survival of the patients may be helpful to guide the use of cancer chemotherapeutic agents which target tumor angiogenesis and mast cells. MATERIALS AND METHODS: It was a descriptive study, conducted at Gulab Devi Chest Hospital and University of Health Sciences Lahore. It included 23 newly diagnosed, adult patients of invasive pulmonary adenocarcinoma. Clinical history was obtained and biopsy specimen was processed. Angiogenesis was determined by immunohistochemical staining with CD34. Mast cells were counted in toluidine blue stained sections. Patients were followed-up till death. RESULTS: Mean age of the patients was 54.83 ± 2.799 years. Majority (60.9%) were males. Only 39.1% patients were smokers. Morphologically, large proportions of tumors (73.9%) were nonmucinous and moderately or poorly differentiated. Majority (69.6%) of patients presented at advanced stage (Tumor-Node-Metastasis staging (TNM )III and IV). Mean microvascular density (MVD) was 13.04 ± 1.12 and mean MCD was 3.26 ± 0.36 per high power field (HPF). High MVD was associated with poor differentiation and advanced stage and correlated with poor survival (P = 0.0001). High MCD was associated with high grade but not with the advance stage of disease. However, high MCD correlated with poor survival (P = 0.047). Moreover, MCD was positively correlated with angiogenesis (r = 0.727, P = 0.0001). Treatment did not affect the survival significantly (P = 0.069). CONCLUSION: High angiogenesis and MCD predict poor survival and are positively correlated with each other and with the histological grades in pulmonary adenocarcinoma. High angiogenesis is also associated with advance TNM stage of disease.2a:T632,BACKGROUND: Measuring the microvascular and mast cell density in squamous cell carcinoma of lung and correlating them with the patient survival may be helpful to guide the use of cancer chemotherapeutic agents which target molecular mechanisms of tumour angiogenesis and mast cells. MATERIALS AND METHODS: It was an observational study. It included 39 newly diagnosed, adult patients of pulmonary squamous cell carcinoma. Angiogenesis was determined by Chalkley's method after immunohistochemical staining of micro-vessels with CD34. Mast cells per HPF were counted in Tolouidine blue stained sections. RESULTS: Mean age of the patients was 58.33 ± 9.14 years. Male to female ratio was 9:1. Most (92.3%) patients were current smokers. Majority of tumours (71.8%) were localised to major bronchi and/or near to hilum and many of them (74.4%) were poorly differentiated. Mean micro-vascular density was 11.80 ± 3.66 per HPF which showed strong negative correlation (r = -0.481, p =0.002) between microvascular density (MVD) and tumour grade. Mean mast cell density was 1.60 ± 2.04 which showed strong negative correlation (r=-.683, p =0.0001) with grade. Angiogenesis and mast cell density were found to be positively correlated (r=0.439, p =0.005). High MVD, but not the MCD was associated with poor survival. CONCLUSION: Angiogenesis and mast cell density are positively correlated with each other however; only high MVD is associated with decreased survival. Thus, the anti-angiogenic agents may be useful in squamous cell carcinoma lung, especially the well differentiated tumours.2b:T762,Objective This study aimed to quantify the workload involved in patient monitoring by vital signs and early warning scores (EWS), and the time spent by a rapid response team locally known as the Patient-at-Risk (PaR) team in responding to deteriorating patients. Methods The workload involved in the measurement and the documentation of vital signs and EWS was quantified by time and motion study using electronic stopwatch application in 167 complete sets of vital signs observations taken by nursing staff on general hospital wards at Taranaki Base Hospital, New Plymouth, New Zealand. The workload involved in responding to deteriorating patients was measured by the PaR team in real-time and recorded in an electronic logbook specifically designed for this purpose. Dependent variables were studied using analysis of variance (ANOVA), post hoc Tukey, Kruskal Wallis test, Mann-Whitney test and correlation tests. Results The mean time to measure and record a complete set of vital signs including interruptions was 4:18 (95% CI: 4:07–4:28) minutes. After excluding interruptions, the mean time taken to measure and record a set of vital signs was 3:24 (95% CI: 3:15–3:33) minutes. We found no statistical difference between the observer, location of the patient, staff characteristics or experience and patient characteristics. PaR nurses' mean time to provide rapid response was 47:36 (95% CI: 44:57–50:15) minutes. Significantly more time was spent on patients having severe degrees of deterioration (higher EWS) < 0.001 . No statistical difference was observed between ward specialty, and nursing shifts. Conclusions Patient monitoring and response to deterioration consumed considerable time. Time spent in monitoring was not affected by independent and random factors studied; however, time spent on the response was greater when patients had higher degrees of deterioration.2c:T48e,Introduction: Mucins implicated in cancers of various organs.The apical epithelial surfaces of mammalian respiratory, gastrointestinal, and reproductive tracts are coated by mucus, a mixture of water, ions, glycoproteins, proteins, and lipids.The purpose of this study was to confirm the presence of mucin production using Haematoxylin and Eosin (H&E) stain as the gold standard and to describe the types of mucins produced in tumors of lung, colon and ovaries using various types of histochemical techniques.Methods: The resection specimens and biopsies from tumours of colon (n=16), ovaries (n=13) and lung (n=5) were included and stained with H&E to determin the histological diagnosis for selecting tissues with mucin production.Slides were stained with PAS, Alcian blue, High iron diamine-Alcian blue, Meyer's mucicarmine and Alcian blue-PAS to demonstrate the mucin production and to identify types of mucins.Results: In the present study we observed predominance of acid mucins over neutral mucins.In addition in these cases we observed sulphomucin predominating over sialomucin. Conclusion: Mucin histochemistry can effectively determine the types of mucins.2d:T598,Background: The risk of gestational diabetes mellitus is said to be higher if the expecting mother is overweight. We conducted this study to compare the frequency of gestational diabetes mellitus in second and third trimester in obese and non-obese women. Methods: It was a prospective study conducted at Department of Obstetrics and Gynaecology, Bahawal Victoria Hospital Bahawalpur. Sixty two obese and 62 non-obese women having singleton pregnancy were registered for the study at their booking visit at ~14-20 weeks of gestation. Women having family history of obesity and chronic ailments and those with age >35 or parity > 5 were excluded. Chi-square test and independent sample t-test were applied to determine statistical significance. A p-value of < 0.05 was considered significant. Results: Mean age was 28.12±2.72 years among the group of obese women as compared to 27.89±2.34 years in non-obese group. Commonest parity among the obese women was 3-4 (n=31, 50%) compared to 46.8 % (n=29) in non-obese group. Development of gestational diabetes mellitus was 22.58% (n=14) in obese group which was significantly higher than 6.45% (n=4) in non-obese group (p=0.001). Conclusion: It is concluded that gestational diabetes mellitus develops significantly higher in obese women as compared to non-obese women as assessed by BMI in early pregnancy. Key Words: Obesity, Gestational diabetes mellitus, Body Mass Index, Pregnancy2e:T43d,Worldwide spending on long-term and chronic care conditions is increasing to a point that requires immediate interventions and advancements to reduce the burden of the healthcare cost. This research is focused on early detection of prediabetes and type 2 diabetes mellitus (T2DM) using wearable technology. An artificial intelligence model was developed based on adaptive-neuro fuzzy interference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence and calories). The data was collected using an advanced wearable body vest. The real-time data was combined with manual recordings of blood glucose, height, weight, age and sex. The model analyzed the data alongside a clinical knowledge-base. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines and protocols. The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%.2f:T476,Objective: To assess effect of tranexamic acid on blood loss during and after cesarean section. Methodology: We performed a randomized controlled trial at Bahawal Victoria Hospital, Bahawalpur from March to December 2013. A total of 120 parturient women with singleton gestation planned to have cesarean section (CS) at ≥37 weeks were randomized to receive 1 g tranexamic acid slowly intravenously over 10 minutes or placebo injection 20 minutes before CS. Blood loss was measured during and for up to two hours after the operation. Any side effects, complications, medications required, and duration of hospital stay were recorded. Results: Sixty women received transexamic acid and 60 received placebo. Mean blood loss was 260±11 ml vs 485±16 ml in the tranexamic acid group versus the control group (p = 0.003). Only mild, transient adverse effects like nausea and visual disturbances were observed in study group. Conclusions: Administration of tranexamic acid was associated with reduced blood loss during and after CS. This could be of benefit for anemic women or those who either refuse blood transfusion or can’t arrange donors.30:T679,This review focuses on the purported applications of multimodal Gen-AI models for anatomic pathology image analysis and interpretation to predict future directions. A scoping review was conducted to explore the applications of multimodal Gen-AI models in advancing histopathology image analysis. A comprehensive search was conducted using electronic databases for relevant articles published within the past year (July 1, 2023 to June 30, 2024). The selected articles were critically analyzed to identify and summarize the applications of multimodal Gen-AI in anatomic pathology image analysis. Multimodal Gen AI models reported in the literature claim moderate to high accuracy on tasks including image classification, segmentation, and text-to-image retrieval. This review demonstrates the potential of multimodal Gen AI models for useful applications in pathology, including assisting with diagnoses, generating data for education and research, and detection of molecular features from anatomic pathology images. These models use data from a few academic institutions thus they require validation on diverse real-world data. There is an urgent need to build consensus models for optimal model performance through multicenter collaboration using a federated learning approach and the use of carefully curated synthetic anatomic pathology data. These models also need to achieve reliability, generalizability and meet the standards required for clinical use. Despite the rigorous need for evaluation and the need to address genuine concerns, multimodal GenAI models present a promising perspective for the advancement and scalability of anatomic pathology.31:T42f,Xeroderma pigmentosum is an uncommon inherited autosomal recessive disorder. Affected patients have a 2000-fold amplified risk of skin cancer. There is an inability to repair the damage to genetic material caused by ultraviolet light. Basal cell carcinoma is the most commonly associated carcinoma followed by squamous cell carcinoma and melanoma. It is a highly mutilating disorder where occurrence of multiple cancers and repeated surgical treatments result in serious psycho-social implications. We present a case of diagnosed xeroderma pigmentosum in a 25 year old male who presented with multiple lesions and non-healing ulcers on face. Four surgical specimens from left lower eyelid, chin, right cheek and upper lip were taken for histopathological diagnosis. Microscopic examination revealed five different tumors from biopsies of these sites - specimen from right cheek revealed two morphologically distinct tumors. The tumors include basosquamous carcinoma, nodular basal cell carcinoma, adenoid basal cell carcinoma, malignant melanoma and cavernous hemangioma.32:T520,Accurate analysis of tissue samples is important for the detection and treatment of prostate cancer, a disease prevalent in males worldwide. The Gleason score is an important measure system that is used by doctors to grade prostate cancer. It shows how likely the cancer will spread based on the distribution of the cancer cells appear under a microscope. Manual assessment of cells from digitized stained biopsy images is pathologist-dependent, subjective, and tends to produce variable and sometimes conflicting evaluations. Recent deep learning advances have given promising outcomes to assess benign and malignant cells which leads to accurate and efficient grading, overcoming weaknesses in conventional approaches. In this work, a variety of deep neural networks, such as InceptionV3, ResNet50, and InceptionResNetV2, have been used for prostate cancer diagnosis using histopathology slides. Further, the Gleason score is calculated to predict the severity of prostate cancer. From the experiments, it is observed that InceptionResNetV2 outperformed other models with an accuracy of 91.8%, and closely reflects expert pathologist grading. By blending artificial intelligence with medical practice, these advances pave the way for enhanced accuracy in cancer diagnostics, patient care, and timely detection.33:T619,Objectives: The aim of this study was to analyse the current use, identify challenges and barriers and propose a way forward for the use of the pager devices in the in-hospital communications. Methods: Initially, 447 studies were identified through database searching. After checking against the eligibility, 39 studies were included. Full-text records were retrieved and reviewed by two authors. After excluding unrelated studies and duplicate records, a total of 12 articles were selected for the final review. Results: The use of pagers often lacks standardisation, content, format, urgency level, and clarity within the message. Some studies reported that medical staff preferred in-person interactions with consults instead of communicating over the phone or pagers. Productive communication can reduce the turnaround time by up to 50%. The key challenges are; (1) data security and privacy, (2) timely acknowledgement of received communication, (3) lack of two-way communications causing issues in critical care situations and (4) there is no standard process for the in-hospital communications. Conclusion: We found that the clinicians' age, experience, speciality and preferences greatly matter and influence the selection of tools and technology in healthcare. With revolutionary advances in technology, smartphones have inevitably become beneficial to healthcare, owing to multiple instant messaging applications (apps) that can streamline encrypted clinical communication between medical teams and could be safely used for in-hospital communications.34:Tb95,A bstract Surgical pathology reports provide essential diagnostic information critical for cancer staging, treatment planning, and cancer registry documentation. However, their writing styles and formats vary widely, reflecting each pathologist’s stylistic choices, institutional norms, and largely inherited practices from residency training. When performing large-scale data analysis, this unstructured nature and variability across tumor types and institutions pose significant hurdles for automated data extraction. To overcome these challenges, we present a consensus-driven, reasoning-based framework that adapts multiple locally deployed large language models (LLMs) to extract both standard diagnostic variables (site, laterality, histology, stage, grade, and behavior) and organ-specific biomarkers. Each LLM generates structured outputs, accompanied by justifications, which are subsequently evaluated for accuracy and coherence by three separate reasoning models (DeepSeek-R1-large, Qwen3-32B, and QWQ-32B). Final consensus values are determined through aggregation. Board-certified pathologists conducted expert validation. This framework was applied to over 6,100 pathology reports from The Cancer Genome Atlas (TCGA) spanning 10 organ systems and 510 reports from Moffitt Cancer Center. For the TCGA dataset, automated evaluation demonstrated mean accuracy of 84.9% ± 7.3%, with histology (89.0%), site (88.3%), and behavior (87%) showing the highest extraction accuracy averaged across all models. Expert review of randomly selected 138 reports confirmed high agreement for behavior (100.0%), histology (99%), grade (97%), and site (95%) in the TCGA dataset, with slightly lower performance for stage (88%) and laterality (87%). In Moffitt Cancer Center reports (brain, breast, and lung), accuracy remained high (88.2% ± 7.2%), with behavior (99%), histology and laterality (96%), grade (93%), and site (91%) achieving strong agreement. Biomarker extraction achieved 70.6% ± 8.1% overall accuracy, with TP53 (85%) on brain tumor, Ki-67 (68%) on breast cancer, and ROS1 (82%) on lung cancer showing highest accuracy. Inter-evaluator agreement analysis revealed high concordance (correlations > 0.89) across the three evaluation models. Statistical analyses revealed significant main effects of model type (F=1716.82, p<0.001), variable (F=3236.68, p<0.001), and organ system (F=1946.43, p<0.001), as well as model × variable × organ interactions (F=24.74, p<0.001), emphasizing the role of clinical context in model performance. These results highlight the potential of stratified, multi-organ evaluation frameworks with multi-evaluator consensus in LLM benchmarking for clinical applications. Overall, this consensus-based approach demonstrates that locally deployed LLMs can provide a transparent, accurate, and auditable solution for integration into real-world pathology workflows such as synoptic reporting and cancer registry abstraction.35:T69f,Aims and objectives: This study investigated clinical staff perceptions of learning about current monitoring practices and the planned introduction of an electronic system for patient monitoring. The aim of this research was to evaluate the perceptions of clinical staff (nurses and doctors) about the perceived strengths and weaknesses of the current state of the rapid response system (RRS) and how those strengths and weakness would be affected by introducing an electronic RRS. Methods: This research applied a descriptive study methodology. Two detailed sessions on demonstration on the electronic RRS for measuring and recording vital signs and the electronic Early Warning System (EWS) were followed by two structured surveys administered through an online portal (SurveyMonkey) for nurses and doctors working at Taranaki District Health Board. The study was planned and conducted between October 2020 and May 2021 at Taranaki Base Hospital, New Plymouth, New Zealand. Results: We found that the perceptions of clinical staff were a combination of key practice issues with current manual monitoring, expectations of improved visibility of vital sign charts, better communication between staff and thus improved patient care with the introduction of an electronic system. A majority (24, 60%) of nurses reported that, when called to assess deteriorating patients, the responders arrive at bedside within 5-30 min and an additional 11 (27%) said the responders arrive within 5 min. That is a collective 87% responder arrival within 30 min. Conclusion: Staff believe that an electronic RRS could improve communication, speed up decision making and have a positive impact on patient outcomes.36:T720,Diagnostic pathology depends on complex, structured reasoning to interpret clinical, histologic, and molecular data. Replicating, or even approximating this cognitive process algorithmically remains a significant challenge. As large language models (LLMs) gain traction in medicine, it is critical to assess their clinical utility in specialized domains such as pathology. We evaluated the quality of expressed diagnostic reasoning in the outputs of four LLMs, OpenAI o1, OpenAI o3-mini, Gemini 2.0 Flash Thinking Experimental, and DeepSeek-R1, using 15 constructed-response pathology questions covering board-examination-level content across a range of cognitive demands, from factual recall to integrative knowledge synthesis. Eleven expert pathologists independently scored model-generated responses using a structured rubric encompassing five language quality metrics (accuracy, relevance, coherence, depth, and conciseness) and seven diagnostic reasoning strategies as communicated in the textual output. Scores were normalized and compared using ANOVA and Tukey’s HSD. Gemini significantly outperformed all models in overall reasoning quality, particularly in analytical depth and coherence. While all models achieved comparable factual accuracy, only Gemini and DeepSeek consistently demonstrated expert-like reasoning patterns in their outputs, including algorithmic, inductive, and Bayesian approaches. Performance varied by reasoning type, with the highest scores in algorithmic and deductive reasoning and the lowest in heuristic and pattern recognition. Gemini achieved the highest inter-observer agreement (p < 0.001), suggesting greater interpretability. However, more in-depth reasoning was generally associated with reduced conciseness. Addressing these trade-offs will be key to safe clinical integration.37:T650,The transformation of anatomic pathology from microscope-based practice to digital and computational workflows has created unprecedented opportunities for artificial intelligence (AI)-driven diagnostics. Whole slide imaging systems, digital cytology, and enterprise pathology platforms now enable large-scale image analysis, quantitative tissue characterization, and integration with molecular and clinical data. In parallel, advances in machine and deep learning, and generative models have produced AI systems capable of tumor detection, grading, prognostication, biomarker assessment, and molecular inference directly from routine histologic and cytologic slides. Despite this rapid progress, translation of AI from research environments into routine clinical anatomic pathology remains limited. Barriers include data heterogeneity and limited generalizability, challenges in validation and regulatory compliance, infrastructure and interoperability constraints, workflow integration difficulties, and concerns regarding transparency, accountability, and professional trust. This review synthesizes current evidence on digital pathology and AI applications across surgical pathology and cytopathology, examines the technical, organizational, and regulatory factors that impede clinical adoption, and outlines practical recommendations for developing clinically deployable AI systems. Addressing these challenges through robust digital infrastructure, representative data sets, rigorous validation, and coordinated governance will be essential for realizing the full clinical potential of AI in anatomic pathology.38:T5cf,Artificial Intelligence (AI) is evolving at a rapid pace with impressive performance in many areas including health care. This study aimed to investigate the opportunities, challenges and barriers in implementing AI in patient monitoring using smart medical devices. This chapter provides a comprehensive analysis of the current state, potential applications, benefits, and challenges associated with the integration of AI into patient care settings. It could serve as a valuable resource for researchers, practitioners and policymakers interested in the transformative impact of AI on patient care. We reviewed studies that report on the opportunities and challenges in improving personalized patient care plans (PPCP) using AI. It is suggested that a holistic approach is required involving strategy, communications, integrations and collaboration between technology developers, healthcare professionals, regulatory bodies and end users including physicians and patients. Developing frameworks that prioritize ethical considerations, patient privacy, and model transparency is crucial for the responsible deployment of AI in healthcare. Balancing these opportunities and challenges requires collaboration between wider stakeholders to create a robust framework that maximizes the benefits of AI in healthcare while addressing the key challenges and barriers such as the explainability of the models, validation, regulation, and privacy integration with the existing clinical workflows.39:T4c2,Abstract This review investigates the effectiveness of exploiting the massive Artificial Intelligence (AI) technology in the diagnosis of prostate cancer histopathological images. It focuses on studying and analyzing the current state and practice for utilizing AI tools, including significant machine learning and deep learning models in the histopathological image analysis process. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology was adopted for conducting this systematic review to include recent research articles that have been published since 2017. Leveraging novel deep learning models and advanced imaging techniques, AI demonstrates promising capabilities in improving accuracy and efficiency in detecting and classifying prostate cancer. A comprehensive comparison of existing works has been presented with in-depth discussions around current limitations and key challenges while proposing some future advancements. This study aims to pave the way for future research and further integration of AI into the diagnostic processes toward early detection, personalized treatment strategies, and enhanced patient outcomes in the context of a prostate cancer diagnosis.3a:T6f3,Introduction: The relative incidence of adenocarcinoma of the lung is increasing, and it is now the most common primary lung malignancy. This study examines certain clinical and pathological factors which may affect patient survival in lung adenocarcinoma in the developing country of Pakistan. Methods: This is a descriptive study involving 56 patients with biopsy-proven primary adenocarcinoma of lung. Biopsy tissues were reviewed along with medical record case histories. Formalin-fixed, paraffin-embedded tissue specimens were used for mucin histochemistry with mucicarmine, periodic acid Schiff (PAS) and alcian blue stains. Immunohistochemistry (IHC) with monoclonal antibodies against CD34 was used for assessment of microvascular density. Mast cell density was measured on Toluidine blue stained sections. Results: Mean age of patients was 55.96 ± 1.67 years. Male to female ratio was 3:2. 51.8% of patients were smokers, and weight-loss was seen in 46.4% of the patients. A majority (66.1%) had stage III/IV tumors with positive lymph nodes, and 26.8% tumors were mucinous. High mast cell density was found in 32.1% and high microvessel density in 44.6% tumor specimens. Weight-loss, stage, nodal status, mucin production and microvessel density showed significant deleterious effect on patient survival. Conclusions: The current study illustrates that for this population, weight loss, advanced stage of tumor, nodal status, mucin production by the tumor, and increased angiogenesis are predictors of poor survival in patients with primary pulmonary adenocarcinoma. Further prospective work will need to be performed to determine if these factors can assist in prognostication or formulation of treatment plan in newly diagnosed individuals in the general population.3b:T4ce,Objective: To see the frequency of coronary artery stenosis in the patients of stable angina having left bundle branchblock, and to see the effects of gender and age on this correlation. Design: Experimental case series. Setting: Bahawal Victoria Hospital,Bahawalpur. Period: June 2006 to May 2008. Methods: One hundred and forty one (141) patients of Stable Angina presenting with atypicalchest pain, both male and female of 30—70 years age, positive for LBBB and with normal Cardio-Thoracic Ratio, normal echocardiographyand negative for conventional risk factors and family history of Coronary Artery Disease were taken. All patients were angiographied .Results: A total of 141 patients were investigated through coronary angiogram to see the frequency of coronary artery stenosis in thesepatients. Ninety five (67.4%) patients proved to have coronary artery stenosis. The male patients showed higher frequency (87.4%) ofharboring coronary vessel blockade as compared to that (35.2%) in females (P value <0.0005). There was no relation between age and thefrequency of coronary stenosis (P value >0.05). Conclusions: There is significant difference (P value <0.0005) in coronary artery stenosisamong men and women having LBBB.3c:T6a2,Once-weekly dipeptidyl peptidase-4 inhibition represents a potential strategy to reduce dosing burden while maintaining glycaemic efficacy in type 2 diabetes mellitus. Objectives: To compare once-weekly trelagliptin with once-daily vildagliptin in adults receiving background metformin, using change in glycated hemoglobin and attainment of glycated hemoglobin below 7%, with secondary assessment of body weight and body mass index. Methods: A randomized, open-label, parallel-group clinical trial was conducted at a tertiary care hospital. Adults aged 30 to 60 years with type 2 diabetes mellitus, glycated hemoglobin 7.5% to 9.5% on metformin, were randomized by lottery to trelagliptin 100 mg once weekly or vildagliptin 50 mg once daily for 12 weeks. Independent-samples t testing and chi-square or Fisher's exact testing were applied as appropriate. Prespecified stratified analyses were performed for the efficacy endpoint. Results: A total of 102 participants were randomized, and 93 completed follow-ups. Baseline characteristics were comparable. Glycated hemoglobin change was −1.24±0.78% with trelagliptin and −1.52±0.67% with vildagliptin, mean difference 0.28% (95% CI −0.02 to 0.58), p=0.068. Target attainment was 53.2% versus 63.0%, p=0.336. Weight and body mass index changes did not differ significantly, and stratified analyses showed no significant subgroup effects. Conclusions: No statistically significant difference was detected between once-weekly trelagliptin and once-daily vildagliptin in glycaemic improvement over 12 weeks in patients. Short-term changes in body weight and body mass index were also not significantly different between the two treatment groups.3d:T9e6,Abstract Background General-purpose large language models (LLMs) are increasingly evaluated in diagnostic pathology, but prior studies have largely emphasized diagnostic accuracy rather than how models fail. We evaluated four LLMs for diagnostic performance, pathology-relevant errors and hallucinations, their burden, and potential clinical impact across multi-organ pathology cases. Design In this retrospective multicenter study, 153 pathology cases from two institutions spanning 20 organs were evaluated using ChatGPT-5.3 (LLM1), Gemini 3 (LLM2), Grok 4.20 (LLM3), and Claude Opus 4.6 (LLM4). Each LLM received multi-magnification histologic images with clinical context and generated a microscopic description and diagnosis. No data splitting, training, or fine-tuning was performed. Twenty-one pathologists assessed 612 outputs for diagnostic correctness, error and hallucination type and burden, clinical impact (0–4), and overall performance (1–5). Results Strict diagnostic accuracy was 48.9% overall (60.1% including partially correct diagnoses) and ranged from 36.6% to 58.2% across LLMs. Errors occurred in 82.0% of outputs and hallucinations in 76.6%; 90.2% contained at least one error or hallucination. Misinterpretation was the most frequent error (72.1%), while fabricated histologic features were the dominant hallucination type (74.5%). LLM2 had significantly lower misinterpretation rates than the other three LLMs, while LLM3 had significantly higher fabricated-feature hallucination rates than LLM1 and LLM2. Errors were more frequent in incorrect than correct diagnoses (97.5% vs 66.2%), as were hallucinations (95.1% vs 59.9%; both p < 0.001). Every strictly incorrect diagnosis contained an error and/or hallucination, while 79.9% of strictly correct diagnoses also contained at least one. In multivariable analysis, LLM3 was independently associated with higher odds of an incorrect diagnosis. Diagnostic correctness was the strongest determinant of high clinical impact; each one-point increase in hallucination burden increased the odds by 63% (adjusted OR, 1.63). Conclusion Diagnostic accuracy alone substantially underestimates the safety limitations of general-purpose LLMs in histopathology. Errors and hallucinations were common even when the final diagnosis was correct, and their burden was independently associated with clinical impact. Evaluation frameworks should therefore assess both diagnostic correctness and the reliability and potential consequences of accompanying generated content.3e:T6ab,Abstract Diagnostic errors in pathology remain an important contributor to patient morbidity and healthcare inefficiency despite increasing laboratory automation and quality management initiatives. Failure Mode and Effects Analysis (FMEA) offers a structured framework for prospective identification and prioritization of workflow risks across the total testing process. Concurrently, artificial intelligence (AI) has demonstrated substantial capability in image analysis, data validation, and workflow optimisation in both anatomical and clinical pathology. We present our findings from a survey among 43 pathologists who identified 266 errors i.e., (183, 69%) within pre-analytical phase, 59 (22%) errors within analytical phase and 22 (9%) errors within post-analytical phase. Then we present the results of the FMEA along with the most suitable AI approaches to mitigate the errors identified. In the end, we present a conceptual framework to integrate AI particularly agentic AI with FMEA-driven quality governance. Agentic AI enables dynamic monitoring, predictive error detection, and autonomous workflow orchestration across pre-analytical, analytical, and post-analytical phases. Integrating agentic AI with FMEA may enable continuous recalibration of risk models, transition pathology quality programs from reactive to predictive paradigms, and support scalable diagnostic safety ecosystems. Implementation considerations including algorithmic transparency, human–AI interaction, and regulatory governance are discussed. This integrated approach represents a potential next-generation model for improving diagnostic accuracy, operational efficiency, and patient safety across pathology services.3f:T822,Abstract Glioblastoma (GBM) is an aggressive tumor with a ~15-month median survival and high interpatient variability. Deep learning (DL) models using structural MRI have shown promise for individualized survival prediction, but most are developed and tested at a single institution, limiting generalizability. We externally validated a multimodal DL model trained on heterogeneous data from three sites using an independent GBM cohort from Moffitt. This study offers a rare and rigorous assessment of model performance on truly unseen data, highlighting its potential for real-world clinical deployment. We identified 149 patients with confirmed glioblastoma at Moffitt who had preoperative MRI scans acquired within four weeks of surgery. Each patient had four axial MRI sequences available: T1, T2, T1-postcontrast, and T2-FLAIR. The pretrained multimodal DL model was applied without additional fine-tuning. The model uses a ViT architecture and incorporates age as a clinical variable via a late fusion approach. Patients were stratified into high- and low-risk groups based on predicted survival scores. The concordance index (C-Index) and time-dependent area under the receiver operator curve (AUC) at one year were calculated (±95% CI). The model achieved a C-index of 0.680 ± 0.051 and an AUC of 0.773 ± 0.079. Kaplan–Meier analysis showed a significant difference in overall survival between model-predicted high- and low-risk groups (hazard ratio = 1.69, 95% CI: 1.22–2.36; p = 0.001). Age was significantly higher in the high-risk group (p < 0.05), consistent with its known prognostic relevance. Incorporating age via late fusion modestly improved performance, increasing the C-index to 0.686 ± 0.052. This externally validated multimodal deep learning model accurately stratified glioblastoma patients by survival risk using routine preoperative MRIs. The findings highlight the feasibility of deploying such models across institutions and support their potential to enable personalized prognosis and risk-adapted treatment planning in clinical neuro-oncology.40:T7a5,Abstract The aim of this study was to investigate the impact on the delivery, adoption, and effectiveness of Generative Artificial Intelligence integrated wearable devices and internet-of-thing (IoT) for long-term condition monitoring. We adopted preferred reporting items for systematic reviews and meta-analyses review methodology and screened a total of 226 articles. After considering the eligibility and selection criteria, we selected 13 articles published between 2020 and 2024. The selection criteria were based on the inclusion of studies that report on the adoption and effectiveness of Generative Artificial Intelligence integrated wearable devices and IoT for long-term condition monitoring. We found wearable health monitoring and personalized patient care plans leveraged Generative Artificial Intelligence (Gen AI) to predict health events by analyzing continuous data from wearables devices and IoT devices like smartwatches, glucose monitors, and various health and well-being sensors. Gen AI models provided tailored advice on physical activity, diet, and sleep, leading to improved health outcomes and user satisfaction. Comparative analysis from reviewed studies demonstrates substantial performance improvements: accuracy enhanced from 85.6% to 97.7%, precision improved from 85.1% to 96.8% and computational latency reduced significantly from 320 ms to 120 ms. Moving AI processing closer to the data source (e.g., on the wearable device itself) can reduce latency and improve real-time decision-making. This is particularly useful for critical health and safety applications. Moreover, robust integration with electronic health records and healthcare providers can enhance the usefulness of data collected by wearables, allowing for more comprehensive and coordinated care. Continued advancements in AI algorithms will improve the predictive capabilities of these systems, enabling even more pro-active and personalized interventions.41:Tc1c,Abstract Background: Familial breast cancer represents 5-10% of breast cancer cases, whereby the prevalence of germline mutations in the breast cancer susceptibility genes BRCA1 and BRCA2 (gBRCAm) varies greatly among ethnic groups. In Jordan, pathogenic gBRCAm have been observed in over 10% of high-risk breast cancer patients tested as per the NCCN (National Comprehensive Cancer Network) guidelines. Almost half of pathogenic/likely pathogenic variants detected are non-BRCA1 or BRCA2 genes. Hence, the underlying genetic factors contributing to familial breast cancer remain unknown. Methods: This study aims to identify cancer risk variants and assess the performance of existing breast cancer polygenic risk scores (PRS) in a Middle Eastern cohort. We performed whole genome sequencing analyses on germline DNA of 180 patients with familial non-BRCA breast cancer (King Hussein Cancer Center, Jordan), and 6000 healthy subjects from the Qatar Precision Health Institute. Results: Whole genome analysis identified 88,783 single nucleotide variants that are pathogenic/likely pathogenic or are predicted to have a high impact on protein sequence. Approximately 12% of variants were found in cancer-associated genes, of which 74 were classified as pathogenic/likely pathogenic and high-impact variants. Five variants (in MSH3, XPA, PALB2, TP53, and RAD51) were only observed in patients and not in healthy control subjects, suggesting that they might be associated with cancer risk susceptibility. In addition, we found 37 pathogenic/likely pathogenic and high-impact variants in non-cancer related genes, which were only observed in patient samples and could be of interest for further study as novel genetic variants associated with an increased risk of developing breast cancer. Performance analysis of 120 existing breast cancer PRSs revealed that 84% of scores could discriminate breast cancer patients from controls. The four best-performing PRS (PGS003759, PGS003738, PGS003398, and PGS000510) demonstrated AUCs ranging from 0.664 to 0.702, indicative of a good performance in our cohort. Conclusions: In-depth genomic analysis of non-BRCA familial breast cancer patients from Jordan identified several rare variants in cancer-related genes as well as in novel non-cancer-related genes. Four distinct PRSs derived from European ancestry cohorts demonstrated a good performance in our cohort, suggesting an added clinical validity in patients of Arab ancestry. Our findings highlight the need for additional studies across ancestries to identify common and population-specific genetic variants that may predispose women to familial breast cancer. Citation Format: Ehsan Ullah, Hikmat Abdel-Razeq, Sana Bentebbal, Abdullah Shaar, Nehad Alajez, Mohamad Saad, Julie V. Decock. Comprehensive Analysis of Rare Variants Associated with Genetic Predisposition to Non-BRCA Familial Breast Cancer Among Arabs [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P3-04-09.42:T945,Background: Diabetes Mellitus (DM) is a serious worldwide health concern.Pakistan is among the top 10 countries with the highest prevalence of diabetes in the worldwith estimates reaching as high as 21.6% in adults aged 30–79 years. HbA1C is the most usefuldiagnostic tool to assess the control and progression of disease and related complications.However, the predictors of good and bad control are not well established in our population.Objective: 1). To assess the level of A1C control among type 2 diabetic patients. 2). To find outfactors which can predict uncontrolled A1C. Study Design: Retrospective observational study.Setting: Among type 2 diabetes mellitus patients attending the outpatient of Diabetes Endocrineand Metabolic Centre (DEMC) of Lahore General Hospital, Lahore. Period: June 2012 to Feb2014. Material and methods: Total of 809 patients were randomly selected to assess A1C inthis population. At the same time, we assessed other factors associated with uncontrolled A1C.Chart review of the included patients was done using a data collection sheet, structured for thispurpose. Patient demographic data was gathered. Specific information including complicationsand laboratory results (HbA1C, Triglycerides (TG), Dyslipidemias) was collected. Diabetescomplication data was obtained clinically and through laboratory workup. The outcome wascalculated. Patients with A1C of <7% were considered well controlled. Other lab findings werecategorized with results being controlled according to the accepted cut off points. Results: Atotal of 809 patients were enrolled. Only 32.9% attained A1C control. Analyses showed thatsome factors were significantly associated with uncontrolled A1C. They were diabetes-relatedcomplications like presence of neuropathy, longer duration of diabetes and Dyslipidemias.When multivariate analysis was carried out, the chances of having uncontrolled A1C weresignificantly higher among patients who developed neuropathy and longer duration of diabetes,while triglycerides and other dyslipidemias were not statistically significant. Conclusion: Thelevel of HbA1C is significantly uncontrolled in our population. Uncontrolled HbA1C is morelikely to exist in patients with neuropathy and longer duration of Diabetes. High triglycerides andother dyslipidemia are also present in our population, but not statistically significant.43:T50d,Background: Keratinocytic tumors are commonest skin tumours. S100A6 is useful in differentiation of BCC, SCC and other adnexal tumours. We studied expression of S100A6 and its pattern to see its role in differentiation of various keratinocytic tumours. Methods: Immunohistochemistry was performed using streptavidin-biotin peroxidase technique with the polyclonal antibody for S100A6. The expression was based on percentage of tumour cells showing cytoplasmic positivity and the cytoplasmic staining intensity. Only cytoplasmic expression was considered as positive. Results: All the types of keratinocytic tumours showed positivity for S100A6 except BCCs. Among SCCs, higher expression of S100A6 was seen in well-differentiated SCCs as compared to poorly-differentiated ones. Only basosquamous BCCs i.e. BCCs with focal areas of squamous differentiation showed S100A6 positivity. In benign keratinocytic lesions including acanthomas and verrucas, mature squamous cells were found positive with no significant difference in pattern of staining. The precancerous lesion i.e. actinic keratosis and Bowen’s disease showed non-specific S100A6 staining. Conclusions: S100A6 is consistently expressed in mature squamoid cells of keratinocytic tumours with a sensitivity of 98%, if we exclude BCCs.44:T567,Objective: Neo-vascularization is vital for expansion and metastasis of a tumour and is influenced by a number of mediators. Mast cells are believed to release many pro-angiogenic mediators that may help in tumour expansion and invasion. This study describes the role of mast cells and angiogenesis in different histological types of Ameloblastoma. Study design: It was a prospective study in which 28 biopsies of different histological types of ameloblastoma were obtained. Microvessels were stained with CD-34 and mast cells were counted using Toluidine blue stain. Results: Mean age of patients of Ameloblastoma was 29.71±2.544 (95% CI: 24.49 34.93). The most common histological type of ameloblastoma was plexiform followed by follicular type. Mandible was more commonly involved (89.3%) than maxilla (10.7%). Mean mast cell count in all 28 cases of ameloblastoma was 6.39 ± 0.915 and it was significantly higher than normal oral mucosa (p = 0.000). Mean microvessel count was 12.75 ± 1.234 but it was not significantly higher than mean MVD of normal oral mucosa (p = 0.242). The mast cell count differed significantly among different histological types of ameloblastoma Conclusion: According to this study, the mast cell density is significantly increased in ameloblastoma but the increase in mast cells is not correlated with angiogenesis in and around the tumour tissue.16:["$","div",null,{"className":"mx-auto max-w-6xl px-4 py-6 sm:px-6 sm:py-10","children":[["$","header",null,{"className":"grid gap-6 lg:grid-cols-[1fr_22rem] lg:items-end","children":[["$","div",null,{"children":[["$","p",null,{"className":"label mb-2 text-accent","children":["Researcher profile · ","OpenAlex"]}],["$","h1",null,{"className":"text-3xl font-bold sm:text-4xl","children":"Ehsan Ullah"}],["$","ul",null,{"className":"mt-3 flex flex-col gap-1 text-[15px]","children":[["$","li","Health New Zealand",{"className":"flex items-center gap-2","children":[["$","span",null,{"className":"font-medium","children":"Health New Zealand"}],["$","p",null,{"className":"mt-1.5 max-w-2xl text-sm 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GholamHosseini","id":"openalex:A5034868132","orcid":"https://orcid.org/0000-0002-0135-2687","affiliation":"Auckland University of Technology","institutionId":"I39854758","institutionUrl":"https://ror.org/01zvqw119","affiliations":["School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand"]},{"name":"Ehsan Ullah","id":"openalex:A5006996479","orcid":"https://orcid.org/0000-0001-6470-3731","affiliation":"Health New Zealand","institutionId":"I4405259026","institutionUrl":"https://ror.org/01jvwvd85","affiliations":["Anatomical Pathology, Department of Pathology and Laboratory Medicine, Te Toka Tumai Auckland, Te Whatu Ora (Health New Zealand), Auckland 1061, New Zealand"]},{"name":"Shereen Moataz Afifi","id":"openalex:A5090474148","orcid":"https://orcid.org/0000-0003-2383-4721","affiliation":"German University in Cairo","institutionId":"I96823368","institutionUrl":"https://ror.org/03rjt0z37","affiliations":["Computer Science and Engineering Department, Faculty of Media Engineering & Technology, German University in Cairo, Cairo 11835, Egypt"],"corresponding":true}],"venue":"Applied Sciences","venueId":"S4210205812","publisher":"Multidisciplinary Digital Publishing Institute","publisherId":"P4310310987","year":2024,"publishedDate":"2024-11-25","type":"article","citationCount":49,"referenceCount":40,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.3390/app142310899","oaUrl":"https://www.mdpi.com/2076-3417/14/23/10899/pdf?version=1732517858","pdfUrl":"https://www.mdpi.com/2076-3417/14/23/10899/pdf?version=1732517858","topics":[{"id":"T11636","name":"Artificial Intelligence in Healthcare and Education","score":1,"subfieldId":"2718","subfield":"Health Informatics","fieldId":"27","field":"Medicine"},{"id":"T14512","name":"Technology and Human Factors in Education and 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Ullah","id":"openalex:A5006996479","orcid":"https://orcid.org/0000-0001-6470-3731","affiliation":"Counties Manukau District Health Board","institutionId":"I4210114241","institutionUrl":"https://ror.org/02cq7de70","affiliations":["the Department of Surgery, Health New Zealand, Counties Manukau, New Zealand (Ullah)"]},{"name":"Andrey Bychkov","id":"openalex:A5050325779","orcid":"https://orcid.org/0000-0002-4203-5696","affiliation":"Kameda Medical Center","institutionId":"I4210114023","institutionUrl":"https://ror.org/01gf00k84","affiliations":["the Department of Pathology, Kameda Medical Center, Kamogawa City, Chiba Prefecture, Japan (Bychkov)","the Department of Pathology, Nagasaki University, Nagasaki, Japan (Bychkov)"]},{"name":"Andrew H. Song","id":"openalex:A5066569995","orcid":"https://orcid.org/0000-0001-9356-9156","affiliation":"Brigham and Women's Hospital","institutionId":"I1283280774","institutionUrl":"https://ror.org/04b6nzv94","affiliations":["the Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts (Song, Mahmood)","the Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts (Song, Mahmood)"]},{"name":"Eric E. Walk","id":"openalex:A5009931350","orcid":"https://orcid.org/0009-0004-4776-4339","affiliation":"PathAI (United States)","institutionId":"I4210138410","institutionUrl":"https://ror.org/04eb71689","affiliations":["Office of the Chief Medical Officer, PathAI, Boston, Massachusetts (Walk)"]},{"name":"Peter C. Louis","id":"openalex:A5059757940","affiliation":"Rutgers, The State University of New Jersey","institutionId":"I102322142","institutionUrl":"https://ror.org/05vt9qd57","affiliations":["the Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey (Louis)"]},{"name":"Ghulam Rasool","affiliation":"University of South Florida","institutionId":"I2613432","institutionUrl":"https://ror.org/032db5x82","affiliations":["Department of Machine Learning, Neuro-Oncology, Moffitt Cancer Center and Research Institute, Tampa, Florida (Rasool)","the Department of Machine Learning, Moffitt Cancer Center and Research Institute, Tampa, Florida (Rasool)","the Department of Oncologic Sciences, Morsani College of Medicine and Department of Electrical Engineering, University of South Florida, Tampa (Rasool)","the Department of Machine Learning Moffitt Cancer Center and Research Institute, Tampa, Florida (Rasool)","the Department of Neuro-Oncology Moffitt Cancer Center and Research Institute, Tampa, Florida (Rasool)"]},{"name":"Rajendra S. Singh","affiliation":"Summit Medical Group (United States)","institutionId":"I4210100668","institutionUrl":"https://ror.org/017bzf706","affiliations":["Dermatopathology and Digital Pathology, Summit Health, Berkley Heights, New Jersey (Singh)"]},{"name":"Faisal Mahmood","id":"openalex:A5080050834","orcid":"https://orcid.org/0000-0001-7587-1562","affiliation":"Brigham and Women's Hospital","institutionId":"I1283280774","institutionUrl":"https://ror.org/04b6nzv94","affiliations":["the Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts (Song, Mahmood)","the Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts (Song, Mahmood)"]},{"name":"Marilyn M. Bui","id":"openalex:A5022728544","orcid":"https://orcid.org/0000-0003-4963-2255","affiliation":"Moffitt Cancer Center","institutionId":"I3019308854","institutionUrl":"https://ror.org/01xf75524","affiliations":["Department of Machine Learning, Pathology, Moffitt Cancer Center and Research Institute, Tampa, Florida (Bui)","the Department of Pathology, Moffitt Cancer Center and Research Institute, Tampa, Florida (Bui)"]},{"name":"Anil Vasdev Parwani","id":"openalex:A5079652585","orcid":"https://orcid.org/0000-0002-7616-1666","affiliation":"The Ohio State University","institutionId":"I52357470","institutionUrl":"https://ror.org/00rs6vg23","affiliations":["the Department of Pathology, The Ohio State University, Columbus (Parwani)"]}],"venue":"Archives of Pathology & Laboratory Medicine","venueId":"S139062638","publisher":"American Medical Association","publisherId":"P4310320259","year":2025,"publishedDate":"2025-01-21","type":"article","citationCount":47,"referenceCount":234,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.5858/arpa.2024-0215-ra","oaUrl":"https://meridian.allenpress.com/aplm/article-pdf/doi/10.5858/arpa.2024-0215-RA/3479424/10.5858_arpa.2024-0215-ra.pdf","pdfUrl":"https://meridian.allenpress.com/aplm/article-pdf/doi/10.5858/arpa.2024-0215-RA/3479424/10.5858_arpa.2024-0215-ra.pdf","topics":[{"id":"T11636","name":"Artificial Intelligence in Healthcare and Education","score":0.9955999851226807,"subfieldId":"2718","subfield":"Health Informatics","fieldId":"27","field":"Medicine"},{"id":"T10862","name":"AI in cancer detection","score":0.0044999998062849045,"subfieldId":"1702","subfield":"Artificial Intelligence","fieldId":"17","field":"Computer Science"},{"id":"T13904","name":"Artificial Intelligence Applications","score":0,"subfieldId":"1702","subfield":"Artificial Intelligence","fieldId":"17","field":"Computer Science"}],"sources":["openalex"],"id":"openalex:W4406666355"},{"ids":{"openalex":"W2092261268","doi":"10.4103/0973-1482.119311","pmid":"24125971"},"title":"The role of mast cells and angiogenesis in well-differentiated oral squamous cell carcinoma","abstract":"OBJECTIVE: Neo-vascularization is vital for an expansion and metastasis of a tumor and is influenced by a number of mediators. Mast cells are believed to release many pro-angiogenic mediators that may help in tumor expansion and invasion. This study describes the role of mast cells and angiogenesis in oral squamous cell carcinoma (OSCC). STUDY DESIGN: It was a prospective study in which 37 biopsies of well-differentiated OSCC were obtained. Micro-vessels were stained with cluster of differentiation (CD)-34 and mast cells were counted using the Toluidine blue stain. RESULTS: When compared to normal oral mucosal tissue, it was seen that micro-vessel density and mast cell density indeed increases significantly in squamous cell carcinoma; however, they are not correlated to each other. CONCLUSION: This study reports that angiogenesis does increase in OSCC and mast cells also invade the peri-tumor tissue, but they are not directly correlated.","authors":[{"name":"Ehsan Ullah","id":"openalex:A5006996479","orcid":"https://orcid.org/0000-0001-6470-3731","affiliation":"Quaid-e-Azam Medical College","institutionId":"I4405273397","institutionUrl":"https://ror.org/05rxs6b95","affiliations":["Department of Pathology, Quaid-e-Azam Medical College Bahawalpur, Pakistan"]},{"name":"Ali Tahir","id":"openalex:A5107446941","orcid":"https://orcid.org/0000-0001-9714-0939","affiliation":"Quaid-e-Azam Medical College","institutionId":"I4405273397","institutionUrl":"https://ror.org/05rxs6b95","affiliations":["Department of Pathology, Quaid-e-Azam Medical College Bahawalpur, Pakistan"]},{"name":"A. H. Nagi","id":"openalex:A5108083695","affiliation":"University of Health Sciences Lahore","institutionId":"I2803096283","institutionUrl":"https://ror.org/00gt6pp04","affiliations":["Department of Morbid Anatomy and Histopathology, University of Health Sciences, Lahore, Pakistan"]},{"name":"Omer Sefvan Janjua","id":"openalex:A5033722355","orcid":"https://orcid.org/0000-0002-4279-9186","affiliation":"Allied Hospital","institutionId":"I2801504200","institutionUrl":"https://ror.org/02rsn0x61","affiliations":["Department of Oral and Maxillofacial Surgery, Punjab Dental College/Allied Hospital Faisalabad, Pakistan"]}],"venue":"Journal of Cancer Research and Therapeutics","venueId":"S53738828","publisher":"BioMed Central","publisherId":"P4310320256","year":2013,"publishedDate":"2013-01-01","type":"article","citationCount":39,"referenceCount":26,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.4103/0973-1482.119311","oaUrl":"https://doi.org/10.4103/0973-1482.119311","topics":[{"id":"T10422","name":"Angiogenesis and VEGF in Cancer","score":0.9789999723434448,"subfieldId":"1312","subfield":"Molecular Biology","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T11346","name":"Mast cells and histamine","score":0.00800000037997961,"subfieldId":"2403","subfield":"Immunology","fieldId":"24","field":"Immunology and Microbiology"},{"id":"T10307","name":"Head and Neck Cancer Studies","score":0.005400000140070915,"subfieldId":"2733","subfield":"Otorhinolaryngology","fieldId":"27","field":"Medicine"}],"sources":["openalex"],"id":"openalex:W2092261268"},{"ids":{"openalex":"W2190806732","doi":"10.12669/pjms.315.7196","pmid":"26649021"},"title":"Hypovitaminosis D and its association with lifestyle factors","abstract":"$23","authors":[{"name":"Mudassar Ali Roomi","id":"openalex:A5051733784","orcid":"https://orcid.org/0000-0001-6796-4179","affiliation":"Fiji National University","institutionId":"I75418795","institutionUrl":"https://ror.org/00qk2nf71","affiliations":["Mudassar Ali Roomi, MBBS, M.Phil. Assistant Professor Physiology at Fiji National University, Fiji"]},{"name":"Ansa Farooq","id":"openalex:A5054267361","affiliation":"Fiji National University","institutionId":"I75418795","institutionUrl":"https://ror.org/00qk2nf71","affiliations":["Ansa Farooq, M.Sc., M.S. Lecturer Biochemistry at Fiji National University, Fiji"]},{"name":"Ehsan Ullah","id":"openalex:A5006996479","orcid":"https://orcid.org/0000-0001-6470-3731","affiliation":"Auckland University of Technology","institutionId":"I39854758","institutionUrl":"https://ror.org/01zvqw119","affiliations":["Ehsan Ullah, MBBS, M.Phil. Ph.D. Candidate, Faculty of Health, AUT University, Auckland, New Zealand"]},{"name":"Khalid P. Lone","id":"openalex:A5053766900","orcid":"https://orcid.org/0000-0002-9451-2263","affiliation":"Khalid Parvez Lone, Professor and Head Department of Physiology and Cell Biology, UHS, Lahore, Pakistan","affiliations":["Khalid Parvez Lone, Professor and Head Department of Physiology and Cell Biology, UHS, Lahore, Pakistan"]}],"venue":"Pakistan Journal of Medical Sciences","venueId":"S83180243","publisher":"Professional Medical Publications","publisherId":"P4310319271","year":2015,"publishedDate":"2015-09-16","type":"article","citationCount":36,"referenceCount":28,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.12669/pjms.315.7196","oaUrl":"https://pjms.com.pk/index.php/pjms/article/view/7196/3677","pdfUrl":"https://pjms.com.pk/index.php/pjms/article/view/7196/3677","topics":[{"id":"T10277","name":"Vitamin D Research Studies","score":1,"subfieldId":"2734","subfield":"Pathology and Forensic Medicine","fieldId":"27","field":"Medicine"},{"id":"T13112","name":"Nutrition and Health Studies","score":0,"subfieldId":"2739","subfield":"Public Health, Environmental and Occupational Health","fieldId":"27","field":"Medicine"},{"id":"T11013","name":"Skin Protection and Aging","score":0,"subfieldId":"2708","subfield":"Dermatology","fieldId":"27","field":"Medicine"}],"sources":["openalex"],"id":"openalex:W2190806732"},{"ids":{"openalex":"W2766819686","doi":"10.1109/escience.2017.84"},"title":"A Review of Privacy and Consent Management in Healthcare: A Focus on Emerging Data Sources","abstract":"$24","authors":[{"name":"Muhammad Rizwan Asghar","id":"openalex:A5074245801","orcid":"https://orcid.org/0000-0002-9607-376X","affiliation":"University of Auckland","institutionId":"I154130895","institutionUrl":"https://ror.org/03b94tp07","affiliations":["The University of Auckland, Auckland, Auckland, NZ","University of Auckland, Auckland, New Zealand"]},{"name":"TzeHowe Lee","id":"openalex:A5029389817","affiliation":"Orion Health, Auckland, Auckland, NZ","affiliations":["Orion Health, Auckland, Auckland, NZ","Research Team Orion Health, New Zealand"]},{"name":"Mirza Mansoor Baig","id":"openalex:A5032343123","orcid":"https://orcid.org/0000-0002-7431-6193","affiliation":"Auckland University of Technology","institutionId":"I39854758","institutionUrl":"https://ror.org/01zvqw119","affiliations":["Res. Team, Orion Health, New Zealand","Auckland University of Technology, Auckland, New Zealand"]},{"name":"Ehsan Ullah","id":"openalex:A5006996479","orcid":"https://orcid.org/0000-0001-6470-3731","affiliation":"University of Auckland","institutionId":"I154130895","institutionUrl":"https://ror.org/03b94tp07","affiliations":["Cyber Security Foundry, The University of Auckland, New Zealand","Auckland City Hospital, Auckland, New Zealand"]},{"name":"Giovanni Russello","id":"openalex:A5072751099","orcid":"https://orcid.org/0000-0001-6987-0803","affiliation":"University of Auckland","institutionId":"I154130895","institutionUrl":"https://ror.org/03b94tp07","affiliations":["Clinical Quality and Safety Service, Auckland District Health Board, New Zealand","University of Auckland, Auckland, New Zealand"]},{"name":"Gillian Dobbie","id":"openalex:A5016115576","orcid":"https://orcid.org/0000-0001-7245-0367","affiliation":"University of Auckland","institutionId":"I154130895","institutionUrl":"https://ror.org/03b94tp07","affiliations":["Cyber Security Foundry, The University of Auckland, New Zealand","University of Auckland, Auckland, New Zealand"]}],"venue":"IEEE International Conference on eScience","venueId":"S4306418677","publisher":null,"year":2017,"publishedDate":"2017-10-01","type":"conference-paper","citationCount":35,"referenceCount":20,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.1109/escience.2017.84","oaUrl":"https://arxiv.org/pdf/1711.00546","pdfUrl":"https://arxiv.org/pdf/1711.00546","topics":[{"id":"T14029","name":"Thoreau and American Literature","score":0.7196999788284302,"subfieldId":"1710","subfield":"Information Systems","fieldId":"17","field":"Computer Science"},{"id":"T10350","name":"Electronic Health Records Systems","score":0.13420000672340393,"subfieldId":"3605","subfield":"Health Information Management","fieldId":"36","field":"Health Professions"},{"id":"T13275","name":"Medical and 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Pakistan"]},{"name":"Muhammad Arshad Iqbal","id":"openalex:A5025520270","affiliation":"Quaid-e-Azam Medical College","institutionId":"I4405273397","institutionUrl":"https://ror.org/05rxs6b95","affiliations":["Muhammad Arshad Iqbal, Department of Pathology, Quaid-e-Azam Medical College (QAMC), Bahawalpur, Pakistan"]},{"name":"Ameer Ahmed","id":"openalex:A5104227466","affiliation":"Quaid-e-Azam Medical College","institutionId":"I4405273397","institutionUrl":"https://ror.org/05rxs6b95","affiliations":["Ameer Ahmad, Department of Paediatrics, Quaid-e-Azam Medical College (QAMC), Bahawalpur, Pakistan"]},{"name":"Ehsan Ullah","id":"openalex:A5006996479","orcid":"https://orcid.org/0000-0001-6470-3731","affiliation":"Quaid-e-Azam Medical College","institutionId":"I4405273397","institutionUrl":"https://ror.org/05rxs6b95","affiliations":["Ehsan Ullah, Department of Pathology, Quaid-e-Azam Medical College (QAMC), Bahawalpur, Pakistan"]}],"venue":"Pakistan Journal of Medical Sciences","venueId":"S83180243","publisher":"Professional Medical Publications","publisherId":"P4310319271","year":2013,"publishedDate":"2013-03-31","type":"article","citationCount":32,"referenceCount":20,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.12669/pjms.292.3066","oaUrl":"https://doi.org/10.12669/pjms.292.3066","topics":[{"id":"T10170","name":"Retinal Diseases and Treatments","score":0.9970999956130981,"subfieldId":"2731","subfield":"Ophthalmology","fieldId":"27","field":"Medicine"},{"id":"T10560","name":"Diabetes Management and Research","score":0.0006000000284984708,"subfieldId":"2712","subfield":"Endocrinology, Diabetes and Metabolism","fieldId":"27","field":"Medicine"},{"id":"T10408","name":"Chronic Kidney Disease and 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Baig","id":"openalex:A5032343123","orcid":"https://orcid.org/0000-0002-7431-6193","affiliation":"Auckland University of Technology","institutionId":"I39854758","institutionUrl":"https://ror.org/01zvqw119","affiliations":["School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand"]},{"name":"Hamid GholamHosseini","id":"openalex:A5034868132","orcid":"https://orcid.org/0000-0002-0135-2687","affiliation":"Auckland University of Technology","institutionId":"I39854758","institutionUrl":"https://ror.org/01zvqw119","affiliations":["School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand"]},{"name":"Jun Lu","id":"openalex:A5039239695","orcid":"https://orcid.org/0000-0003-0225-6643","affiliation":"Nanchang University","institutionId":"I141649914","institutionUrl":"https://ror.org/042v6xz23","affiliations":["College of Food Engineering and Nutrition Sciences, Shaanxi Normal University, Xi'an 710119, Shaanxi Province, China","College of Food Science and Technology, Nanchang University, Nanchang 330031, Jiangxi Province, China","Maurice Wilkins Centre for Molecular Discovery, Auckland 1010, New Zealand","School of Public Health and Interdisciplinary Studies, Faculty of Health and Environmental Sciences, Auckland University of Technology, New Zealand","School of Science, Faculty of Health and Environmental Sciences, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand"],"corresponding":true}],"venue":"Heliyon","venueId":"S2898612692","publisher":"Elsevier BV","publisherId":"P4310320990","year":2022,"publishedDate":"2022-02-01","type":"article","citationCount":30,"referenceCount":24,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.1016/j.heliyon.2022.e08944","oaUrl":"http://www.cell.com/article/S2405844022002328/pdf","pdfUrl":"http://www.cell.com/article/S2405844022002328/pdf","topics":[{"id":"T10508","name":"Patient Safety and Medication Errors","score":0.7240999937057495,"subfieldId":"3604","subfield":"Emergency Medical Services","fieldId":"36","field":"Health Professions"},{"id":"T13248","name":"Healthcare Technology and Patient Monitoring","score":0.11980000138282776,"subfieldId":"2746","subfield":"Surgery","fieldId":"27","field":"Medicine"},{"id":"T12871","name":"Healthcare Quality and Management","score":0.05480000004172325,"subfieldId":"3605","subfield":"Health Information Management","fieldId":"36","field":"Health 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Agha","id":"openalex:A5079545326","orcid":"https://orcid.org/0000-0002-2704-8931","affiliation":"Blackpool Victoria Hospital","institutionId":"I2801018919","institutionUrl":"https://ror.org/01r9ea713","affiliations":["Adnan Agha, MRCPUK, FCPS","MCPS Department of General Medicine, Blackpool Victoria Hospital NHS, United Kingdom"]},{"name":"Noha Saleh","id":"openalex:A5109842984","affiliation":"Alexandria University","institutionId":"I84524832","institutionUrl":"https://ror.org/00mzz1w90","affiliations":["Alexandria University, Egypt"]},{"name":"Ehsan Ullah","id":"openalex:A5006996479","orcid":"https://orcid.org/0000-0001-6470-3731","affiliation":"Lahore General Hospital","institutionId":"I2799885502","institutionUrl":"https://ror.org/00s3e5069","affiliations":["Lahore General Hospital"]},{"name":"Imran Ali Syed","id":"openalex:A5089514528","orcid":"https://orcid.org/0009-0006-9024-4237","affiliation":"Lahore General 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Occupational Health","fieldId":"27","field":"Medicine"},{"id":"T12047","name":"Viral Infections and Vectors","score":0.00019999999494757503,"subfieldId":"2725","subfield":"Infectious Diseases","fieldId":"27","field":"Medicine"},{"id":"T14053","name":"Dengue and Mosquito Control Research","score":0,"subfieldId":"3312","subfield":"Sociology and Political Science","fieldId":"33","field":"Social Sciences"}],"sources":["openalex"],"id":"openalex:W2169229878"},{"ids":{"openalex":"W3120624594","doi":"10.1055/s-0040-1719043","pmid":"33406540"},"title":"Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications","abstract":"$28","authors":[{"name":"Mirza Mansoor Baig","id":"openalex:A5032343123","orcid":"https://orcid.org/0000-0002-7431-6193","affiliation":"Auckland University of Technology","institutionId":"I39854758","institutionUrl":"https://ror.org/01zvqw119","affiliations":["School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand"],"corresponding":true},{"name":"Hamid GholamHosseini","id":"openalex:A5034868132","orcid":"https://orcid.org/0000-0002-0135-2687","affiliation":"Auckland University of Technology","institutionId":"I39854758","institutionUrl":"https://ror.org/01zvqw119","affiliations":["School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand"]},{"name":"Jairo Gutiérrez","id":"openalex:A5023063391","orcid":"https://orcid.org/0000-0002-2103-8636","affiliation":"Auckland University of Technology","institutionId":"I39854758","institutionUrl":"https://ror.org/01zvqw119","affiliations":["School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand"]},{"name":"Ehsan Ullah","id":"openalex:A5006996479","orcid":"https://orcid.org/0000-0001-6470-3731","affiliation":"Auckland University of Technology","institutionId":"I39854758","institutionUrl":"https://ror.org/01zvqw119","affiliations":["School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand"]},{"name":"María Lindén","id":"openalex:A5027388167","orcid":"https://orcid.org/0000-0003-1940-1747","affiliation":"Mälardalen University","institutionId":"I82509713","institutionUrl":"https://ror.org/033vfbz75","affiliations":["School of Innovation Design and Engineering, Mälardalen University, Västerås, Sweden"]}],"venue":"Applied Clinical Informatics","venueId":"S187139995","publisher":"Thieme Medical Publishers (Germany)","publisherId":"P4310320000","year":2021,"publishedDate":"2021-01-01","type":"article","citationCount":22,"referenceCount":38,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.1055/s-0040-1719043","oaUrl":"http://www.thieme-connect.de/products/ejournals/pdf/10.1055/s-0040-1719043.pdf","pdfUrl":"http://www.thieme-connect.de/products/ejournals/pdf/10.1055/s-0040-1719043.pdf","topics":[{"id":"T11396","name":"Artificial Intelligence in Healthcare","score":0.7192000150680542,"subfieldId":"3605","subfield":"Health Information Management","fieldId":"36","field":"Health Professions"},{"id":"T10444","name":"Context-Aware Activity Recognition Systems","score":0.09049999713897705,"subfieldId":"1707","subfield":"Computer Vision and Pattern Recognition","fieldId":"17","field":"Computer Science"},{"id":"T11196","name":"Non-Invasive Vital Sign Monitoring","score":0.04450000077486038,"subfieldId":"2204","subfield":"Biomedical 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