THE ROLE OF ARTIFICIAL INTELLIGENCE IN PERSONALIZED MEDICINE AND PREDICTIVE DIAGNOSTICS – A NARRATIVE REVIEW
Shahid Abbasa, Abdul Sattarb, Syeda Hina Shahc, Sidrah Hafeezd, Waqas Mahmoode, Raza Iqbalf, Keziah Shaheend, Pervaiz Azamg, Tazeem Shahbazh
- aThe University of Agriculture Peshawar, Pakistan
- bLahore Garrison University, Lahore, Pakistan
- cNational University of Medical Sciences, Rawalpindi, Pakistan
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Abstract
Background: Artificial intelligence (AI) has revolutionized personalized medicine and predictive diagnostics by enabling data-driven, individualized healthcare strategies. AI-powered models leverage vast datasets, including genomic, proteomic, and clinical information, to improve disease detection, optimize treatment selection, and enhance patient outcomes. With the increasing burden of chronic diseases and the growing demand for precision medicine, AI presents significant opportunities to transform traditional healthcare paradigms. However, challenges related to clinical implementation, algorithmic bias, and regulatory considerations necessitate a critical evaluation of its applications. Objective: This narrative review aims to explore the role of AI in personalized medicine and predictive diagnostics, analyzing its clinical applications, benefits, limitations, and future directions. The review synthesizes current evidence on AI-driven advancements in disease diagnosis, risk stratification, and treatment optimization while addressing key challenges hindering its widespread adoption. Main Discussion Points: AI has demonstrated superior diagnostic accuracy in various medical domains, including oncology, cardiology, and neurology, through deep learning and machine learning algorithms. Predictive models enhance risk assessment, enabling early intervention and personalized therapeutic approaches. Despite these advancements, methodological limitations, variability in outcome measurement, and concerns regarding data standardization and interpretability pose significant barriers. Ethical considerations, regulatory frameworks, and the need for unbiased, transparent AI models remain critical challenges in integrating AI into routine clinical practice. Conclusion: AI holds immense potential in advancing personalized medicine and predictive diagnostics, yet its real-world application requires rigorous validation, standardized protocols, and ethical oversight. Future research should focus on developing explainable AI models, conducting large-scale randomized controlled trials, and ensuring equitable healthcare access to maximize AI’s impact on patient care.
TL;DRThis narrative review synthesizes current evidence on AI-driven advancements in disease diagnosis, risk stratification, and treatment optimization while addressing key challenges hindering its widespread adoption. (Semantic Scholar)