EMERGING AI APPLICATIONS IN THE DIAGNOSIS AND MANAGEMENT OF INTERNAL MEDICINE DISORDERS-A NARRATIVE REVIEW

Abstract

Background: Artificial intelligence (AI) is rapidly reshaping the landscape of healthcare, offering innovative tools for disease detection, prognosis, and personalized management. Internal medicine disorders such as diabetes, hypertension, and heart failure represent a significant global burden and require multifaceted, data-driven strategies for effective management. AI has emerged as a valuable adjunct in this context, providing clinicians with advanced analytic capabilities to support decision-making and improve patient outcomes.

Objective: This narrative review aims to explore the current and emerging applications of AI in the diagnosis, risk prediction, and individualized treatment of key internal medicine disorders, while identifying gaps in the literature and suggesting directions for future research.

Main Discussion Points: The review discusses how AI-based models enhance diagnostic accuracy using imaging and clinical data, improve risk stratification through predictive analytics, and support treatment personalization via real-time data integration. It also critically examines the limitations of existing literature, including small sample sizes, retrospective study designs, and limited generalizability across diverse populations. Ethical challenges, data bias, and the lack of standardization are also addressed.

Conclusion: AI holds significant promise in transforming internal medicine by augmenting clinical decision-making and personalizing care. However, current evidence remains preliminary, with substantial gaps requiring further investigation. Future research should focus on robust, multicenter trials and equitable model development to ensure safe, effective, and inclusive AI integration in clinical practice.