ARTIFICIAL INTELLIGENCE IN CLINICAL DECISION-MAKING: OPPORTUNITIES AND CHALLENGES

Arpita Hazra*, Pulagam Vaishnavi Reddy, Boga Neha, Savitha, Zeenath Begum, Shamna Machanchery and Noorush Shifa Nizami

ABSTRACT

The integration of artificial intelligence (AI) into healthcare is transforming clinical decision-making by leveraging vast datasets, including genomic, biomarker, and phenotype information, to enhance care quality and safety. However, the rapid advancement of AI technologies poses challenges for evaluating their impact on care delivery, patient outcomes, and ethical considerations. This paper explores key aspects of AI in clinical decision support, focusing on evaluation frameworks, challenges, and practical implications. Historically, AI systems have evolved from rule-based expert systems to modern machine learning models, bringing new complexities to their assessment. Challenges include ensuring algorithm generalizability, mitigating biases, and maintaining ethical standards in diverse sociotechnical settings. The need for continuous evaluation throughout the AI lifecycle—from design and development to implementation and surveillance—is emphasized, with the Learning Healthcare System paradigm providing a foundation for ongoing improvement. Practical aspects of evaluation, including the use of established guidelines like GEP-HI and STARE-HI, are examined to ensure transparent and robust assessments. Indicators such as algorithmic accuracy, user interaction, and clinical outcomes are highlighted as essential measures for monitoring AI performance. The paper concludes by addressing the need for adaptive frameworks that account for dynamic algorithms and evolving medical knowledge, ensuring AI's responsible integration into healthcare.

Keywords: Artificial intelligence, clinical decision support, healthcare technology, AI evaluation, algorithm performance, machine learning, healthcare informatics, AI ethics, dynamic algorithms, Learning Healthcare System.