A COMPREHENSIVE ANALYSIS OF THE ROLE OF MEDICAL SYSTEMS DEVELOPED BASED ON ARTIFICIAL INTELLIGENCE IN THE CLINICAL DECISION-MAKING PROCESS, DIAGNOSTIC ACCURACY, AND SAFETY ISSUES
Keywords:
artificial intelligence, clinical decision-making, diagnostic accuracy, medical systems, machine learning, deep learning, clinical decision support, healthcare safety, algorithmic bias, explainable AI.Abstract
Artificial intelligence (AI)–based medical systems are increasingly transforming modern healthcare by enhancing clinical decision-making, improving diagnostic accuracy, and addressing safety challenges. This study provides a comprehensive analysis of the role of AI-driven systems in supporting clinicians through data-driven insights, predictive analytics, and automated diagnostic assistance. The research reviews recent literature on machine learning and deep learning applications in various medical domains, highlighting their effectiveness in image analysis, risk prediction, and clinical workflow optimization. At the same time, the study examines key limitations, including model interpretability, algorithmic bias, automation bias, and safety concerns associated with over-reliance on AI recommendations. Regulatory, ethical, and implementation challenges are also discussed to provide a balanced perspective on the integration of AI into clinical practice. The findings suggest that while AI significantly enhances healthcare performance, its safe and effective use requires human oversight, transparency, and continuous validation in real-world clinical environments.
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