Artificial intelligence (AI) has emerged as a major driver of transformation in clinical decision-making and healthcare delivery systems. Machine learning, deep learning, natural language processing, and computer vision are increasingly being integrated into clinical workflows to support diagnosis, risk prediction, treatment planning, and operational efficiency. This narrative review synthesizes recent literature on the role of AI in clinical decision-making across key domains, including medical imaging, electronic health record analysis, precision medicine, clinical risk stratification, surgical support, and drug discovery. It also examines major barriers to safe and effective implementation, particularly algorithmic bias, limited external validation, data privacy concerns, poor interpretability, workflow disruption, and regulatory uncertainty. Ethical and medicolegal issues, including transparency, accountability, equity, and the effect of AI on clinician-patient relationships, are also discussed. Current evidence suggests that AI performs well in selected narrow tasks, especially in image-based and prediction-focused applications, but its reliability and clinical value remain inconsistent in complex real-world settings. Future progress is likely to depend on stronger prospective validation, explainable and multimodal systems, privacy-preserving learning approaches, and better integration of AI into clinical practice. The responsible use of AI in healthcare will require a multidisciplinary, patient-centered approach that balances innovation with safety, ethics, and clinical usefulness.
Aditya Swaprakash Gadepalli Sri Pratyak (2026) studied this question.