improving diagnostic accuracy, and optimizing healthcare delivery systems. This perspective explores the expanding role of AI across clinical decision support systems, predictive analytics, and personalized medicine, highlighting its potential to support clinicians in managing complex and data-intensive healthcare environments. Evidence from recent studies suggests that AI algorithms can achieve high performance in specific diagnostic tasks, particularly in radiology, dermatology, and pathology; however, their real-world clinical effectiveness remains dependent on robust validation, workflow integration, and generalizability across diverse populations. Despite these promising developments, the integration of AI into clinical practice raises important ethical, practical, and regulatory challenges. Key concerns include limited explainability of complex models, data privacy and security risks, and the potential for algorithmic bias that may exacerbate existing healthcare disparities. In addition, gaps between retrospective model performance and prospective clinical impact continue to limit the translation of AI tools into routine care. These challenges underscore the need for a more holistic evaluation framework that extends beyond technical performance metrics to include clinical usability, transparency, and ethical robustness. A critical appraisal of current AI applications suggests that successful implementation in healthcare depends not only on algorithmic accuracy but also on trust, interpretability, and seamless integration into clinical workflows. Furthermore, clinicians must remain central to decision-making processes, ensuring that AI functions as an assistive technology rather than a replacement for human judgment. Overall, while AI holds substantial promise for improving patient outcomes and healthcare efficiency, its safe and effective adoption requires careful attention to ethical principles, regulatory oversight, and real-world clinical validation.