THE EXPANDING ROLE OF ARTIFICIAL INTELLIGENCE IN MEDICAL DIAGNOSIS AND TREATMENT: IMPACTS ON CLINICAL QUALITY, PATIENT OUTCOMES, AND THE FUTURE OF PHYSICIAN EMPLOYMENT – A REVIEW - Mikołaj Grodzki; Maciej Zachara; Mateusz Bartoszek; Patryk Harnicki; Erwin Grzegorzak; Rafał Pelczar; Jakub Minas; Paulina Dybiak; Adrian Morawiec; Paweł Słoma; Oliwia Krawczyk (International Journal of Innovative Technologies in Social Science, 2026)
Artificial intelligence (AI) is rapidly transforming medical diagnosis and treatment, presenting significant opportunities to enhance diagnostic accuracy, streamline clinical workflows, and enable more personalized, data-driven patient care. This narrative review synthesizes contemporary evidence on the expanding applications of AI in diagnostic interpretation, therapeutic decision-making, and clinical management, while also examining its broader implications for healthcare quality, patient outcomes, and the future of physician employment. A structured review methodology was applied, incorporating 54 peer-reviewed studies published between 2015 and 2025 across key medical domains, including radiology, oncology, chronic disease management, predictive analytics, and healthcare workforce transformation. The reviewed literature demonstrates that AI-based systems can equal or exceed clinician performance in selected, well-defined diagnostic tasks, contributing to earlier disease detection, improved risk stratification, and more optimized treatment planning. At the same time, the translation of these technologies into routine clinical practice remains constrained by persistent challenges, including algorithmic bias, limited transparency and explainability, regulatory uncertainty, data privacy concerns, and variability in real-world performance. Beyond clinical outcomes, the growing automation of cognitive tasks traditionally performed by physicians raises important questions regarding professional deskilling, evolving role boundaries, and the reconfiguration of medical labor. While AI is unlikely to fully replace clinicians, its continued integration is expected to substantially reshape clinical responsibilities, interdisciplinary collaboration, and required skill sets. Overall, this review underscores the dual impact of AI as both a driver of improved clinical quality and a catalyst for structural change within the medical profession, and identifies key research and policy priorities necessary to ensure its safe, ethical, and equitable implementation.