Artificial Intelligence (AI) has rapidly emerged as a transformative force in modern medicine, offering significant advancements across diagnostics, treatment planning, patient care, and public health management. Recent innovations in machine learning, natural language processing, robotics, and data-driven decision support have enhanced accuracy, efficiency, and personalization in clinical practice. AI applications span numerous specialties, including radiology, pathology, emergency medicine, aesthetic medicine, and public health, with demonstrated benefits such as improved imaging interpretation, automated histopathological analysis, optimized triage, predictive modeling for disease prevention, and patient-centered treatment planning. Despite its potential, integrating AI into healthcare presents technical, organizational, and ethical challenges, including data quality limitations, algorithmic bias, transparency and interpretability issues, cybersecurity vulnerabilities, and regulatory complexities. Ethical concerns involve patient privacy, fairness, and the distribution of responsibility for AI-guided clinical decisions. Looking forward, the responsible deployment of AI will require continuous model monitoring, integration with clinical workflows, equitable training datasets, and collaborative oversight to ensure that AI complements healthcare professionals, promotes safety, and maximizes benefits for both individual patients and populations.
Krok et al. (2026) studied this question.