Modern Applications of Artificial Intelligence in Medical Physics: A Theoretical Review

Abstract

The rapid integration of artificial intelligence (AI) into medical physics is reshaping major applications in medical imaging, radiation therapy, treatment planning, quality assurance, and precision medicine. This study aimed to critically review and analyze contemporary AI applications in medical physics, evaluate their technical and clinical contributions, identify the major barriers limiting their transition from experimental performance to safe clinical implementation, and highlight emerging directions for future research. A descriptive, comparative, analytical, and critical review approach was adopted to synthesize recent scientific literature and critically compare previous studies in terms of technological maturity, generalizability, data quality, external validation, interpretability, quality assurance, and patient-safety implications. The findings indicate that the most mature AI applications are concentrated in medical image analysis and reconstruction, automatic segmentation, tumor and organ-at-risk delineation, radiotherapy treatment planning, dose prediction, and quality assurance. AI has demonstrated considerable potential to reduce workflow time, improve consistency and accuracy, and support adaptive radiotherapy and personalized medicine. However, the critical synthesis revealed a substantial gap between high algorithmic performance and actual clinical readiness. Many models continue to rely on retrospective or single-institution datasets, with limited external validation and considerable variability across devices, patient populations, imaging protocols, and clinical environments. Additional concerns include algorithmic bias, uncertainty, limited interpretability, data privacy, cybersecurity, and regulatory accountability. The study concludes that computational accuracy alone is insufficient to establish the clinical value of AI in medical physics and proposes a conceptual transition from an AI accuracy-centered paradigm toward clinically trustworthy AI. Within this framework, clinical readiness results from the combined effects of algorithmic performance, representative data quality, external validation, interpretability, uncertainty quantification, professional oversight, workflow integration, regulatory compliance, and patient safety. The study further emphasizes that AI should augment rather than replace medical physicists, supporting a human–AI collaboration model in which professional expertise, verification, and clinical accountability remain central to safe implementation