医療人工知能はどのように医師の生産性を変革するか？

This study examines the impact of medical artificial intelligence (AI) on physician workload and the quality of patient care. A meta-analysis of empirical studies found that AI significantly reduces physician workload and diagnostic time by automating repetitive interpretation and documentation processes, freeing clinicians to focus solely on patients. Automated generative AI-based electronic medical record systems reduce documentation time by approximately 40%, while voice recognition and AI scribing technologies reduce patient charting time by 28.8%. This reduces administrative burden, a major cause of physician burnout, by more than 30%. In radiology, AI-based interpretation reduced the interpretation time for abnormal contrast-enhanced brain CT lesions by 11.23%, the interpretation time for lung lesions by 52.82%, and the analysis time for peripheral blood smears by 61%. Importantly, these time savings occur naturally and, in some cases, improve diagnostic accuracy for major diseases (e.g., lung nodules, brain lesions, and breast cancer). Furthermore, AI minimizes the workload of interpretation through its automatic filtering function. This includes a 77.4%-86.7% reduction in review time for pulmonary nodules, a 51.3%-72.9% reduction in endometrial slide screening time, and an 86% saving in manual review time for epilepsy electroencephalography evaluation. These findings confirm that AI is establishing itself as a reliable tool that simultaneously improves physician efficiency, diagnostic efficiency, and clinical accuracy. Therefore, future healthcare policies regarding AI should not simply focus on expanding the workforce, but should adopt a strategic approach, optimizing resource efficiency and building a more resilient healthcare system.