高齢者の転倒・脆弱性に対するケアパス改善のためのデータと人工知能の活用：臨床医のための機会、課題、および実践的考察

Older people living with falls and frailty are common in emergency attendances, admissions and functional decline. Artificial intelligence (AI) and machine learning (ML) are increasingly incorporated in risk prediction, service streamlining and re-engineering, yet their roles in healthcare practice remain unclear. This CME article provides a practical overview for clinicians of acute care and internal medicine with a special interest in older people's care. We summarize emerging applications of AI and AI-assisted tools across the falls and frailty care pathway, from community support through the emergency department, ortho-geriatrics and post-acute rehabilitation. We highlight potential benefits: enhanced risk stratification, facilitation of comprehensive geriatric assessment (CGA), rehabilitation, and delivery of care transition programs. We then discuss challenges and ethical concerns, for instance, "digital ageism", automation bias and weak evidence for impact. Finally, we outline pragmatic questions and steps clinicians can adopt when using AI-enabled tools in clinical settings.