15Medical AI in Japan—Green Shoots and Persistent Challengesfrom medical images and clinical data used in hospitals and then support physicians in their diagnostic and administrative tasks. For example, it can instantly highlight suspicious cancerous regions on chest CT scans or endoscopic videos, helping prevent oversights and shortening the time to diagnosis. Systems that aggregate blood ‐ test values and lifestyle information to predict which patients may deteriorate within a few days are already moving from research to practical use. In examination rooms, tools have emerged that record conversations with patients as audio, automatically generate chart notes, reduce physicians’ overtime, and maintain the quality of documentation.Three main factors explain why these technologies attract attention. First, population aging is increasing the number of patients, and doctor shortages—especially in rural areas—have become severe. Second, the volume of medical data is exploding, making it impossible for human eyes and hands alone to keep up. Third, controlling the rise of healthcare costs demands greater efficiency and earlier disease detection. AI is not meant to replace physicians; rather, it is positioned as an “augmentation tool” that reduces overwork and human error while enhancing the quality of care.Of course, AI’s performance depends on access to large quantities of high ‐ quality data. Furthermore, for AI to be integrated into clinical practice, reimbursement systems and personal ‐ data protection rules must be in place. Without a well ‐ developed environment, even advanced technology may remain unused on the front lines of healthcare.National Cancer Center, software that automatically detects cerebral aneurysms has been included in the national health ‐insurance scheme, allowing patients to benefit at no extra charge. The introduction of chatbot ‐ based pre-consultation interviews has also shortened preparation time: visitors enter their symptoms on a smartphone, and the summarized information is transferred directly into the electronic medical record before the examination begins.Yet the barriers to widespread adoption remain high. The most formidable obstacle is a narrow reimbursement window combined with lengthy review procedures. Many AI applications are still awaiting approval while gathering clinical data, so they can be used only as out-of-pocket services in everyday practice. Medical AI refers to technology that enables computers to learn Medical AI has certainly begun to take root in Japan. At the Data sharing among hospitals also lags behind, preventing AI developers from securing enough cases to improve accuracy, and domestic models struggle to catch up with those from overseas.Infrastructure presents another hurdle. In regional or mid-sized hospitals, outdated electronic chart systems and image servers persist, meaning that even state-of-the-art AI tools cannot perform to their full potential after installation. Furthermore, there is a chronic shortage of specialists who can handle medical data and AI, a bottleneck that slows the translation of research results into clinical use.Thus, while Japan’s medical AI scene is beginning to blossom, its growth is held back by four intertwined walls: reimbursement policy, data utilization, hospital infrastructure, and skilled human resources.What Happens if Japan’s Challenges Remain Unresolved?If the four barriers—reimbursement rules, data sharing, outdated infrastructure, and workforce shortages—are left standing, Japan’s healthcare system and society will inevitably begin to falter. The first impact will fall on patients. Without widespread use of AI–assisted image interpretation, physicians will take longer to review scans, and detection of time-critical illnesses such as cancer or stroke will be delayed. In rural areas, where specialists are scarce, it may become routine to wait several days for results, further widening the quality gap between urban and regional care.Working conditions for healthcare professionals will also deteriorate. When AI does not help with charting or organizing patient interviews, physicians spend long hours at their computers after clinics close, increasing fatigue-related turnover. Staff shortages then erode the overall quality of care and place even heavier burdens on those who remain, creating a vicious circle.Economic repercussions would follow. Overseas, rapid AI-driven diagnostics and telemedicine are becoming standard, so Japan could lose its competitiveness in medical tourism and international research partnerships. Without ample, high-quality data, domestic AI start-ups would struggle to scale, and talented researchers might migrate to environments where their work is better supported.Japan would likewise fall behind in global collaboration. If the country cannot join international networks that share data and AI models to raise diagnostic accuracy, it will miss out on the latest knowledge and risk isolation during pandemics or large-scale disasters, when cooperative medical frameworks are crucial.In short, neglecting these challenges would harm patient Chihiro ItoWhat Is Medical AI?Graduate School of Tokyo University of Science, JapanIs Japan's Medical AI Falling Behind?ー Problems Blocking Progress and Ways to Fix
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