Clinical artificial intelligence (AI) in Japan: Regulatory pathways, domain-specific evidence, and its data infrastructure from an international perspective

Artificial intelligence (AI) has advanced rapidly across clinical domains, generating both a growing evidence base and dedicated regulatory frameworks for AI-based software as a medical device (SaMD). This review provides a comprehensive assessment of clinical AI across five major domains—diagnostic imaging, gastrointestinal endoscopy, cardiology and remote patient monitoring, diagnosis of infectious diseases, and an AI-ready data infrastructure—examining Japan's regulatory framework, approved device portfolio, and research contributions in an international context. We reviewed literature published between 2019 and 2026, using Japan's regulatory trajectory, approved device portfolio, and domain-specific research output as the primary lens for international comparison and prioritizing prospective studies, multicenter trials, and real-world implementation reports. The state of evidence varies markedly across the domains examined: endoscopy AI has the strongest randomized trial base, while diagnostic imaging AI has seen a systematic decline in real-world performance despite large-scale regulatory approval. Across the three dimensions examined, Japan has a distinctive profile: its strengths are a regulatory and clinical deployment infrastructure—evince by an established program medical device pathway and among the world's highest densities of diagnostic imaging systems and endoscopy volumes—while the data infrastructure lags, constrained by limited open-access resources relative to programs such as The Cancer Imaging Archive and the European Health Data Space. Large language models and generative AI, falling largely outside existing SaMD frameworks, carry the risk of hallucinations and gaps in oversight that healthcare systems in Japan and abroad are only beginning to address. Japan's established program medical device regulatory pathway, high-volume clinical deployment infrastructure, and proven regulatory-approval-to-reimbursement pathway provide a strong foundation for clinical AI adoption; post-approval change management frameworks and clinical accountability mechanisms need to be strengthened, AI-ready data accessibility needs to be expanded, and validated tools need to be embedded within reimbursed clinical workflows to translate this foundation into internationally competitive AI development and deployment.