Ueda Daiju, Walston Shannon, Takita Hirotaka, Mitsuyama Yasuhito, Miki Yukio
Department of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, Abeno-ku, Osaka, Japan.
Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Abeno-ku, Osaka, Japan.
Jpn J Radiol. 2025 Apr;43(4):537-541. doi: 10.1007/s11604-024-01716-y. Epub 2024 Dec 13.
Japan leads OECD countries in medical imaging technology deployment but lacks open, large-scale medical imaging databases crucial for AI development. While Japan maintains extensive repositories, access restrictions limit their research utility, contrasting with open databases like the US Cancer Imaging Archive and UK Biobank. The 2018 Next Generation Medical Infrastructure Act attempted to address this through new data-sharing frameworks, but implementation has been limited by strict privacy regulations and institutional resistance. This data gap risks compromising AI system performance for Japanese patients and limits global medical AI advancement. The solution lies not in developing individual AI models, but in democratizing access to well-curated Japanese medical imaging data. By implementing privacy-preserving techniques and streamlining regulatory processes, Japan could enhance domestic healthcare outcomes while contributing to more robust global AI models, ultimately reclaiming its position as a leader in medical innovation.
在医学成像技术应用方面,日本在经合组织国家中处于领先地位,但缺乏对人工智能发展至关重要的开放、大规模医学成像数据库。尽管日本拥有大量的存储库,但访问限制限制了它们的研究用途,这与美国癌症成像存档库和英国生物银行等开放数据库形成了对比。2018年的《下一代医疗基础设施法案》试图通过新的数据共享框架来解决这一问题,但实施受到严格隐私法规和机构抵制的限制。这种数据差距有可能损害日本患者的人工智能系统性能,并限制全球医疗人工智能的进步。解决方案不在于开发单个人工智能模型,而在于使对精心策划的日本医学成像数据的访问民主化。通过实施隐私保护技术和简化监管流程,日本可以改善国内医疗保健成果,同时为更强大的全球人工智能模型做出贡献,最终重新夺回其在医学创新领域的领先地位。