Scoping review of regulatory transparency in AI-based radiology software: analysis of PMDA-approved SaMD products

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Abstract

Background: The integration of artificial intelligence (AI) in radiology has accelerated globally, with Japan’s Pharmaceuticals and Medical Devices Agency (PMDA) approving numerous AI-based Software as a Medical Device (SaMD) products. However, the transparency and completeness of clinical evidence available to healthcare providers remain unclear. Purpose: To systematically evaluate the availability and transparency of clinical evidence in package inserts of PMDA-approved AI-based radiology SaMD products, identifying gaps that may impact clinical implementation. Materials and methods: We conducted a scoping review of all PMDA-approved SaMD products as of December 31, 2024. Products were included if they utilized AI technology and were classified for radiology applications. Data extraction focused on product characteristics, study designs, demographic information, and performance metrics. Results: Of 151 approved SaMD products, 40 utilized AI technology, with 20 specifically designed for radiology applications. Critical gaps were identified in demographic reporting, with no products providing complete case demographic data. Performance metrics varied widely, with sensitivity ranging from 67.7% to 100% in standalone studies. Physician-assisted studies consistently demonstrated performance improvements but lacked stratified results by characteristics in all cases. Conclusion: Current package insert requirements provide insufficient transparency for evidence-based clinical implementation of AI-based radiology SaMD. Enhanced regulatory frameworks and industry-led initiatives for comprehensive validation are essential for safe and effective AI deployment in Japanese healthcare.

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APA

Kikuchi, T., Walston, S. L., Takita, H., Mitsuyama, Y., Ito, R., Hashimoto, M., … Ueda, D. (2026). Scoping review of regulatory transparency in AI-based radiology software: analysis of PMDA-approved SaMD products. Japanese Journal of Radiology, 44(6), 1095–1111. https://doi.org/10.1007/s11604-025-01942-y

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