ArticleScientific reports2022
Systematic analysis of the test design and performance of AI/ML-based medical devices approved for triage/detection/diagnosis in the USA and Japan.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Japanese regulation and approval process for medical artificial intelligence (AI) as software as a medical device (SaMD): Current status and emerging challenges.Global health & medicine · 2026Review
- Transforming Stroke Diagnosis with Artificial Intelligence: A Scoping Review of Brainomix e-Stroke, Aidoc, RapidAI, and Viz.ai.Medicina (Kaunas, Lithuania) · 2026Article
- Machine-learning algorithms for detecting intracranial hemorrhage on head computed tomography.The Cochrane database of systematic reviews · 2026Article
- Digital pathology and artificial intelligence in breast and gynecologic oncology: from molecular prediction to multimodal integration.Frontiers in oncology · 2026Review
- Artificial Intelligence in Emergency Medicine: A Primer for the Nonexpert.Journal of the American College of Emergency Physicians open · 2025Article
- Advancing clinical biochemistry: addressing gaps and driving future innovations.Frontiers in medicine · 2025Review
- Utilizing ChatGPT for Curriculum Learning in Developing a Clinical Grade Pneumothorax Detection Model: A Multisite Validation Study.Journal of clinical medicine · 2024Article
- Deep learning model for pleural effusion detection via active learning and pseudo-labeling: a multisite study.BMC medical imaging · 2024Article
- Fairness of artificial intelligence in healthcare: review and recommendations.Japanese journal of radiology · 2024Review
- Development and validation of a reinforcement learning model for ventilation control during emergence from general anesthesia.NPJ digital medicine · 2023Article
- Performance evaluation methods for improvements at post-market of artificial intelligence/machine learning-based computer-aided detection/diagnosis/triage in the United States.PLOS digital health · 2023Article
- Device failures and patient adverse events from EUS: 26-year FDA MAUDE analysis (2000-2025).Endoscopic ultrasoundArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of computer-aided detection (CAD) using artificial intelligence (AI) and machine learning (ML) is rapidly evolving. Submission of AI/ML-based CAD devices for regulatory approval requires information about clinical trial design and performance criteria, but the requirements vary between countries. This study compares the requirements for AI/ML-based CAD devices approved by the US Food and Drug Administration (FDA) and the Pharmaceuticals and Medical Devices Agency (PMDA) in Japan. A list of 45 FDA-approved and 12 PMDA-approved AI/ML-based CAD devices was compiled. In the USA, devices classified as computer-aided simple triage were approved based on standalone software testing, whereas devices classified as computer-aided detection/diagnosis were approved based on reader study testing. In Japan, however, there was no clear distinction between evaluation methods according to the category. In the USA, a prospective randomized controlled trial was conducted for AI/ML-based CAD devices used for the detection of colorectal polyps, whereas in Japan, such devices were approved based on standalone software testing. This study indicated that the different viewpoints of AI/ML-based CAD in the two countries influenced the selection of different evaluation methods. This study's findings may be useful for defining a unified global development and approval standard for AI/ML-based CAD.
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