Evidence map›Paper›PMID 41533310›Full record

ArticleJapanese journal of radiology2026

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

Tomohiro Kikuchi, Shannon L Walston, Hirotaka Takita, Yasuhito Mitsuyama, Rintaro Ito, Masahiro Hashimoto, Takeshi Nakaura, Hiroaki Hyakutake, Sho Kawabe, Harushi Mori and 1 more

Abstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in Japanese journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Tomohiro KikuchiDepartment of Radiology, Jichi Medical University, 3311-1, Yakushiji, Shimotsuke-shi, Tochigi, 329-0498, Japan.
Shannon L WalstonDepartment of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.
Hirotaka TakitaDepartment of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.
Yasuhito MitsuyamaDepartment of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.
Rintaro ItoDepartment of Innovative BioMedical Visualization, Nagoya University Graduate School of Medicine, Showa-ku, Nagoya, Japan.
Masahiro HashimotoDepartment of Radiology, Keio University School of Medicine, Shinjukuku, Tokyo, Japan.
Takeshi NakauraDepartment of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Chuo-ku, Kumamoto, Japan.
Hiroaki HyakutakeMedical AI Promotion Institute Co., Ltd., Life Science Building, 12-9 Nihonbashi Odemmachi, Chuo-ku, Tokyo, Japan.
Sho KawabeMedical AI Promotion Institute Co., Ltd., Life Science Building, 12-9 Nihonbashi Odemmachi, Chuo-ku, Tokyo, Japan.
Harushi MoriDepartment of Radiology, Jichi Medical University, 3311-1, Yakushiji, Shimotsuke-shi, Tochigi, 329-0498, Japan.
Daiju UedaMedical AI Promotion Institute Co., Ltd., Life Science Building, 12-9 Nihonbashi Odemmachi, Chuo-ku, Tokyo, Japan. daiju.ueda@mapi-jp.org.ORCID http://orcid.org/0000-0002-3878-3616

Funding

JPMJPR2521 JPMJPR2521
6 · The paper itself

Abstract

backgroundThe 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

methodsWe 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.

resultsOf 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.

conclusionCurrent 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.

Indexed as

Artificial IntelligenceDevice ApprovalEquipment and SuppliesRadiologySoftwareHumansJapanArtificial intelligenceData transparencyPMDARadiologyScoping reviewSoftware as a medical device

Identifiers

PMID41533310
PMCPMC13222318

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.