Evidence mapPaperPMID 41849560Full record

ArticleJMIR medical informatics2026

Approval of AI-Based Medical Devices in China From 2020 to 2025: Retrospective Analysis.

Lingli Zhang, Jianzhou Yan

Abstract read
In one paragraph

Article in JMIR medical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Lingli ZhangSchool of International Pharmaceutical Business, China Pharmaceutical University, 639 Longmian Avenue, Jiangning District, Nanjing, 211198, China, 86 02586185038.ORCID http://orcid.org/0000-0001-7987-0217
Jianzhou YanSchool of International Pharmaceutical Business, China Pharmaceutical University, 639 Longmian Avenue, Jiangning District, Nanjing, 211198, China, 86 02586185038.ORCID http://orcid.org/0000-0002-0526-975X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence-based medical devices (AIMDs) have emerged as transformative technologies in modern health care. However, comprehensive analysis of recent approval trends and characteristics of AIMDs in China remains limited. Objective: This study aimed to provide an up-to-date overview of AIMDs approved in China up to June 2025. Methods: We conducted a search of the Drugdataexpy database to identify AIMDs approved up to June 30, 2025, using artificial intelligence-related keywords in the "structural composition" and "intended use" fields. After manual verification and exclusion of non-AIMDs, we collected key characteristics, including name, manufacturer, approval date, risk class, clinical evaluation pathway, medical specialty, data source, review pathway, and algorithm type. Statistical analysis encompassed descriptive statistics and trend analysis. We used the Fisher exact test and Pearson chi-square test to assess the associations between risk class and categorical variables. Results: A total of 154 AIMDs were identified since the first approval in 2020, with annual approvals increasing from 9 in 2020 to 45 in 2024 (a 49.53% compound annual growth rate), although the 20 approvals in the first half of 2025 suggest a potential moderation in pace. Most AIMDs (123/154, 79.9%) were categorized as class III, and the risk class was significantly associated with approval year (P=.03), manufacturer location (P=.03), and medical specialty (P=.004). Of the 123 class III devices, 19 (15.4%) were approved through innovation review, and 2 (1.6%) each were approved through priority and emergency approval. Deep learning was the dominant algorithm (143/154, 92.9%). Radiology dominated the field (106/154, 68.8%), with computed tomography serving as the primary data source (96/154, 62.3%), particularly for applications in pulmonary nodule detection and cardiovascular assessment. Clinical trials were the primary evaluation pathway for 76.6% (118/154) of all AIMDs. This approach was predominant for class III devices (116/123, 94.3%), whereas most class II devices (21/31, 67.7%) used a clinical exemption pathway. Market concentration was evident, with the top 4 manufacturers accounting for 38.3% (59/154) of all approvals and geographically clustered in major innovation hubs such as Beijing, Shanghai, Shenzhen, and Hangzhou. Conclusions: China's AIMD ecosystem is experiencing growth, heavily focused on radiology and computed tomography-based solutions within a risk-proportionate regulatory framework. The market is characterized by significant manufacturer and geographic concentration.

Indexed as

Artificial IntelligenceDevice ApprovalEquipment and SuppliesChinaHumansIntelligent SystemsRetrospective StudiesAIAI-based medical deviceartificial intelligenceartificial intelligence–based medical deviceChinadeep learningmedical devicemedical device approvalradiologyregulatory science

Identifiers

PMID41849560
PMCPMC12998604

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.