Evidence map›Paper›PMID 37991819›Full record

SynthesisJournal of medical Internet research2023

Guidelines, Consensus Statements, and Standards for the Use of Artificial Intelligence in Medicine: Systematic Review.

Ying Wang, Nian Li, Lingmin Chen, Miaomiao Wu, Sha Meng, Zelei Dai, Yonggang Zhang, Mike Clarke

Erratum issuedAbstract readSystematic Review
In one paragraph

Synthesis in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
–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

27 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Ying Wang *Department of Medical Administration, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0001-7535-088X
Nian Li *Department of Medical Administration, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0003-3253-2565
Lingmin ChenDepartment of Anesthesiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0002-4542-2763
Miaomiao WuDepartment of General Practice, National Clinical Research Center for Geriatrics, International Medical Center, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0003-3035-9914
Sha MengDepartment of Operation Management, West China Hospital, Sichuan University, Chengdu, China.ORCID 0009-0000-5206-0900
Zelei DaiDepartment of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0002-5714-9741
Yonggang ZhangDepartment of Periodical Press, National Clinical Research Center for Geriatrics, Chinese Evidence-based Medicine Center, Nursing Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0002-5251-3815
Mike ClarkeNorthern Ireland Methodology Hub, Queen's University Belfast, Belfast, United Kingdom.ORCID 0000-0002-2926-7257

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe application of artificial intelligence (AI) in the delivery of health care is a promising area, and guidelines, consensus statements, and standards on AI regarding various topics have been developed.

objectiveWe performed this study to assess the quality of guidelines, consensus statements, and standards in the field of AI for medicine and to provide a foundation for recommendations about the future development of AI guidelines.

methodsWe searched 7 electronic databases from database establishment to April 6, 2022, and screened articles involving AI guidelines, consensus statements, and standards for eligibility. The AGREE II (Appraisal of Guidelines for Research & Evaluation II) and RIGHT (Reporting Items for Practice Guidelines in Healthcare) tools were used to assess the methodological and reporting quality of the included articles.

resultsThis systematic review included 19 guideline articles, 14 consensus statement articles, and 3 standard articles published between 2019 and 2022. Their content involved disease screening, diagnosis, and treatment; AI intervention trial reporting; AI imaging development and collaboration; AI data application; and AI ethics governance and applications. Our quality assessment revealed that the average overall AGREE II score was 4.0 (range 2.2-5.5; 7-point Likert scale) and the mean overall reporting rate of the RIGHT tool was 49.4% (range 25.7%-77.1%).

conclusionsThe results indicated important differences in the quality of different AI guidelines, consensus statements, and standards. We made recommendations for improving their methodological and reporting quality.

trial registrationPROSPERO International Prospective Register of Systematic Reviews (CRD42022321360); https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=321360.

Indexed as

Artificial IntelligenceMedicineConsensusConsensus Statements as TopicDatabases, FactualGuidelines as TopicHumansartificial intelligenceclinical practiceconsensus statementsguidelinesstandardssystematic review

Identifiers

PMID37991819
PMCPMC10701655

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.