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Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Abstract:
A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico, evaluation, but few have yet demonstrated real benefit to patient care. Early stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use, and pave the way to further large scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multistakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two round, modified Delphi process to collect and analyse expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 predefined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. 123 experts participated in the first round of Delphi, 138 in the second, 16 in the consensus meeting, and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI specific reporting items (made of 28 subitems) and 10 generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we have developed a guideline comprising key items that should be reported in early stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings.
Publication status:
Published
Peer review status:
Peer reviewed

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Files:
Publisher copy:
10.1136/bmj-2022-070904

Authors


More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Surgical Sciences
Role:
Author
ORCID:
0000-0002-0017-8891
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Balliol College
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
NDORMS
Sub department:
Botnar Research Centre
Role:
Author
ORCID:
0000-0002-2772-2316

Contributors

Role:
Contributor
Institution:
University of Oxford
Division:
MSD
Department:
Primary Care Health Sciences
Role:
Contributor
ORCID:
0000-0002-7984-514X
Institution:
University of Oxford
Division:
MSD
Department:
NDM
Role:
Contributor
Institution:
University of Oxford
Division:
MSD
Role:
Contributor
Institution:
University of Oxford
Division:
SSD
Department:
Oxford Internet Institute
Role:
Contributor


More from this funder
Funder identifier:
https://ror.org/054225q67
Grant:
27294
More from this funder
Funder identifier:
https://ror.org/0439y7842
Grant:
EP/N020774/1


Publisher:
BMJ Publishing Group
Journal:
BMJ More from this journal
Volume:
377
Article number:
e070904
Publication date:
2022-05-18
Acceptance date:
2022-04-26
DOI:
EISSN:
0959-8138
ISSN:
1759-2151
Pmid:
35584845


Language:
English
Pubs id:
1260771
Local pid:
pubs:1260771
Deposit date:
2022-07-20

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