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Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol

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Title: Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol
Authors: Sounderajah, V
Ashrafian, H
Golub, RM
Shetty, S
De Fauw, J
Hooft, L
Moons, K
Collins, G
Moher, D
Bossuyt, PM
Darzi, A
Karthikesalingam, A
Denniston, AK
Mateen, BA
Ting, D
Treanor, D
King, D
Greaves, F
Godwin, J
Pearson-Stuttard, J
Harling, L
McInnes, M
Rifai, N
Tomasev, N
Normahani, P
Whiting, P
Aggarwal, R
Vollmer, S
Markar, SR
Panch, T
Liu, X
STARD-AI Steering Committee
Item Type: Journal Article
Abstract: Introduction Standards for Reporting of Diagnostic Accuracy Study (STARD) was developed to improve the completeness and transparency of reporting in studies investigating diagnostic test accuracy. However, its current form, STARD 2015 does not address the issues and challenges raised by artificial intelligence (AI)-centred interventions. As such, we propose an AI-specific version of the STARD checklist (STARD-AI), which focuses on the reporting of AI diagnostic test accuracy studies. This paper describes the methods that will be used to develop STARD-AI. Methods and analysis The development of the STARD-AI checklist can be distilled into six stages. (1) A project organisation phase has been undertaken, during which a Project Team and a Steering Committee were established; (2) An item generation process has been completed following a literature review, a patient and public involvement and engagement exercise and an online scoping survey of international experts; (3) A three-round modified Delphi consensus methodology is underway, which will culminate in a teleconference consensus meeting of experts; (4) Thereafter, the Project Team will draft the initial STARD-AI checklist and the accompanying documents; (5) A piloting phase among expert users will be undertaken to identify items which are either unclear or missing. This process, consisting of surveys and semistructured interviews, will contribute towards the explanation and elaboration document and (6) On finalisation of the manuscripts, the group’s efforts turn towards an organised dissemination and implementation strategy to maximise end-user adoption. Ethics and dissemination Ethical approval has been granted by the Joint Research Compliance Office at Imperial College London (reference number: 19IC5679). A dissemination strategy will be aimed towards five groups of stakeholders: (1) academia, (2) policy, (3) guidelines and regulation, (4) industry and (5) public and non-specific stakeholders. We anticipate that dissemination will take place in Q3 of 2021.
Issue Date: 28-Jun-2021
Date of Acceptance: 8-Jun-2021
URI: http://hdl.handle.net/10044/1/90367
DOI: 10.1136/bmjopen-2020-047709
ISSN: 2044-6055
Publisher: BMJ Journals
Journal / Book Title: BMJ Open
Volume: 11
Issue: 6
Copyright Statement: © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
Sponsor/Funder: National Institute of Health Research
Keywords: health informatics
protocols & guidelines
quality in health care
STARD-AI Steering Committee
1103 Clinical Sciences
1117 Public Health and Health Services
1199 Other Medical and Health Sciences
Publication Status: Published
Article Number: ARTN e047709
Appears in Collections:Department of Surgery and Cancer
Institute of Global Health Innovation
School of Public Health

This item is licensed under a Creative Commons License Creative Commons