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

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

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Journal article

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

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.

Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Sounderajah, V., Ashrafian, H., Golub, R. M., Shetty, S., De Fauw, J., Hooft, L., Moons, K., Collins, G., Moher, D., Bossuyt, P. M., Darzi, A., Karthikesalingam, A., Denniston, A. K., Mateen, B. A., Ting, D., Treanor, D., King, D., Greaves, F., Godwin, J., … Liu, X. (2021). Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open, 11(6).

MLA Style

Sounderajah, V, et al. “Developing a Reporting Guideline for Artificial Intelligence-Centred Diagnostic Test Accuracy Studies: the STARD-AI Protocol.” BMJ Open, vol. 11, no. 6, 2021.

Chicago Style

Sounderajah, V, H Ashrafian, RM Golub, et al. 2021. “Developing a Reporting Guideline for Artificial Intelligence-Centred Diagnostic Test Accuracy Studies: the STARD-AI Protocol.” BMJ Open 11 (6).
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