Dataset Information

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.

SUBMITTER: Sounderajah V

PROVIDER: S-EPMC8240576 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Publications

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

Sounderajah Viknesh V Ashrafian Hutan H Golub Robert M RM Shetty Shravya S De Fauw Jeffrey J Hooft Lotty L Moons Karel K Collins Gary G Moher David D Bossuyt Patrick M PM Darzi Ara A Karthikesalingam Alan A Denniston Alastair K AK Mateen Bilal Akhter BA Ting Daniel D Treanor Darren D King Dominic D Greaves Felix F Godwin Jonathan J Pearson-Stuttard Jonathan J Harling Leanne L McInnes Matthew M Rifai Nader N Tomasev Nenad N Normahani Pasha P Whiting Penny P Aggarwal Ravi R Vollmer Sebastian S Markar Sheraz R SR Panch Trishan T Liu Xiaoxuan X

BMJ open 20210628 6

<h4>Introduction</h4>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. Thi ...[more]

PMID: 34183345

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Dataset Information

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

Introduction

Methods and analysis

Ethics and dissemination

Publications

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

Similar Datasets

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