Literature DB >> 35585198

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

Baptiste Vasey1,2,3, Myura Nagendran4, Bruce Campbell5,6, David A Clifton7, Gary S Collins8, Spiros Denaxas9,10,11,12, Alastair K Denniston13,14,15, Livia Faes15, Bart Geerts16, Mudathir Ibrahim17,18, Xiaoxuan Liu13,14, Bilal A Mateen9,19,20, Piyush Mathur21, Melissa D McCradden22,23, Lauren Morgan24, Johan Ordish25, Campbell Rogers26, Suchi Saria27,28, Daniel S W Ting29,30, Peter Watkinson31,32, Wim Weber33, Peter Wheatstone34, Peter McCulloch17.   

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 multi-stakeholder, 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 analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined 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. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 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 ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we 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.
© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.

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Year:  2022        PMID: 35585198     DOI: 10.1038/s41591-022-01772-9

Source DB:  PubMed          Journal:  Nat Med        ISSN: 1078-8956            Impact factor:   87.241


  29 in total

1.  No surgical innovation without evaluation: the IDEAL recommendations.

Authors:  Peter McCulloch; Douglas G Altman; W Bruce Campbell; David R Flum; Paul Glasziou; John C Marshall; Jon Nicholl; Jeffrey K Aronson; Jeffrey S Barkun; Jane M Blazeby; Isabell C Boutron; W Bruce Campbell; Pierre-Alain Clavien; Jonathan A Cook; Patrick L Ergina; Liane S Feldman; David R Flum; Guy J Maddern; Jon Nicholl; Bournaby C Reeves; Christoph M Seiler; Steven M Strasberg; Jonathan L Meakins; Deborah Ashby; Nick Black; John Bunker; Martin Burton; Marion Campbell; Kalipso Chalkidou; Iain Chalmers; Marc de Leval; Jon Deeks; Patrick L Ergina; Adrian Grant; Muir Gray; Roger Greenhalgh; Milos Jenicek; Sean Kehoe; Richard Lilford; Peter Littlejohns; Yoon Loke; Rajan Madhock; Kim McPherson; Jonathan Meakins; Peter Rothwell; Bill Summerskill; David Taggart; Parris Tekkis; Matthew Thompson; Tom Treasure; Ulrich Trohler; Jan Vandenbroucke
Journal:  Lancet       Date:  2009-09-26       Impact factor: 79.321

2.  Clinical research underlies ethical integration of healthcare artificial intelligence.

Authors:  Melissa D McCradden; Elizabeth A Stephenson; James A Anderson
Journal:  Nat Med       Date:  2020-09       Impact factor: 53.440

3.  No Surgical Innovation Without Evaluation: Evolution and Further Development of the IDEAL Framework and Recommendations.

Authors:  Allison Hirst; Yiannis Philippou; Jane Blazeby; Bruce Campbell; Marion Campbell; Joshua Feinberg; Maroeska Rovers; Natalie Blencowe; Christopher Pennell; Tom Quinn; Wendy Rogers; Jonathan Cook; Angelos G Kolias; Riaz Agha; Philipp Dahm; Art Sedrakyan; Peter McCulloch
Journal:  Ann Surg       Date:  2019-02       Impact factor: 12.969

4.  From development to deployment: dataset shift, causality, and shift-stable models in health AI.

Authors:  Adarsh Subbaswamy; Suchi Saria
Journal:  Biostatistics       Date:  2020-04-01       Impact factor: 5.899

5.  The Clinician and Dataset Shift in Artificial Intelligence.

Authors:  Samuel G Finlayson; Adarsh Subbaswamy; Karandeep Singh; John Bowers; Annabel Kupke; Jonathan Zittrain; Isaac S Kohane; Suchi Saria
Journal:  N Engl J Med       Date:  2021-07-15       Impact factor: 91.245

Review 6.  The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World.

Authors:  Claire M Felmingham; Nikki R Adler; Zongyuan Ge; Rachael L Morton; Monika Janda; Victoria J Mar
Journal:  Am J Clin Dermatol       Date:  2021-03       Impact factor: 7.403

7.  A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis.

Authors:  Xiaoxuan Liu; Livia Faes; Aditya U Kale; Siegfried K Wagner; Dun Jack Fu; Alice Bruynseels; Thushika Mahendiran; Gabriella Moraes; Mohith Shamdas; Christoph Kern; Joseph R Ledsam; Martin K Schmid; Konstantinos Balaskas; Eric J Topol; Lucas M Bachmann; Pearse A Keane; Alastair K Denniston
Journal:  Lancet Digit Health       Date:  2019-09-25

Review 8.  Research Trends in Artificial Intelligence Applications in Human Factors Health Care: Mapping Review.

Authors:  Onur Asan; Avishek Choudhury
Journal:  JMIR Hum Factors       Date:  2021-06-18

9.  Aviation and healthcare: a comparative review with implications for patient safety.

Authors:  Narinder Kapur; Anam Parand; Tayana Soukup; Tom Reader; Nick Sevdalis
Journal:  JRSM Open       Date:  2015-12-02

10.  Human factors challenges for the safe use of artificial intelligence in patient care.

Authors:  Mark Sujan; Dominic Furniss; Kath Grundy; Howard Grundy; David Nelson; Matthew Elliott; Sean White; Ibrahim Habli; Nick Reynolds
Journal:  BMJ Health Care Inform       Date:  2019-11
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  5 in total

Review 1.  Artificial Intelligence in Colorectal Cancer Surgery: Present and Future Perspectives.

Authors:  Giuseppe Quero; Pietro Mascagni; Fiona R Kolbinger; Claudio Fiorillo; Davide De Sio; Fabio Longo; Carlo Alberto Schena; Vito Laterza; Fausto Rosa; Roberta Menghi; Valerio Papa; Vincenzo Tondolo; Caterina Cina; Marius Distler; Juergen Weitz; Stefanie Speidel; Nicolas Padoy; Sergio Alfieri
Journal:  Cancers (Basel)       Date:  2022-08-04       Impact factor: 6.575

2.  An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics.

Authors:  Jingyu Zhong; Yangfan Hu; Guangcheng Zhang; Yue Xing; Defang Ding; Xiang Ge; Zhen Pan; Qingcheng Yang; Qian Yin; Huizhen Zhang; Huan Zhang; Weiwu Yao
Journal:  Insights Imaging       Date:  2022-08-20

3.  A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability.

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Journal:  Insights Imaging       Date:  2022-08-20

4.  E-health as a sine qua non for modern healthcare.

Authors:  Rachel Knevel; Thomas Hügle
Journal:  RMD Open       Date:  2022-09

Review 5.  Oncologic Imaging and Radiomics: A Walkthrough Review of Methodological Challenges.

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Journal:  Cancers (Basel)       Date:  2022-10-05       Impact factor: 6.575

  5 in total

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