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Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI
Baptiste Vasey, Myura Nagendran, Bruce Campbell, David A. Clifton, Gary S. Collins, Spiros Denaxas, Alastair K. Denniston, Livia Faes, Bart Geerts, Mudathir Ibrahim, Xiaoxuan Liu, Bilal A. Mateen, Piyush Mathur, Melissa D. McCradden, Lauren Morgan, Johan Ordish, Campbell Rogers, Suchi Saria, Daniel Shu Wei Ting, Peter Watkinson, Wim Weber, Peter Wheatstone, Peter McCulloch, Aaron Lee, Alan G. Fraser, Ali Connell, Alykhan Vira, Andre Esteva, Andrew D. Althouse, Andrew L. Beam, Anne de Hond, Anne‐Laure Boulesteix, Anthony Bradlow, Ari Ercole, Arsenio Páez, Athanasios Tsanas, Barry Kirby, Ben Glocker, Carmelo Velardo, Chang Min Park, Charisma Hehakaya, Chris Baber, Chris Paton, Christian Johner, Christopher Kelly, Christopher Yau, Clare McGenity, Constantine Gatsonis, Corinne Faivre‐Finn, Crispin Simon, Danielle Sent, Danilo Bzdok, Darren Treanor, David Wong, David F. Steiner, David Higgins, Dawn Benson, Declan P. O’Regan, Dinesh V. Gunasekaran, Dominic Danks, Emanuele Neri, Evangelia Kyrimi, Falk Schwendicke, Farah Magrabi, Frances Ives, Frank Rademakers, G Fowler, Giuseppe Frau, Henry David Jeffry Hogg, Hani J. Marcus, Heang‐Ping Chan, Henry Xiang, Hugh McIntyre, Hugh Harvey, Hyungjin Kim, Ibrahim Habli, James C. Fackler, James Shaw, Janet Higham, Jared M. Wohlgemut, Jaron Chong, Jean‐Emmanuel Bibault, Jérémie F. Cohen, Jesper Kers, Jessica Morley, Joachim Krois, João Monteiro, Joel Horovitz, John Fletcher, Jonathan Taylor, Jung Hyun Yoon, Karandeep Singh, Karel G.M. Moons, Kassandra Karpathakis, Ken Catchpole, Kerenza Hood, Konstantinos Balaskas, Konstantinos Kamnitsas, Laura G. Militello, Laure Wynants, Lauren Oakden‐Rayner, Laurence Lovat, Luc Smits, Ludwig Christian Hinske, M. Khair ElZarrad, Maarten van Smeden, Mara Giavina‐Bianchi, Mark Daley, Mark Sendak, Mark Sujan, Maroeska M. Rovers, Matthew DeCamp, Matthew Woodward, Matthieu Komorowski, Max Marsden, Maxine Mackintosh, Michael D. Abràmoff, Miguel Ángel Armengol de la Hoz, Neale Hambidge, Neil Daly, Niels Peek, Oliver Redfern, Omer F. Ahmad, Patrick M. Bossuyt, Pearse A. Keane, Pedro Ferreira, Petra Schnell‐Inderst, Pietro Mascagni, Prokar Dasgupta, Pujun Guan, Rachel Barnett, Rawen Kader, Reena Chopra, Ritse M. Mann, Rupa Sarkar, Saana M. Mäenpää, Samuel G. Finlayson, Sarah Vollam, Sebastian J. Vollmer, Seong Ho Park, Shakir Laher, Shalmali Joshi, Siri Lise van der Meijden, Susan C. Shelmerdine, Tien‐En Tan, Tom J. W. Stocker, Valentina Giannini, Vince I. Madai, Virginia Newcombe, Wei Yan Ng, Wendy Rogers, William Ogallo, Yoonyoung Park, Zane Perkins
Bibliographic record
VenueNature Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicArtificial Intelligence in Healthcare and Education
Canadian institutionsWestern UniversityMila - Quebec Artificial Intelligence InstituteHospital for Sick ChildrenPublic Health OntarioUniversity of Toronto
FundersNIHR Oxford Biomedical Research CentreNational Medical Research CouncilMedical Research CouncilRussian Science FoundationNational Institutes of HealthNational Health Insurance ServiceAgency for Science, Technology and ResearchUniversity of OxfordCancer Research UKWellcome TrustUniversity College LondonResearch Councils UKNational Institute for Health and Care ResearchAlan Turing InstituteGlaxoSmithKlineEngineering and Physical Sciences Research CouncilPioneer FundNational Science FoundationUK Research and InnovationAstraZenecaDuke-NUS Medical SchoolAmerican Heart AssociationSheffield Teaching Hospitals NHS Foundation TrustAlfred P. Sloan FoundationNational Heart, Lung, and Blood InstituteAcademy of Medical Sciences
KeywordsGuidelineStage (stratigraphy)Clinical decision support systemDecision support systemArtificial intelligenceComputer scienceMedicinePathologyBiology
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.075
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Opus teacher head0.552
GPT teacher head0.632
Teacher spread0.080 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
Study designNot applicable
DomainReporting
GenreMethods
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Quick stats
Citations519
Published2022
Admission routes1
Has abstractno