The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index; none of the 7 sources its claims cite is checked for support yet.
Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol
Open accessAbstract
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.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The STARD-AI protocol describes the planned development of an AI-specific reporting guideline for diagnostic test accuracy studies, extending STARD 2015. It outlines a six-stage process including item generation and a three-round modified Delphi consensus, with publication anticipated in Q3 2021.
Key findings
- STARD 2015 is the most widely accepted reporting standard for diagnostic test accuracy studies, consisting of 30 items and endorsed by over 200 biomedical journals, but it does not address AI-specific issues such as data preprocessing, model development choices, external validation datasets, standardised nomenclature, and unfamiliar outcome measures.
- STARD-AI development is distilled into six stages: project organisation; item generation; a three-round modified Delphi consensus; drafting the checklist and accompanying documents; piloting among expert users; and finalisation, publication, and dissemination.
- Item generation included a January 2020 literature review, a February 2020 online scoping survey of 80 international experts that generated over 2500 responses, and a patient and public involvement and engagement focus group.
- The modified Delphi uses a 5-point Likert-like scale with a predefined consensus threshold of ≥75%; items rated 1 or 2 by ≥75% are deemed essential, items rated 4 or 5 by ≥75% are excluded, and non-consensus items proceed to later rounds or the virtual consensus meeting.
Methods
- Protocol constructed in accordance with the EQUATOR Network toolkit for developing reporting guidelines, with a nine-member Project Team and a multidisciplinary Steering Committee.
- Item generation combined a literature review of MEDLINE and EMBASE via Ovid, an online scoping survey of 80 experts, and a remote patient and public involvement and engagement focus group; findings were classified into four categories: general considerations, suggested modifications, additions, and removals to STARD 2015 items.
- A pragmatic modified Delphi consensus process is underway: rounds 1 and 2 are online surveys using DelphiManager V.4.0, and round 3 is a virtual teleconference consensus meeting using the nominal group technique; piloting will use surveys and semistructured interviews with thematic analysis.
Limitations
- The paper states: “Views of Delphi panellists may differ from those experts who decline participation.”
- Evident from the text: this is a protocol, so no final STARD-AI checklist, Delphi consensus results, pilot findings, or implementation outcomes are reported.
- Evident from the text: development relies on expert consensus and selected stakeholder groups; no formal validation or reliability testing of the eventual checklist is described in this protocol.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
6 Not checked yet · 6 Own method · 3 Own interpretation · 2 No basis given · 1 Own result
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Without this guidance, and in a relatively nascent area, key stakeholders are poorly placed to appraise quality and compare diagnostic accuracy between scientific studies.”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Much of the evidence supporting diagnostic algorithms has been disseminated in the absence of AI-specific reporting guidelines.
In the paper
“The Standards for Reporting of Diagnostic Accuracy Studies (STARD) 2015 statement remains the most widely accepted set of reporting standards for diagnostic test accuracy studies.5”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Introduction
What the sources say
- Not checked yetcites [5] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Issues include unclear methodological interpretation (eg, data preprocessing steps, model development choices and the use of external validation datasets), the lack of standardised nomenclature (eg, the varying definition of the term ‘validation’), as well as the use of unfamiliar outcome measures (eg, Jaccard similarity coefficient and F-score).”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“studies have shown that adherence to the STARD checklist leads to improved reporting of key study parameters.7 8”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Introduction
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“However, its current form, STARD 2015 does not address the issues and challenges raised by artificial intelligence (AI)-centred interventions.”
- Also stated in: § Introduction · plain
“However, in its current iteration, STARD 2015 is not designed to address the issues and challenges raised by AI-driven modalities.”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Abstract > Introduction
What the sources say
- Not checked yetcites [5] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Introduction · plain
In the paper
“The majority of AI interventions that are close to translation are in the field of medical diagnostics.3”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Introduction
What the sources say
- Not checked yetcites [3] Benjamens, 2020, The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database, NPJ Digit Med source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 12
- It may state two separate claims as one. 3
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This process generated over 2500 responses, which were analysed and classed into the aforementioned four broad categories.”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Methods > Stage 2: item generation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The threshold for consensus is predefined at ≥75%.”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Methods > Stage 3: modified Delphi consensus process (ongoing)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, ensuring fair representation across geographies and demographics was a pertinent consideration during recruitment.”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Methods > Stage 3: modified Delphi consensus process (ongoing)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“There are no specific reporting standards for artificial intelligence (AI) diagnostic test accuracy studies.”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Abstract > Ethics and dissemination
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Prior to stage 2, the STARD-AI project was registered with the EQUATOR Network.”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Methods > Stage 1: project organisation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Views of Delphi panellists may differ from those experts who decline participation.”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Abstract > Ethics and dissemination
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Two investigators (VS and HA) independently identified common themes and subthemes from the discussion, which were classed into the aforementioned four broad categories.”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Methods > Stage 2: item generation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This study has adopted a pragmatic modified Delphi consensus methodology.”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Methods > Stage 3: modified Delphi consensus process (ongoing)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“This protocol has been constructed in accordance with the EQUATOR Network toolkit for developing reporting guidelines.14”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Methods
What the sources say
- Not checked yetcites [14] The EQUATOR Network . Toolkits. Available: https://www.equator-network.org/toolkits/ [Accessed 26 Sep 2020].
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The Delphi consensus methodology is a well-established method17 of obtaining a collective opinion from a group of experts through a series of questionnaires; each one refined based on feedback from respondents.”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Methods > Stage 3: modified Delphi consensus process (ongoing)
What the sources say
- Not checked yetcites [17] Brown BB . Delphi process: a methodology used for the elicitation of opinions of experts. published online first, 1968. Available: https://www.rand.org/pubs/papers/P3925.html [Accessed 26 Sep 2020].
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Two individuals (VS and HA) individually screened study titles and abstracts for inclusion. Disagreements were resolved through discussion.”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Methods > Stage 2: item generation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Until these issues are addressed, achieving comprehensive evaluations of these technologies and their potential translational benefits will remain limited.”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper relies on this without support.
A significant number of contemporary AI diagnostic studies include information related to both the development and testing (validation) of AI-centred index tests.
“A significant number of contemporary AI diagnostic studies include information related to both the development and testing (validation) of AI-centred index tests.”
The paper relies on this without support.
Much of the evidence supporting diagnostic algorithms has been disseminated in the absence of AI-specific reporting guidelines.
“Although this paradigm shift may seem imminent, it is crucial to note that much of the evidence supporting diagnostic algorithms has been disseminated in the absence of AI-specific reporting guidelines.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“while STARD-AI provides a framework for reporting AI-based diagnostic accuracy studies [71].”
From Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22. (2025) · cites this paper as [71]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“A protocol that outlined the process for developing STARD-AI was subsequently published 56 .”
From The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence (2025) · cites this paper as [56]
In the citing paper
“Some of these are costly, need high technical support, and have good results [24–27].”
From AI in medical education: uses of AI in construction type A MCQs (2024) · cites this paper as [25]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Abstract > Ethics and dissemination
“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.”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Methods > Stage 5: piloting phase
“The dissemination strategy will be principally tailored towards five groups of stakeholders: (1) academia, (2) policy, (3) guidelines and regulation, (4) industry and (5) patient representing bodies.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Methods > Stage 1: project organisation
“the former UK Minister for Health and the current chair for the National Health Service Accelerated Access Collaborative (AD)”
§ Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol > Glossary > Project team
“the former UK Minister for Health and the current chair for the NHS Accelerated Access Collaborative (AKD)”
Citations
Cites
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies
Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L., Lijmer, J. G., Moher, D., Rennie, D., de Vet, H. C. W., et al. (2015) · BMJ · doi:10.1136/bmj.h5527 · Cited by 3,660
Self-citationThe COMET Handbook: version 1.0
Williamson, P. R., Altman, D. G., Bagley, H., Barnes, K. L., Blazeby, J. M., Brookes, S. T., Clarke, M., Gargon, E., Gorst, S., Harman, N., et al. (2017) · Trials · doi:10.1186/s13063-017-1978-4 · Cited by 2,011
How to use the nominal group and Delphi techniques
McMillan, S. S., King, M. A., & Tully, M. P. (2016) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-016-0257-x · Cited by 1,570
Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Chan, A.-W., Darzi, A., Holmes, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1034-x · Cited by 1,421
Self-citationBenjamens, S., Dhunnoo, P., & Meskó, B. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00324-0 · Cited by 1,215
The STARD Statement for Reporting Studies of Diagnostic Accuracy: Explanation and Elaboration
Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., Moher, D., Rennie, D., de Vet, H. C. W., & Lijmer, J. G. (2003) · Annals of Internal Medicine · doi:10.7326/0003-4819-138-1-200301070-00012-w1 · Cited by 1,124
Cruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Darzi, A., Holmes, C., Yau, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1037-7 · Cited by 933
Self-citationNominal group technique: An effective method for obtaining group consensus
Harvey, N., & Holmes, C. A. (2012) · International Journal of Nursing Practice · doi:10.1111/j.1440-172x.2012.02017.x · Cited by 813
Enhancing the quality and transparency of health research
Groves, T. (2008) · British medical journal · doi:10.1136/bmj.a718 · Cited by 727
Cited by
Sorted by citations
Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., et al. (2024) · BMJ · doi:10.1136/bmj-2023-078378 · Cited by 4,718
Self-citationKocak, B., Baessler, B., Bakas, S., Cuocolo, R., Fedorov, A., Maier-Hein, L., Mercaldo, N., Müller, H., Orlhac, F., Pinto dos Santos, D., et al. (2023) · Insights into Imaging · doi:10.1186/s13244-023-01415-8 · Cited by 471
Tam, T. Y. C., Sivarajkumar, S., Kapoor, S., Stolyar, A. V., Polanska, K., McCarthy, K. R., Osterhoudt, H., Wu, X., Visweswaran, S., Fu, S., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01258-7 · Cited by 424
The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence
Sounderajah, V., Guni, A., Liu, X., Collins, G. S., Karthikesalingam, A., Markar, S. R., Golub, R. M., Denniston, A. K., Shetty, S., Moher, D., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03953-8 · Cited by 352
Self-citationMcGenity, C., Clarke, E. L., Jennings, C., Matthews, G., Cartlidge, C., Freduah-Agyemang, H., Stocken, D. D., & Treanor, D. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01106-8 · Cited by 290
Self-citationWhere Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis
Thurzo, A., Urbanová, W., Novák, B., Czakó, L., Siebert, T., Stano, P., Mareková, S., Fountoulaki, G., Kosnáčová, H., & Varga, I. (2022) · Healthcare · doi:10.3390/healthcare10071269 · Cited by 221
Methods for clinical evaluation of artificial intelligence algorithms for medical diagnosis
Park, S. H., Han, K., Jang, H. Y., Park, J. E., Lee, J.-G., Kim, D. W., & Choi, J. (2023) · Radiology · doi:10.1148/radiol.220182 · Cited by 163
Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology
Daneshjou, R., Barata, C., Betz‐Stablein, B., Celebi, M. E., Codella, N., Combalia, M., Guitera, P., Gutman, D. A., Halpern, A. C., Helba, B., et al. (2021) · JAMA Dermatology · doi:10.1001/jamadermatol.2021.4915 · Cited by 166
From real-world electronic health record data to real-world results using artificial intelligence
Knevel, R., & Liao, K. P. (2022) · Annals of the Rheumatic Diseases · doi:10.1136/ard-2022-222626 · Cited by 169
Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis
Kolbinger, F. R., Veldhuizen, G. P., Zhu, J., Truhn, D., & Kather, J. N. (2024) · Communications Medicine · doi:10.1038/s43856-024-00492-0 · Cited by 160
APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support
Kwong, J. C. C., Khondker, A., Lajkosz, K., McDermott, M. B. A., Borrat, X., McCradden, M. D., Mamdani, M., Kulkarni, G. S., & Johnson, A. E. W. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.35377 · Cited by 159
Demographic bias in misdiagnosis by computational pathology models
Vaidya, A. J., Chen, R. J., Williamson, D. F. K., Song, A. H., Jaume, G., Yang, Y., Hartvigsen, T., Dyer, E., Lu, M. Y., Lipková, J., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02885-z · Cited by 148
Sadr, S., Mohammad‐Rahimi, H., Motamedian, S. R., Zahedrozegar, S., Motie, P., Vinayahalingam, S., Dianat, O., & Nosrat, A. (2022) · Journal of Endodontics · doi:10.1016/j.joen.2022.12.007 · Cited by 126
Deep learning in oral cancer- a systematic review
Warin, K., & Suebnukarn, S. (2024) · BMC Oral Health · doi:10.1186/s12903-024-03993-5 · Cited by 117
Jacob, C., Brasier, N., Laurenzi, E., Heuss, S., Mougiakakou, S. G., Çöltekin, A., & Peter, M. K. (2024) · Journal of Medical Internet Research · doi:10.2196/67485 · Cited by 125
Nahhas, O. S. M. E., van Treeck, M., Wölflein, G., Unger, M., Ligero, M., Lenz, T., Wagner, S. J., Hewitt, K. J., Khader, F., Foersch, S., et al. (2024) · Nature Protocols · doi:10.1038/s41596-024-01047-2 · Cited by 121
Muralidharan, V., Burgart, A. M., Daneshjou, R., & Rose, S. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00898-5 · Cited by 112
Fraser, A. G., Biasin, E., Bijnens, B. H., Bruining, N., Caiani, E. G., Cobbaert, K., Davies, R. H., Gilbert, S., Hovestadt, L., Kamenjašević, E., et al. (2023) · Expert Review of Medical Devices · doi:10.1080/17434440.2023.2184685 · Cited by 116
Föllmer, B., Williams, M. C., Dey, D., Arbab‐Zadeh, A., Maurovich‐Horvat, P., Volleberg, R. H. J. A., Rueckert, D., Schnabel, J. A., Newby, D. E., Dweck, M. R., et al. (2023) · Nature Reviews Cardiology · doi:10.1038/s41569-023-00900-3 · Cited by 105
Reporting Use of AI in Research and Scholarly Publication—JAMA Network Guidance
Flanagin, A., Pirracchio, R., Khera, R., Berkwits, M., Hswen, Y., & Bibbins-Domingo, K. B. (2024) · JAMA · doi:10.1001/jama.2024.3471 · Cited by 100
Guidelines on clinical research evaluation of artificial intelligence in ophthalmology (2023)
Yang, W., & Xu, Y. (2023) · International Journal of Ophthalmology · doi:10.18240/ijo.2023.09.02 · Cited by 92
Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis
Krakowski, I., Kim, J., Cai, Z. R., Daneshjou, R., Lapins, J., Eriksson, H., Lykou, A., & Linos, E. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01031-w · Cited by 87
Homeyer, A., Geißler, C., Schwen, L. O., Zakrzewski, F., Evans, T., Strohmenger, K., Westphal, M., Bülow, R. D., Kargl, M., Karjauv, A., et al. (2022) · Modern Pathology · doi:10.1038/s41379-022-01147-y · Cited by 88
Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22.
Cecconi, M., Greco, M., Shickel, B., Angus, D. C., Bailey, H., Bignami, E., Calandra, T., Celi, L. A., Einav, S., Elbers, P., et al. (2025) · Critical Care · doi:10.1186/s13054-025-05532-2 · Cited by 80
“CON-SORT-AI extends the CONSORT framework to include AI-specific elements such as algorithm transparency and reproducibility for interventional trials with AI [70], while STARD-AI provides a framework for reporting AI-based diagnostic accuracy studies [71].”
Guni, A., Sounderajah, V., Whiting, P. F., Bossuyt, P. M., Darzi, A. W., & Ashrafian, H. (2024) · JMIR Research Protocols · doi:10.2196/58202 · Cited by 81
Self-citationArtificial intelligence in orthopaedics: A scoping review
Federer, S. J., & Jones, G. G. (2021) · PLoS ONE · doi:10.1371/journal.pone.0260471 · Cited by 77
A cardiologist’s guide to machine learning in cardiovascular disease prognosis prediction
Kresoja, K., Unterhuber, M., Wachter, R., Thiele, H., & Lurz, P. C. (2023) · Basic Research in Cardiology · doi:10.1007/s00395-023-00982-7 · Cited by 79
Turosz, N., Chęcińska, K., Chęciński, M. A., Brzozowska, A., Nowak, Z., & Sikora, M. (2023) · Dentomaxillofacial Radiology · doi:10.1259/dmfr.20230284 · Cited by 74
Artificial intelligence in glaucoma: opportunities, challenges, and future directions
Huang, X., Islam, M. R., Prome, S. A., Ahmed, F., Kazami, E., Serhan, H. A. A., Abd-Alrazaq, A. A., & Yousefi, S. (2023) · BioMedical Engineering OnLine · doi:10.1186/s12938-023-01187-8 · Cited by 73
How AI challenges the medical device regulation: patient safety, benefits, and intended uses
Onitiu, D., Wachter, S., & Mittelstadt, B. (2024) · Journal of Law and the Biosciences · doi:10.1093/jlb/lsae007 · Cited by 61
Laleh, N. G., Ligero, M., Pérez-López, R., & Kather, J. N. (2022) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-22-0390 · Cited by 59
Goh, S. S. N., Goh, R. S. J., Chong, B., Ng, Q. X., Koh, G. C., Ngiam, K. Y., & Hartman, M. (2024) · Journal of Medical Internet Research · doi:10.2196/62941 · Cited by 66
Martindale, A. P. L., Llewellyn, C. D., de Visser, R. O., Ng, B., Ngai, V., Kale, A. U., di Ruffano, L. F., Golub, R. M., Collins, G. S., Moher, D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45355-3 · Cited by 58
Self-citationNiemiec, E. (2022) · Digital Health · doi:10.1177/20552076221089079 · Cited by 71
ARTIFICIAL INTELLIGENCE PLATFORMS IN DENTAL CARIES DETECTION: A SYSTEMATIC REVIEW AND META-ANALYSIS
Abbott, L. P., Saikia, A., & Anthonappa, R. P. (2024) · Journal of Evidence Based Dental Practice · doi:10.1016/j.jebdp.2024.102077 · Cited by 63
Artificial intelligence in cancer: applications, challenges, and future perspectives
Cheng, H., & Shi, S.-S. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02450-3 · Cited by 64
Clinical Applications of Artificial Intelligence in Glaucoma
Yousefi, S. (2023) · Journal of Ophthalmic and Vision Research · doi:10.18502/jovr.v18i1.12730 · Cited by 59
AI, agentic models and lab automation for scientific discovery — the beginning of scAInce
Härtung, T. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1649155 · Cited by 63
Evans, H., & Snead, D. R. J. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01093-w · Cited by 55
Moving towards vertically integrated artificial intelligence development
Zhang, J., Budhdeo, S., William, W., Cerrato, P., Shuaib, H., Sood, H., Ashrafian, H., Halamka, J., & Teo, J. T. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00690-x · Cited by 54
Self-citationGaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows
Pati, S., Thakur, S. P., Hamamcı, İ. E., Baid, U., Baheti, B., Bhalerao, M., Güley, O., Mouchtaris, S., Lang, D., Thermos, S., et al. (2023) · Communications Engineering · doi:10.1038/s44172-023-00066-3 · Cited by 68
Pul, U., & Schwendicke, F. (2024) · Journal of Dentistry · doi:10.1016/j.jdent.2024.105104 · Cited by 58
Waldock, W. J., Zhang, J., Guni, A., Nabeel, A., Darzi, A. W., & Ashrafian, H. (2024) · Journal of Medical Internet Research · doi:10.2196/56532 · Cited by 57
Self-citationArtificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint
Bird, A., Oakden‐Rayner, L., McMaster, C., Smith, L., Zeng, M., Wechalekar, M. D., Ray, S., Proudman, S., & Palmer, L. J. (2022) · Arthritis Research & Therapy · doi:10.1186/s13075-022-02972-x · Cited by 51
Echle, A., Laleh, N. G., Schrammen, P. L., West, N. P., Trautwein, C., Brinker, T. J., Gruber, S. B., Bülow, R. D., Boor, P., Grabsch, H. I., et al. (2021) · ImmunoInformatics · doi:10.1016/j.immuno.2021.100008 · Cited by 52
Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging
Wellnhofer, E. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.890809 · Cited by 48
Perfect Match: Radiomics and Artificial Intelligence in Cardiac Imaging
Baeßler, B., Engelhardt, S., Hekalo, A., Hennemuth, A., Hüllebrand, M., Laube, A. P., Scherer, C., Tölle, M., & Wech, T. (2024) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.123.015490 · Cited by 47
Current state and prospects of artificial intelligence in allergy
van Breugel, M., Fehrmann, R. S. N., Bügel, M., Rezwan, F. I., Holloway, J. W., Nawijn, M. C., Fontanella, S., Čustović, A., & Koppelman, G. H. (2023) · Allergy · doi:10.1111/all.15849 · Cited by 46
Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis
Bojsen, J. A., Elhakim, M. T., Graumann, O., Gaist, D., Nielsen, M., Harbo, F. S. G., Krag, C. H., Sagar, M. V., Kruuse, C. R., Boesen, M., et al. (2024) · Insights into Imaging · doi:10.1186/s13244-024-01723-7 · Cited by 48
A Literature Review on Ethics for AI in Biomedical Research and Biobanking
Kargl, M., Plass, M., & Müller, H. (2022) · Yearbook of Medical Informatics · doi:10.1055/s-0042-1742516 · Cited by 30
Park, S. H., & Suh, C. H. (2024) · Korean Journal of Radiology · doi:10.3348/kjr.2024.0598 · Cited by 43
Nahm, W. J., Sohail, N., Burshtein, J. G., Goldust, M., & Tsoukas, M. M. (2025) · International Journal of Dermatology · doi:10.1111/ijd.17847 · Cited by 49
Artificial intelligence tool development: what clinicians need to know?
Chew, B.-H., & Ngiam, K. Y. (2025) · BMC Medicine · doi:10.1186/s12916-025-04076-0 · Cited by 41
AI in Computational Pathology of Cancer: Improving Diagnostic Workflows and Clinical Outcomes?
Çifçi, D., Veldhuizen, G. P., Foersch, S., & Kather, J. N. (2023) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-061521-092038 · Cited by 41
Farah, L., Davaze‐Schneider, J., Martin, T., Nguyen, P., Borget, I., & Martelli, N. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102547 · Cited by 42
Hu, W., Yii, F. S. L., Chen, R., Zhang, X., Shang, X., Kiburg, K., Woods, E., Vingrys, A. J., Zhang, L., Zhu, Z., et al. (2023) · Translational Vision Science & Technology · doi:10.1167/tvst.12.7.14 · Cited by 43
The ethics of data mining in healthcare: challenges, frameworks, and future directions
Ahmed, M. M., Okesanya, O. J., Oweidat, M. H., Othman, Z. K., Musa, S. S., & Lucero‐Prisno, D. E. (2025) · BioData Mining · doi:10.1186/s13040-025-00461-w · Cited by 41
Siontis, G. C. M., Sweda, R., Noseworthy, P. A., Friedman, P. A., Siontis, K. C., & Patel, C. J. (2021) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100466 · Cited by 44
Challenges for augmenting intelligence in cardiac imaging
Sengupta, P. P., Dey, D., Davies, R. H., Duchateau, N., & Yanamala, N. V. K. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00142-0 · Cited by 41
AI in medical education: uses of AI in construction type A MCQs
Rezigalla, A. A. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05250-3 · Cited by 36
Hassanein, F. E. A., Ahmed, Y., Maher, S., Barbary, A. E., & Abou‐Bakr, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-22979-z · Cited by 35
Applications of Artificial Intelligence and Deep Learning in Glaucoma
Chen, D. K., Ran, E. A., Tan, T. F., Ramachandran, R., Li, F., Cheung, C. Y.-L., Yousefi, S., Tham, C. C. Y., Ting, D. S. W., Zhang, X., et al. (2023) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000596 · Cited by 36
Self-citationApplied machine learning in hematopathology
Dehkharghanian, T., Mu, Y., Tizhoosh, H. R., & Campbell, C. J. V. (2023) · International Journal of Laboratory Hematology · doi:10.1111/ijlh.14110 · Cited by 33
Computer-aided anatomy recognition in intrathoracic and -abdominal surgery: a systematic review
den Boer, R. B., de Jongh, C., Huijbers, W. T. E., Jaspers, T. J. M., Pluim, J. P. W., van Hillegersberg, R., van Eijnatten, M. A. J. M., & Ruurda, J. P. H. (2022) · Surgical Endoscopy · doi:10.1007/s00464-022-09421-5 · Cited by 34
Bowness, J. S., Morse, R. M., Lewis, O. M., Lloyd, J., Laurent, D. B.-S., Bellew, B., Macfarlane, A. J. R., Pawa, A., Taylor, A., Noble, J. A., et al. (2023) · British Journal of Anaesthesia · doi:10.1016/j.bja.2023.09.023 · Cited by 32
Estévez, M., Benedum, C. M., Jiang, C., Cohen, A. B., Phadke, S., Sarkar, S., & Bozkurt, S. (2022) · Cancers · doi:10.3390/cancers14133063 · Cited by 31
Uy, H., Fielding, C., Hohlfeld, A., Ochodo, E., Opare, A., Mukonda, E. E., Minnies, D., & Engel, M. E. (2023) · PLOS Global Public Health · doi:10.1371/journal.pgph.0002160 · Cited by 32
What’s in the box? A toolbox for safe deployment of artificial intelligence in veterinary medicine
Basran, P. S., & Appleby, R. B. (2024) · Journal of the American Veterinary Medical Association · doi:10.2460/javma.24.01.0027 · Cited by 30
Novak, A., Ather, S., Gill, A., Aylward, P., Maskell, G., Cowell, G. W., Morgado, A. T. E., Duggan, T., Keevill, M., Gamble, O., et al. (2024) · Emergency Medicine Journal · doi:10.1136/emermed-2023-213620 · Cited by 31
Changes in software as a medical device based on artificial intelligence technologies
Zinchenko, V. V., Четвериков, С. Ф., Akhmad, E. S., Arzamasov, K. M., Vladzymyrskyy, A. V., Андрейченко, А. Е., & Morozov, S. P. (2022) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-022-02669-1 · Cited by 30
Automated diagnosis of autism with artificial intelligence: State of the art
Valizadeh, A., Moassefi, M., Nakhostin-Ansari, A., Some’eh, S. H., Hosseini-Asl, S. H., Torbati, M. S., Aghajani, R., Ghorbani, Z. M., Oskouie, I. M., Aghajani, F., et al. (2023) · Reviews in the Neurosciences · doi:10.1515/revneuro-2023-0050 · Cited by 29
The barriers to uptake of artificial intelligence in hepatology and how to overcome them
Clusmann, J., Balaguer‐Montero, M., Bassegoda, O., Schneider, C. V., Seraphin, T. P., Paintsil, E. K., Luedde, T., Pérez-López, R., Caldéraro, J., Gilbert, S., et al. (2025) · Journal of Hepatology · doi:10.1016/j.jhep.2025.07.003 · Cited by 30
RetractedMadrid-García, A., Barbancho, B. M., Rodríguez‐González, A., Fernández‐Gutiérrez, B., Rodríguez‐Rodríguez, L., & Menasalvas, E. (2023) · Seminars in Arthritis and Rheumatism · doi:10.1016/j.semarthrit.2023.152213 · Cited by 29
Searching for and selecting studies
Spijker, R., Dinnes, J., Glanville, J., & Eisinga, A. (2023) · Wiley eBooks · doi:10.1002/9781119756194.ch6 · Cited by 29
Ten Essential Pillars in Artificial Intelligence for University Science Education: A Scoping Review
Deroncele-Acosta, Á., Valdiviezo, O. B., Trujillo, M. D. L. Á. S., Palacios-Núñez, M. L., Rueda-Garcés, H., & Brito-Garcías, J. G. (2024) · SAGE Open · doi:10.1177/21582440241272016 · Cited by 30
QUAIDE - Quality assessment of AI preclinical studies in diagnostic endoscopy
Antonelli, G., Libânio, D., de Groof, A. J., van der Sommen, F., Mascagni, P., Sinonquel, P., Abdelrahim, M., Ahmad, O. F., Berzin, T. M., Bhandari, P., et al. (2024) · Gut · doi:10.1136/gutjnl-2024-332820 · Cited by 29
Kareemi, H., Yadav, K., Price, C. M., Bobrovitz, N., Meehan, A., Li, H., Goel, G., Masood, S., Grant, L., Ben‐Yakov, M., et al. (2025) · Academic Emergency Medicine · doi:10.1111/acem.15099 · Cited by 28
Deep learning for electrocardiogram interpretation: Bench to bedside
Schots, B. B. S., Pizarro, C. S., Arends, B. K. O., Oerlemans, M. I. F. J., Ahmetagić, D., van der Harst, P., & van Es, R. (2025) · European Journal of Clinical Investigation · doi:10.1111/eci.70002 · Cited by 30
Huang, J., Shlobin, N. A., DeCuypere, M., & Lam, S. K. (2021) · Neurosurgery · doi:10.1227/neu.0000000000001736 · Cited by 28
Zinchenko, V. V., Arzamasov, K. M., Четвериков, С. Ф., Maltsev, A. V., Петрович, Н. В., Akhmad, E. S., Е., Ш. Д., Андрейченко, А. Е., Vladzymyrskyy, A. V., & Morozov, S. P. (2022) · Современные технологии в медицине · doi:10.17691/stm2022.14.5.02 · Cited by 28
Kodenko, M. R., Vasilev, Y. A., Vladzymyrskyy, A. V., Omelyanskaya, O. V., Leonov, D. V., Blokhin, I. A., Петрович, Н. В., Kulberg, N. S., Samorodov, A. V., Mokienko, O. A., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12123197 · Cited by 28
Dreizin, D., Staziaki, P. V., Khatri, G. D., Beckmann, N. M., Feng, Z., Liang, Y., DelProposto, Z., Klug, M., Spann, J. S., Sarkar, N., et al. (2023) · Emergency Radiology · doi:10.1007/s10140-023-02120-1 · Cited by 28
Ewals, L. J. S., van der Wulp, K., van den Borne, B. E. E. M., Pluyter, J. R., Jacobs, I., Mavroeidis, D., van der Sommen, F., & Nederend, J. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12103536 · Cited by 30
Danilatou, V., Dimopoulos, D., Κωστούλας, Θ., & Douketis, J. D. (2024) · Thrombosis and Haemostasis · doi:10.1055/a-2299-4758 · Cited by 25
Data set terminology of deep learning in medicine: a historical review and recommendation
Walston, S. L., Seki, H., Takita, H., Mitsuyama, Y., Sato, S., Hagiwara, A., Ito, R., Hanaoka, S., Miki, Y., & Ueda, D. (2024) · Japanese Journal of Radiology · doi:10.1007/s11604-024-01608-1 · Cited by 27
Digital health and care: emerging from pandemic times
Peek, N. B., Sujan, M. A., & Scott, P. (2023) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2023-100861 · Cited by 30
Zhong, J., Xing, Y., Lu, J. A., Zhang, G., Mao, S., Chen, H., Yin, Q., Cen, Q., Jiang, R., Hu, Y., et al. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-02117-x · Cited by 25
Artificial intelligence chatbots in endodontic education—Concepts and potential applications
Mohammad‐Rahimi, H., Setzer, F. C., Aminoshariae, A., Dummer, P. M. H., Duncan, H. F., & Nosrat, A. (2025) · International Endodontic Journal · doi:10.1111/iej.14231 · Cited by 23
Dwyer, B., Flathers, M., Sano, A., Dempsey, A. G., Cipriani, A., Gazi, A. H., Hill, B., Gorban, C., Rodríguez, C. I., Stromeyer, C., et al. (2025) · NPP—Digital Psychiatry and Neuroscience · doi:10.1038/s44277-025-00049-6 · Cited by 31
Self-citationSrisuwananukorn, A., Salama, M. E., & Pearson, A. T. (2023) · Haematologica · doi:10.3324/haematol.2021.280209 · Cited by 22
Image analysis of cutaneous melanoma histology: a systematic review and meta-analysis
Clarke, E. L., Wade, R. G., Magee, D. R., Newton‐Bishop, J., & Treanor, D. E. (2023) · Scientific Reports · doi:10.1038/s41598-023-31526-7 · Cited by 21
Self-citationAngelantonio, E. D., Pennells, L. A. M., Abdelhamid, M., Aboyans, V., Asteggiano, R., Čelutkienė, J., Grobbee, D. E., Iung, B., Jüni, P., McEvoy, J. W., et al. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf016 · Cited by 21
Park, J. S., Park, S.-Y., Moon, J. W., Kim, K., & Suh, D. I. (2025) · Journal of Medical Internet Research · doi:10.2196/66491 · Cited by 23
Wang, X., Wang, Q., Ding, G., Wang, J., Tang, Y., & Feng, Y. (2025) · iScience · doi:10.1016/j.isci.2025.114082 · Cited by 21
Dubois, C., Eigen, D., Delmas, E., Einfalt, M., Lemaçon, C., Berteloot, L., Bossuyt, P. M., Drummond, D., Scherdel, P., Simon, F., et al. (2024) · BMJ · doi:10.1136/bmj-2023-076703 · Cited by 21
Self-citationPacholec, C., Flatland, B., Xie, H., & Zimmerman, K. (2024) · Veterinary Clinical Pathology · doi:10.1111/vcp.13401 · Cited by 19
Deep Learning for Detecting Dental Plaque and Gingivitis From Oral Photographs: A Systematic Review
Moharrami, M., Vahab, E., Bagherianlemraski, M., Hemmati, G., Singhal, S., Quiñonez, C., Schwendicke, F., & Glogauer, M. (2025) · Community Dentistry And Oral Epidemiology · doi:10.1111/cdoe.70001 · Cited by 20
Machine Learning for Multi-Omics Characterization of Blood Cancers: A Systematic Review
Alhamrani, S. Q., Ball, G., El-Sherif, A. A., Ahmed, S. S., Mousa, N. O., Alghorayed, S. A., Alatawi, N. A., Ali, A. M., Alqahtani, F. A., & Gabre, R. M. (2025) · Cells · doi:10.3390/cells14171385 · Cited by 20
King, H., Wright, J. M., Treanor, D. E., Williams, B. J., & Randell, R. (2022) · Journal of Medical Internet Research · doi:10.2196/38039 · Cited by 20
Self-citation
Metadata
- Authors
- 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., Pearson-Stuttard, J., Harling, L., McInnes, M., Rifai, N., Tomasev, N., Normahani, P., Whiting, P., Aggarwal, R., Vollmer, S., Markar, S. R., Panch, T., & Liu, X.
- Affiliations
- Imperial College London (GB)
- American Medical Association (US)
- Google Health, Pal Alto, California, USA
- DeepMind Technologies Ltd, London, UK
- Utrecht University (NL)
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
- Ottawa Hospital Research Institute (CA)
- Amsterdam University Medical Centers (NL)
- Google (United Kingdom) (GB)
- University Hospitals Birmingham NHS Foundation Trust (GB)
- The Alan Turing Institute (GB)
- Singapore National Eye Center (SG)
- University of Leeds (GB)
- Optum, Paddington, London, UK
- Google DeepMind (United Kingdom) (GB)
- School of Public Health, Imperial College London, London, UK
- University of Ottawa (CA)
- Harvard University (US)
- University of Bristol (GB)
- Corresponding author
- Ashrafian, Hutan
- ORCID
- Sounderajah, Viknesh
- Ashrafian, Hutan
- Shetty, Shravya
- Hooft, Lotty
- Moons, Karel
- Collins, Gary
- Moher, David
- Bossuyt, Patrick M
- Darzi, Ara
- Karthikesalingam, Alan
- Denniston, Alastair K
- Mateen, Bilal Akhter
- Ting, Daniel
- Treanor, Darren
- King, Dominic
- Greaves, Felix
- Godwin, Jonathan
- Harling, Leanne
- McInnes, Matthew
- Rifai, Nader
- Normahani, Pasha
- Whiting, Penny
- Aggarwal, Ravi
- Vollmer, Sebastian
- Markar, Sheraz R
- Panch, Trishan
- Liu, Xiaoxuan
- Venue
- BMJ Open
- Publisher
- BMJ
- Published
- 2021-06
- Volume
- 11
- Issue
- 6
- Pages
- e047709
- ISSN
- 2044-6055 (ISSN-L)
- PMC
- PMC8240576
- OpenAlex
- W3173393692
- Cited by
- 333
- References
- 18
- Citations by year
- 2026: 79 · 2025: 76 · 2024: 82 · 2023: 57 · 2022: 32 · 2021: 7
- FWCI
- 25.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMJ Open (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion, other-oa)
- University of Birmingham Research Portal (University of Birmingham) (publishedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- Funders
- NIHR Applied Research Collaboration (No grant number)
- National Pathology Imaging Co-operative (No grant number)
- Industrial Strategy Challenge Fund, UK Research and Innovation (No grant number)
- NIHR Imperial Biomedical Research Centre (No grant number)
- Cancer Research UK Programme Grant (C49297/A27294)
- NIHR Oxford Biomedical Research Centre (No grant number)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Sociology and Political Science › Delphi Technique in Research
- Keywords
- reporting guidelines, artificial intelligence, diagnostic accuracy, Delphi consensus, transparency in research reporting
- MeSH terms
- Artificial Intelligence, Diagnostic Tests, Routine, Humans, London, Research Design, Research Report
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
