CoChat
Sign Up
Cited by 41Artificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareArtificial Intelligence Applications

Artificial intelligence tool development: what clinicians need to know?

Open access
Chew, Boon-How, Ngiam, Kee Yuan·2025·BMC Medicine
DOI 10.1186/s12916-025-04076-0PMC PMC12023651
Open this paper in CoChat — free accountOpen PDF

Abstract

Digital medicine and smart healthcare will not be realised without the cognizant participation of clinicians. Artificial intelligence (AI) today primarily involves computers or machines designed to simulate aspects of human intelligence using mathematically designed neural networks, although early AI systems relied on a variety of non-neural network techniques. With the increased complexity of the neural layers, deep machine learning (ML) can self-learn and augment many human tasks that require decision-making on the basis of multiple sources of data. Clinicians are important stakeholders in the use of AI and ML tools. The review questions are as follows: What is the typical process of AI tool development in the full cycle? What are the important concepts and technical aspects of each step? This review synthesises a targeted literature review and reports and summarises online structured materials to present a succinct explanation of the whole development process of AI tools. The development of AI tools in healthcare involves a series of cyclical processes: (1) identifying clinical problems suitable for AI solutions, (2) forming project teams or collaborating with experts, (3) organising and curating relevant data, (4) establishing robust physical and virtual infrastructure, and computer systems' architecture that support subsequent stages, (5) exploring AI neural networks on open access platforms before making a new decision, (6) validating AI/ML models, (7) registration, (8) clinical deployment and continuous performance monitoring and (9) improving the AI ecosystem ensures its adaptability to evolving clinical needs. A sound understanding of this would help clinicians appreciate the development of AI tools and engage in codesigning, evaluating and monitoring the tools. This would facilitate broader use and closer regulation of AI/ML tools in healthcare settings.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s12916-025-04076-0

Cites

25 of 25 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Strong development or vendor-reported performance does not necessarily translate into reliable clinical utility in practice.

    “These concerns are reinforced by empirical evidence showing that strong development or vendor-reported performance does not necessarily translate into reliable clinical utility in practice [6,7].”

    From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [6]

    The Discussion (page unavailable) says that some AI models achieve expert-level accuracy but that weak study designs can overestimate performance claims; it calls for comparative evaluation to establish clinical utility. The AI/ML model validation section likewise distinguishes development performance from real-world clinical impact. These passages support the general caution, but they do not provide empirical evidence about vendor-reported performance specifically. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • The underlying evidence for the socio-technical framing of robustness was heterogeneous and often more conceptual or methodological than operational.

    “although the underlying evidence was heterogeneous and often more conceptual or methodological than operational [6,19,29,31,32,37,45,46].”

    From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [6]

    The Background (page unavailable) describes this paper as a review of AI-tool development and discusses evaluation and reporting frameworks, supporting its characterization as methodological rather than an operational study. The paper does not assess whether the broader body of evidence cited in the sentence was heterogeneous or *often* conceptual or methodological. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • AI, data science, and digital health are actively transforming medical education today.

    “The merging of artificial intelligence (AI), data science, and digital health is no longer just a future idea but an active force transforming medical education today.”

    From Institutionalizing convergence education for medical artificial intelligence (2025) · cites this paper as [2]

    The Background (page unavailable) describes accelerating AI use in clinical care and says clinicians educated about digital health technologies can contribute to their development. The section on clinical deployment calls for training clinicians to use AI tools. Neither passage establishes that AI, data science, and digital health are already transforming medical education, rather than healthcare practice and clinician training. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 75

  1. [1]Elenko E, Underwood L, Zohar D. Defining digital medicine. Nat Biotechnol. 2015;33:456–61. 25965750 · DOI
  2. [2]Tian S, Yang W, Grange JML, Wang P, Huang W, Ye Z. Smart healthcare: making medical care more intelligent. Glob Health J. 2019;3:62–5. · DOI
  3. [3]Adler-Milstein J, Aggarwal N, Ahmed M, Castner J, Evans BJ, Gonzalez AA, et al. Meeting the moment: addressing barriers and facilitating clinical adoption of artificial intelligence in medical diagnosis. NAM Perspect. 2022:10.31478/202209c. PMC9875857 36713769 · DOI
  4. [4]Rosen R. How is technology changing clinician-patient relationships? BMJ. 2024;384:q574. 38458641
  5. [5]Sauerbrei A, Kerasidou A, Lucivero F, Hallowell N. The impact of artificial intelligence on the person-centred, doctor-patient relationship: some problems and solutions. BMC Med Inform Decis Mak. 2023;23:73. 37081503 PMC10116477 · DOI
  6. [6]Trinkley KE, An R, Maw AM, Glasgow RE, Brownson RC. Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions. Implement Sci. 2024;19:17. 38383393 PMC10880216 · DOI
  7. [8]Haug CJ, Drazen JM. Artificial Intelligence and Machine Learning in Clinical Medicine, 2023. N Engl J Med. 2023;388:1201–8. 36988595 · DOI · Paper page
  8. [10]Muehlematter UJ, Bluethgen C, Vokinger KN. FDA-cleared artificial intelligence and machine learning-based medical devices and their 510(k) predicate networks. Lancet Digit Health. 2023;5:e618–26. 37625896 · DOI
  9. [11]Mehta N, Pandit A, Shukla S. Transforming healthcare with big data analytics and artificial intelligence: A systematic mapping study. J Biomed Inform. 2019;100: 103311. 31629922 · DOI
  10. [12]Bays HE, Fitch A, Cuda S, Gonsahn-Bollie S, Rickey E, Hablutzel J, et al. Artificial intelligence and obesity management: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023. Obes Pillars. 2023;6: 100065. 37990659 PMC10662105 · DOI
  11. [13]Mohaideen K, Negi A, Verma DK, Kumar N, Sennimalai K, Negi A. Applications of artificial intelligence and machine learning in orthognathic surgery: A scoping review. J Stomatol Oral Maxillofac Surg. 2022;123:e962–72. 35803558 · DOI
  12. [14]Yin J, Ngiam KY, Teo HH. Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review. J Med Internet Res. 2021;23: e25759. 33885365 PMC8103304 · DOI · Paper page
  13. [15]Ghaddaripouri K, Ghaddaripouri M, Mousavi AS, Mousavi Baigi SF, Rezaei Sarsari M, Dahmardeh Kemmak F, et al. The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review. Health Sci Rep. 2024;7: e1893. 38357491 PMC10865276 · DOI
  14. [16]Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 2019;20:e262–73. 31044724 · DOI · Paper page
  15. [17]Lysaght T, Lim HY, Xafis V, Ngiam KY. AI-Assisted Decision-making in Healthcare: The Application of an Ethics Framework for Big Data in Health and Research. Asian Bioeth Rev. 2019;11:299–314. 33717318 PMC7747260 · DOI · Paper page
  16. [19]Shah P, Kendall F, Khozin S, Goosen R, Hu J, Laramie J, et al. Artificial intelligence and machine learning in clinical development: a translational perspective. Npj Digit Med. 2019;2:69. 31372505 PMC6659652 · DOI
  17. [20]Bach TA, Kristiansen JK, Babic A, Jacovi A. Unpacking Human-AI Interaction in Safety-Critical Industries: A Systematic Literature Review. 2023. 10.48550/ARXIV.2310.03392. · DOI
  18. [21]Fehr J, Citro B, Malpani R, Lippert C, Madai VI. A trustworthy AI reality-check: the lack of transparency of artificial intelligence products in healthcare. Front Digit Health. 2024;6:1267290. 38455991 PMC10919164 · DOI
  19. [22]Yu K-H, Kohane IS. Framing the challenges of artificial intelligence in medicine. BMJ Qual Saf. 2019;28:238–41. 30291179 · DOI · Paper page
  20. [23]Adler-Milstein J, Redelmeier DA, Wachter RM. The Limits of Clinician Vigilance as an AI Safety Bulwark. JAMA. 2024. 10.1001/jama.2024.3620. 38483397 · DOI
  21. [25]Shaffer VA, Probst CA, Merkle EC, Arkes HR, Medow MA. Why Do Patients Derogate Physicians Who Use a Computer-Based Diagnostic Support System? Med Decis Making. 2013;33:108–18. 22820049 · DOI · Paper page
  22. [26]Miller A, Moon B, Anders S, Walden R, Brown S, Montella D. Integrating computerized clinical decision support systems into clinical work: A meta-synthesis of qualitative research. Int J Med Inf. 2015;84:1009–18. 26391601 · DOI · Paper page
  23. [29]Knop M, Weber S, Mueller M, Niehaves B. Human Factors and Technological Characteristics Influencing the Interaction of Medical Professionals With Artificial Intelligence-Enabled Clinical Decision Support Systems: Literature Review. JMIR Hum Factors. 2022;9: e28639. 35323118 PMC8990344 · DOI · Paper page
  24. [31]Hassan N, Slight R, Morgan G, Bates DW, Gallier S, Sapey E, et al. Road map for clinicians to develop and evaluate AI predictive models to inform clinical decision-making. BMJ Health Care Inform Online. 2023;30: e100784. PMC10414079 37558245 · DOI
  25. [32]Kwong JCC, Khondker A, Lajkosz K, McDermott MBA, Frigola XB, McCradden MD, et al. APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support. JAMA Netw Open. 2023;6: e2335377. 37747733 PMC10520738 · DOI
  26. [33]Bakker L, Aarts J, Uyl-de Groot C, Redekop K. How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care. BMC Med Inform Decis Mak. 2021;21:336. 34844594 PMC8628451 · DOI
  27. [34]Committee on Human-System Integration Research Topics for the 711th Human Performance Wing of the Air Force Research Laboratory, Board on Human-Systems Integration, Division of Behavioral and Social Sciences and Education, National Academies of Sciences, Engineering, and Medicine. Human-AI Teaming: State-of-the-Art and Research Needs. Washington, D.C.: National Academies Press; 2022.
  28. [35]Tan M, Lee H, Wang D, Subramonyam H. Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education. 2023. 10.48550/ARXIV.2311.05792. · DOI
  29. [38]Kotecha D, Asselbergs FW, Achenbach S, Anker SD, Atar D, Baigent C, et al. CODE-EHR best-practice framework for the use of structured electronic health-care records in clinical research. Lancet Digit Health. 2022;4:e757–64. 36050271 · DOI
  30. [41]Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ. 2020;370:m3164. PMC7490784 32909959
  31. [42]Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350 jan07 4:g7594–g7594. 25569120
  32. [48]Hernandez-Boussard T, Bozkurt S, Ioannidis JPA, Shah NH. MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care. J Am Med Inform Assoc. 2020;27:2011–5. 32594179 PMC7727333 · DOI · Paper page
  33. [50]Hawksworth C, Elvidge J, Knies S, Zemplenyi A, Petykó Z, Siirtola P, et al. Protocol for the development of an artificial intelligence extension to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022. medRxiv. 2023. 10.1101/2023.05.31.23290788. · DOI
  34. [51]Elvidge J, Hawksworth C, Avşar TS, Zemplenyi A, Chalkidou A, Petrou S, et al. Consolidated Health Economic Evaluation Reporting Standards for Interventions that use Artificial Intelligence (CHEERS-AI). Value Health. 2024;:S1098301524023660. PMC11343728 38795956
  35. [52]Bilbro NA, Hirst A, Paez A, Vasey B, Pufulete M, Sedrakyan A, et al. The IDEAL Reporting Guidelines: A Delphi Consensus Statement Stage Specific Recommendations for Reporting the Evaluation of Surgical Innovation. Ann Surg. 2021;273:82–5. 32649459 · DOI
  36. [53]McCulloch P, Altman DG, Campbell WB, Flum DR, Glasziou P, Marshall JC, et al. No surgical innovation without evaluation: the IDEAL recommendations. The Lancet. 2009;374:1105–12. 19782876 · DOI
  37. [54]Marcus HJ, Bennett A, Chari A, Day T, Hirst A, Hughes-Hallett A, et al. IDEAL-D Framework for Device Innovation: A Consensus Statement on the Preclinical Stage. Ann Surg. 2022;275:73–9. 33856386 PMC8683254 · DOI
  38. [59]Whicher D, Rapp T. The Value of Artificial Intelligence for Healthcare Decision Making—Lessons Learned. Value Health. 2022;25:328–30. 35227442 · DOI
  39. [60]Simon GJ, Aliferis C, editors. Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls. Cham: Springer International Publishing; 2024. 39836790
  40. [61]Das S, Nayak SP, Sahoo B, Nayak SC. Machine Learning in Healthcare Analytics: A State-of-the-Art Review. Arch Comput Methods Eng. 2024. 10.1007/s11831-024-10098-3. · DOI
  41. [62]Mello MM, Guha N. Understanding Liability Risk from Using Health Care Artificial Intelligence Tools. N Engl J Med. 2024;390:271–8. 38231630 · DOI · Paper page
  42. [63]Markowetz F. All models are wrong and yours are useless: making clinical prediction models impactful for patients. Npj Precis Oncol. 2024;8:54. 38418530 PMC10901807 · DOI
  43. [75]Asilomar AI Principles. Open Letters. 2017. https://futureoflife.org/open-letter/ai-principles/. Accessed 12 Oct 2024.
  44. [77]Prainsack B, Forgó N. New AI regulation in the EU seeks to reduce risk without assessing public benefit. Nat Med. 2024. 10.1038/s41591-024-02874-2. 38499661 · DOI
  45. [79]Cohen IG, Evgeniou T, Gerke S, Minssen T. The European artificial intelligence strategy: implications and challenges for digital health. Lancet Digit Health. 2020;2:e376–9. 33328096 · DOI
  46. [82]Klonoff DC. The Current Status of mHealth for Diabetes: Will it Be the Next Big Thing? J Diabetes Sci Technol. 2013;7:749–58. 23759409 PMC3869144 · DOI
  47. [83]Zeitoun J-D, Ravaud P. Artificial intelligence in health care: value for whom? Lancet Digit Health. 2020;2:e338–9. 33328093 · DOI
  48. [87]Chua CE, Lee Ying Clara N, Furqan MS, Lee Wai Kit J, Makmur A, Tham YC, et al. Integration of customised LLM for discharge summary generation in real-world clinical settings: a pilot study on RUSSELL GPT. Lancet Reg Health - West Pac. 2024;51:101211. PMC11776078 39881943
  49. [88]Pantuck AJ, Lee D, Kee T, Wang P, Lakhotia S, Silverman MH, et al. Modulating BET Bromodomain Inhibitor ZEN‐3694 and Enzalutamide Combination Dosing in a Metastatic Prostate Cancer Patient Using CURATE.AI, an Artificial Intelligence Platform. Adv Ther. 2018;1:1800104.
  50. [90]Blasiak A, Tan LWJ, Chong LM, Tadeo X, Truong ATL, Senthil Kumar K, et al. Personalized dose selection for the first Waldenström macroglobulinemia patient on the PRECISE CURATE.AI trial. Npj Digit Med. 2024;7:223. PMC11350179 39191913
  51. [91]Zarrinpar A, Lee D-K, Silva A, Datta N, Kee T, Eriksen C, et al. Individualizing liver transplant immunosuppression using a phenotypic personalized medicine platform. Sci Transl Med. 2016;8. 27053773
  52. [92]Seyhan AA. Lost in translation: the valley of death across preclinical and clinical divide – identification of problems and overcoming obstacles. Transl Med Commun. 2019;4(1):18. 10.1186/s41231-019-0050-7. · DOI
  53. [93]Nagendran M, Chen Y, Lovejoy CA, Gordon AC, Komorowski M, Harvey H, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ. 2020;368:m689. 10.1136/bmj.m689. PMC7190037 32213531 · DOI · Paper page
  54. [94]Rajagopal A, Ayanian S, Ryu AJ, Qian R, Legler SR, Peeler EA, et al. Machine Learning Operations in Health Care: A Scoping Review. Mayo Clin Proc Digit Health. 2024;2:421–37. 40206123 PMC11975983 · DOI · Paper page
  55. [95]Ahmed MI, Spooner B, Isherwood J, Lane M, Orrock E, Dennison A. A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare. Cureus. 2023. 10.7759/cureus.46454. 37927664 PMC10623210 · DOI · Paper page
  56. [96]Teo ZL, Kwee A, Lim JC, Lam CS, Ho D, Maurer-Stroh S, et al. Artificial intelligence innovation in healthcare: Relevance of reporting guidelines for clinical translation from bench to bedside. Ann Acad Med Singapore. 2023;52:199–212. 38904533 · DOI
  57. [97]Ayorinde A, Mensah DO, Walsh J, Ghosh I, Ibrahim SA, Hogg J, et al. Health Care Professionals’ Experience of Using AI: Systematic Review With Narrative Synthesis. J Med Internet Res. 2024;26: e55766. 39476382 PMC11561443 · DOI · Paper page
  58. [98]Celi LA, Cellini J, Charpignon M-L, Dee EC, Dernoncourt F, Eber R, et al. Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review. PLOS Digit Health. 2022;1: e0000022. 36812532 PMC9931338 · DOI · Paper page
  59. [99]Subbaswamy A, Saria S. From development to deployment: dataset shift, causality, and shift-stable models in health AI. Biostatistics. 2020;21(2):345–52. 10.1093/biostatistics/kxz041. 31742354 · DOI · Paper page
  60. [100]Ganapathi S, Palmer J, Alderman JE, Calvert M, Espinoza C, Gath J, et al. Tackling bias in AI health datasets through the STANDING Together initiative. Nat Med. 2022;28:2232–3. 36163296 · DOI · Paper page
  61. [101]Ratwani RM, Sutton K, Galarraga JE. Addressing AI Algorithmic Bias in Health Care. JAMA. 2024;332:1051. 39230911 · DOI · Paper page
  62. [103]Ghebrehiwet I, Zaki N, Damseh R, Mohamad MS. Revolutionizing personalized medicine with generative AI: a systematic review. Artif Intell Rev. 2024;57:128. · DOI
  63. [104]Ibrahim M, Khalil YA, Amirrajab S, Sun C, Breeuwer M, Pluim J, et al. Generative AI for synthetic data across multiple medical modalities: a systematic review of recent developments and challenges. arXiv. 2024. 10.48550/arXiv.2407.00116. 40023073 · DOI
  64. [105]Takita H, Kabata D, Walston SL, Tatekawa H, Saito K, Tsujimoto Y, et al. Diagnostic performance comparison between generative AI and physicians: a systematic review and meta-analysis. medRxiv. 2024. 10.1101/2024.01.20.24301563. PMC11929846 40121370 · DOI
  65. [106]Beam AL, Manrai AK, Ghassemi M. Challenges to the Reproducibility of Machine Learning Models in Health Care. JAMA. 2020;323:305. 31904799 PMC7335677 · DOI · Paper page
  66. [107]Azad TD, Ehresman J, Ahmed AK, Staartjes VE, Lubelski D, Stienen MN, et al. Fostering reproducibility and generalizability in machine learning for clinical prediction modeling in spine surgery. Spine J. 2021;21:1610–6. 33065274 · DOI
  67. [108]Alberto IRI, Alberto NRI, Altinel Y, Blacker S, Binotti WW, Celi LA, et al. A scientometric analysis of fairness in health AI literature. PLOS Glob Public Health. 2024;4: e0002513. 38241250 PMC10798451 · DOI
  68. [109]Yang R, Nair SV, Ke Y, D’Agostino D, Liu M, Ning Y, et al. Disparities in clinical studies of AI enabled applications from a global perspective. Npj Digit Med. 2024;7:209. 39127820 PMC11316833 · DOI
  69. [110]Serra-Burriel M, Locher L, Development VKN, Pipeline and Geographic Representation of trials for artificial intelligence, machine learning-enabled medical devices (2010 to 2023). NEJM AI. 2024;1(1):AIpc2300038. 10.1056/AIpc2300038. · DOI
  70. [111]Liu M, Ning Y, Teixayavong S, Mertens M, Xu J, Ting DSW, et al. A translational perspective towards clinical AI fairness. Npj Digit Med. 2023;6:172. 37709945 PMC10502051 · DOI · Paper page
  71. [112]Susanto AP, Lyell D, Widyantoro B, Berkovsky S, Magrabi F. Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review. J Am Med Inform Assoc. 2023;30:2050–63. 37647865 PMC10654852 · DOI
  72. [114]Van De Sande D, Van Genderen ME, Smit JM, Huiskens J, Visser JJ, Veen RER, et al. Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter. BMJ Health Care Inform. 2022;29: e100495. 35185012 PMC8860016 · DOI · Paper page
  73. [117]Stiefel KM, Coggan JS. The energy challenges of artificial superintelligence. Front Artif Intell. 2023;6:1240653. 37941679 PMC10629395 · DOI
  74. [118]Jovanovic M, Mitrov G, Zdravevski E, Lameski P, Colantonio S, Kampel M, et al. Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns. J Med Internet Res. 2022;24: e36553. 36331530 PMC9675018 · DOI
  75. [119]Wolff J, Pauling J, Keck A, Baumbach J. Systematic Review of Economic Impact Studies of Artificial Intelligence in Health Care. J Med Internet Res. 2020;22: e16866. 32130134 PMC7059082 · DOI

Cited by

Sorted by citations

Authors
Chew, B.-H., & Ngiam, K. Y.
Affiliations
  • Universiti Putra Malaysia (MY)
  • National University of Singapore (SG)
Corresponding author
  • Chew, Boon-How
  • Ngiam, Kee Yuan
ORCID
Venue
BMC Medicine
Publisher
Springer Science and Business Media LLC
Published
2025-04-24
Volume
23
Issue
1
Pages
244-244
ISSN
1741-7015 (ISSN-L)
DOI
10.1186/s12916-025-04076-0
PMC
PMC12023651
OpenAlex
W4409755001
Cited by
41
References
25
Citations by year
2026: 28 · 2025: 13
FWCI
2.42
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medicine (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia) (acceptedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • College of Family Physicians of Canada
  • National University of Singapore
  • National University Health System
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
artificial intelligence in healthcare, deep learning, artificial neural networks, clinical decision support, AI model validation, AI regulation, clinical deployment, performance monitoring, clinician engagement, co-design, smart healthcare
MeSH terms
Artificial Intelligence, Humans, Neural Networks, Computer

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.

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