CoChat
Sign Up
PreprintCited by 8Artificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareArtificial Intelligence Applications

Artificial Intelligence in Relation to Accurate Information and Tasks in Gynecologic Oncology and Clinical Medicine – Dunning-Kruger Effects and Ultracrepidarianism

Open access
Pavlik, Edward J., Land Woodward, Jamie, Lawton, Frank, Swiecki-Sikora, Allison L., Ramaiah, Dharani D., Rives, Taylor A.·2025·Diagnostics
DOI 10.20944/preprints202502.0521.v1PMC PMC11941301

Preprint — published as 10.3390/diagnostics15060735

Open this paper in CoChat — free account

Abstract

We have published on the accuracy of the Google Virtual Assistant, Alexa, Siri, Cortana, Gemini and Copilot. Emerging from this published work was a focus on the ac-curacy of AI that could be determined through validations. In our work published in 2023, the accuracy of responses to a panel of 24 queries related to gynecologic oncology was low, with Google Virtual Assistant (VA) providing the most correct audible replies (18.1%), followed by Alexa (6.5%), Siri (5.5%), and Cortana (2.3%). In the months following our publication, there was explosive excitement about several generative AIs that continue to transform the landscape of information accessibility by presenting the search results in impressively engaging narratives. This type of presentation has been enabled by combining machine learning algorithms with Natural Language Processing (NLP). In 2024, we published our exploration of the generative AIs Gemini and Copilot as well as the Google Assistant in relation to how accurately they responded to the panel of 24 queries that we used in the 2023 publication. Google Gemini achieved an 87.5% accuracy rate, while the accuracy of Microsoft Copilot was 83.3%. In contrast, the Google VA’s accuracy in audible responses improved from 18% in the 2023 report to 63% in 2024. Because of our investigation in this area, we have examined the accuracy of results obtained through different AI models in this review. The landscape of the findings reviewed here surveyed 252 papers published in 2024, topically reporting on AI in medicine of which 83 articles are considered in the present review because they contain evidenced-based findings. In particular, the types of cases considered deal with AI accuracy in initial differential diagnoses, cancer treatment recommendations, board-style exams and performance in various clinical tasks. Importantly, summaries of the validation techniques used to evaluate AI findings are presented. This review focuses on those AIs that have a clinical relevancy evidenced by application and evaluation in clinical publications. This relevancy speaks to both what has been promised and what has been delivered by various AI systems. Readers will be able to understand when generative AI may be expressing views without having the necessary information (ultracrepidarianism) or is responding as if the generative AI had expert knowledge when it does not. Without an awareness that AIs may deliver inadequate or confabulated information, incorrect medical decisions and inappropriate clinical applications can result (Dunning-Kruger effect). As a result, in certain cases a generative AI might underperform and provide results which greatly overestimate any medical or clinical validity.

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 · 10.20944/preprints202502.0521.v1

Cites

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?

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

No references could be resolved yet.

Research works printed but not linked: 76

  1. [1]1. Land JM, Pavlik EJ, Ueland E, et al. Evaluation of replies to voice queries in gynecologic oncology by virtual assistants Siri, Alexa, Google, and Cortana. BioMed Informatics. 2023;3(3):553-562. https://doi.org/ 10.3390/biomedinformatics3030038 · DOI
  2. [2]2. Pavlik EJ, Ramaiah DD, Rives TA, Swiecki-Sikora AL, Land JM. Replies to Queries in Gynecologic Oncology by Bard, Bing, and the Google Assistant. BioMed Informatics . 2024;4(3):1773-1782. https://doi.org/ 10.3390/biomedinformatics4030097. · DOI
  3. [7]7. Kurzweil R. The singularity is nearer: When we merge with AI. Penguin Books; 2024. ISBN 9780399562761.
  4. [10]10. Haug C.J., & Drazen, J.M. (2023). Artificial Intelligence and Machine Learning in Clinical Medicine, 2023. New England Journal of Medicine, 388(13), 12011208. https://doi.org/10.1056/NEJMra2302038 · DOI · Paper page
  5. [11]11. Gomes B, Ashley EA. Artificial Intelligence in Molecular Medicine. N Engl J Med. 20224 Jun 29;388(26):2456-2465 . https://doi.org/ 10.1056/NEJMra2204787. PMID: 37379136. · DOI
  6. [12]12. Colasacco CJ, Born HL. A Case of Artificial Intelligence Chatbot Hallucination. JAMA Otolaryngol Head Neck Surg. 2024 Jun 1;150(6):457-458 . https://doi.org/ 10.1001/jamaoto.2024.0428. PMID: 38635259. · DOI
  7. [13]13. Kacena MA, Plotkin LI, Fehrenbacher JC. The Use of Artificial Intelligence in Writing Scientific Review Articles. Curr Osteoporos Rep. 2024 Feb;22(1):115-121 . https://doi.org/ 10.1007/s11914-023-00852-0. Epub 2024 Jan 16. PMID: 38227177; PMCID: PMC10912250. · DOI · Paper page
  8. [14]14. Omiye JA, Gui H, Rezaei SJ, Zou J, Daneshjou R. Large Language Models in Medicine: The Potentials and Pitfalls : A Narrative Review. Ann Intern Med. 2024 Feb;177(2):210-220 . https://doi.org/ 10.7326/M23-2772. Epub 2024 Jan 30. PMID: 38285984. · DOI
  9. [15]15. Kanjee Z, Crowe B, Rodman A. Accuracy of a Generative Artificial Intelligence Model in a Complex Diagnostic Challenge. JAMA. 2023;330(1):78-80 . https://doi.org/ 10.1001/jama.2023.8288 · DOI · Paper page
  10. [16]16. Rengers TA, Thiels CA, Salehinejad H. Academic Surgery in the Era of Large Language Models: A Review. JAMA Surg. 2024;159(4):445-450 . https://doi.org/ 10.1001/jamasurg.2023.6496 · DOI
  11. [17]17. Shah NH, Halamka JD, Saria S, et al. A Nationwide Network of Health AI Assurance Laboratories. JAMA. 2024;331(3):245-249 . https://doi.org/ 10.1001/jama.2023.26930 · DOI · Paper page
  12. [18]18. Li SW, Kemp MW, Logan SJS, et al. ChatGPT outscored human candidates in a virtual objective structured clinical examination in obstetrics and gynecology. Am J Obstet Gynecol . 2023;229(2):172.e1-172.e12. https://doi.org/ 10.1016/j.ajog.2023.04.020 · DOI
  13. [19]19. Anastasio MK, Peters P, Foote J, Melamed A, Modesitt SC, Musa F, Rossi E, Albright BB, Havrilesky LJ, Moss HA. The doc versus the bot: A pilot study to assess the quality and accuracy of physician and chatbot responses to clinical questions in gynecologic oncology. Gynecol Oncol Rep . 2024;55:101477. https://doi.org/ 10.1016/j.gore.2024.101477. PMID:39224817; PMCID:PMC11367046. · DOI
  14. [20]20. Fanizzi A, Arezzo F, Cormio G, Comes MC, Cazzato G, Boldrini L, Bove S, Bollino M, Kardhashi A, Silvestris E, Quarto P, Mongelli M, Naglieri E, Signorile R, Loizzi V, Massafra R. An explainable machine learning model to solid adnexal masses diagnosis based on clinical data and qualitative ultrasound indicators. Cancer Med. 2024 Jun;13(12):e7425 . https://doi.org/ 10.1002/cam4.7425. PMID: 38923847; PMCID: PMC11196372. · DOI
  15. [21]21. Liu T, Miao K, Tan G, et al. A study on automatic ORADS classification of sonograms of ovarian adnexal lesions based on deep convolutional neural networks. Ultrasound Med Biol . 2025;51(2):387-395. https://doi.org/ 10.1016/j.ultrasmedbio.2024.11.009. Epub 2024 Nov 26. PMID: 39603844. · DOI
  16. [22]22. Moro F, Ciancia M, Zace D, Vagni M, Tran HE, Giudice MT, Zoccoli SG, Mascilini F, Ciccarone F, Boldrini L, D'Antonio F, Scambia G, Testa AC. Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review. Int J Cancer. 2024 Nov 15;155(10):1832-1845. https://doi.org/ 10.1002/ijc.35092. Epub 2024 Jul 11. PMID: 38989809. · DOI
  17. [23]23. Mitchell S, Nikolopoulos M, El-Zarka A, Al-Karawi D, Al-Zaidi S, Ghai A, Gaughran JE, Sayasneh A. Artificial intelligence in ultrasound diagnoses of ovarian cancer: A systematic review and meta -analysis. Cancers (Basel) . 2024;16(2):422 . https://doi.org/ 10.3390/cancers16020422. PMID:38275863; PMCID:PMC10813993. · DOI
  18. [24]24. Feng, Y. An integrated machine learning-based model for joint diagnosis of ovarian cancer with multiple test indicators. J Ovarian Res 17, 45 (2024). https://doi.org/10.1186/s13048 -024-01365-9 · DOI
  19. [25]25. Bogaerts JM, Steenbeek MP, Bokhorst JM, et al. Assessing the impact of deep-learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes. J Pathol Clin Res. 2024 Nov;10(6):e70006 . https://doi.org/ 10.1002/2056-4538.70006. PMID: 39439213; PMCID: PMC11496567. · DOI
  20. [26]26. Bergstrom EN, et al. Deep learning artificial intelligence predicts homologous recombination deficiency and platinum response from histologic slides. J Clin Oncol . 2024;42:3550-3560. https://doi.org/ 10.1200/JCO.23.02641. · DOI · Paper page
  21. [27]27. Capasso I, Cucinella G, Wright DE, et al. Artificial intelligence model for enhancing the accuracy of transvaginal ultrasound in detecting endometrial cancer and endometrial atypical hyperplasia. Int J Gynecol Cancer. 2024 Oct 7;34(10):1547-1555 . https://doi.org/ 10.1136/ijgc-2024-005652. PMID: 39089731. · DOI
  22. [28]28. Hu L, Bell D, Antani S, et al. An observational study of deep learning and automated evaluation of cervical images for cancer screening. JNCI: Journal of the National Cancer Institute . 2019;111(9):923-932. https://doi.org/ 10.1093/jnci/djy225. · DOI
  23. [29]29. Xue Z, Novetsky AP, Einstein MH, et al. A demonstration of automated visual evaluation of cervical images taken with a smartphone camera. Int. J. Cancer. 2020; 147: 24162423. https://doi.org/10.1002/ijc.33029 · DOI
  24. [30]30. Desai KT, Befano B, Xue Z, et al. The development of 'automated visual evaluation' for cervical cancer screening: The promise and challenges in adapting deep-learning for clinical testing. Int. J. Cancer. 2022; 150(5): 741-752 . https://doi.org/ 10.1002/ijc.33879 · DOI
  25. [31]31. Parham, G.P., Egemen, D., Befano, B. et al. Validation in Zambia of a cervical screening strategy including HPV genotyping and artificial intelligence (AI) -based automated visual evaluation. Infect Agents Cancer 18, 61 (2023). https://doi.org/10.1186/s1302 7-023-00536-5 · DOI
  26. [32]32. Egemen D, Perkins RB, Cheung LC, et al. Artificial intelligence-based image analysis in clinical testing: lessons from cervical cancer screening. JNCI: J Natl Cancer Inst. 2024 Jan;116(1):26-33. https://doi.org/ 10.1093/jnci/djad202. · DOI
  27. [33]33. RiosDoria E, Wang J, Rodriguez I, et al. Artificial intelligence powered insights: Assessment of ChatGPT's treatment recommendations in gynecologic oncology. Gynecol Oncol . 2024;190(Suppl 1):S45. https://doi.org/ 10.1016/j.ygyno.2024.07.071. · DOI
  28. [34]34. Rao A, Pang M, Kim J, et al. Assessing the utility of ChatGPT throughout the entire clinical workflow: development and usability study. J Med Internet Res. 2023;25:e48659 . https://doi.org/ 10.2196/48659. · DOI · Paper page
  29. [35]35. Cabral S, Restrepo D, Kanjee Z, et al. Clinical reasoning of a generative artificial intelligence model compared with physicians. JAMA Intern Med . 2024;184(5):581-583. https://doi.org/ 10.1001/jamainternmed.2024.0295. · DOI · Paper page
  30. [36]36. Chen S, Kann BH, Foote MB, Aerts HJWL, Savova GK, Mak RH, Bitterman DS. Use of Artificial Intelligence Chatbots for Cancer Treatment Information. JAMA Oncol. 2023 Oct 1;9(10):1459-1462. https://doi.org/ 10.1001/jamaoncol.2023.2954 · DOI · Paper page
  31. [37]37. Pan A, Musheyev D, Bockelman D, Loeb S, Kabarriti AE. Assessment of Artificial Intelligence Chatbot Responses to Top Searched Queries About Cancer. JAMA Oncol. 2023;9(10):1437-1440. https://doi.org/ 10.1001/jamaoncol.2023.2947 · DOI
  32. [38]38. Janopaul-Naylor JR, Koo A, Qian DC, et al. Physician assessment of ChatGPT and Bing answers to American Cancer Society's questions to ask about your cancer. Am J Clin Oncol. 2024;47(1):17-21. https://doi.org/ 10.1097/COC.0000000000001050. · DOI
  33. [39]39. Shea YF, Lee CMY, Ip WCT, et al. Use of GPT-4 to analyze medical records of patients with extensive investigations and delayed diagnosis. JAMA Netw Open . 2023;6(8):e2325000. https://doi.org/ 10.1001/jamanetworkopen.2023.25000. · DOI
  34. [40]40. Schubert MC, Wick W, Venkataramani V. Performance of large language models on a neurology boardstyle examination. JAMA Netw Open . 2023;6(12):e2346721. https://doi.org/ 10.1001/jamanetworkopen.2023.46721. · DOI · Paper page
  35. [41]41. Wu S, Koo M, Blum L, et al. Benchmarking open-source large language models, GPT-4 and Claude 2 on multiple-choice questions in nephrology. NEJM AI. 2024;1(2):doi:10.1056/AIdbp2300092. · DOI
  36. [42]42. Katz U, Cohen E, Shachar E, et al. GPT versus resident physicians -a benchmark based on official board scores. NEJM AI . 2024;1(5) . https://doi.org/ 10.1056/AIdbp2300192. · DOI · Paper page
  37. [43]43. Jabbour S, Fouhey D, Shepard S, et al. Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Clinical Vignette Survey Study. JAMA. 2023;330(23):2275 -2284. https://doi.org/ 10.1001/jama.2023.22295 · DOI · Paper page
  38. [44]44. Han T, Adams LC, Bressem KK, et al. Comparative analysis of multimodal large language model performance on clinical vignette questions. JAMA . 2024;331(15):1320-1321. https://doi.org/ 10.1001/jama.2023.27861. · DOI · Paper page
  39. [45]45. Rydzewski NR, Dinakaran D, Zhao SG, et al. Comparative evaluation of LLMs in clinical oncology. NEJM AI . 2024;1(5) . https://doi.org/ 10.1056/aioa2300151. Epub 2024 Apr 16.. · DOI
  40. [46]46. Longwell JB, Hirsch I, Binder F, et al. Performance of large language models on medical oncology examination questions. JAMA Netw Open . 2024;7(6):e2417641. https://doi.org/ 10.1001/jamanetworkopen.2024.17641. · DOI · Paper page
  41. [47]47. Chen D, Huang RS, Jomy J, et al. Performance of multimodal artificial intelligence chatbots evaluated on clinical oncology cases. JAMA Netw Open . 2024;7(10):e2437711. https://doi.org/ 10.1001/jamanetworkopen.2024.37711. · DOI · Paper page
  42. [48]48. Thirunavukarasu AJ, Mahmood S, Malem A, et al. Large language models approach expert-level clinical knowledge and reasoning in ophthalmology: A head-to-head cross-sectional study. PLOS Digit Health . 2024;3(4):e0000341 . https://doi.org/ 10.1371/journal.pdig.0000341. · DOI
  43. [49]49. Liu T-L, Hetherington TC, Dharod A, et al. Does AI-powered clinical documentation enhance clinician efficiency? A longitudinal study. NEJM AI . 2024;1(12) . https://doi.org/ 10.1056/AIoa2400659. · DOI · Paper page
  44. [50]50. Tai-Seale M, Baxter SL, Vaida F, et al. AI-Generated Draft Replies Integrated Into Health Records and Physicians' Electronic Communication. JAMA Netw Open. 2024;7(4):e246565. https://doi.org/ 10.1001/jamanetworkopen.2024.6565 · DOI · Paper page
  45. [51]51. Liu TL, Hetherington TC, Stephens C, McWilliams A, Dharod A, Carroll T, Cleveland JA. AI-Powered Clinical Documentation and Clinicians' Electronic Health Record Experience: A Nonrandomized Clinical Trial. JAMA Netw Open. 2024 Sep 3;7(9):e2432460 . https://doi.org/ 10.1001/jamanetworkopen.2024.32460. PMID: 39240568; PMCID: PMC11380097. · DOI
  46. [52]52. Small WR, Wiesenfeld B, Brandfield -Harvey B, et al. Large language model-based responses to patients' in-basket messages. JAMA Netw Open . 2024;7(7):e2422399. https://doi.org/ 10.1001/jamanetworkopen.2024.22399. · DOI
  47. [53]53. Ayers JW, Poliak A, Dredze M, et al. Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Intern Med . 2023;183(6):589-596. https://doi.org/ 10.1001/jamainternmed.2023.1838. PMID:37115527; PMCID:PMC10148230. · DOI · Paper page
  48. [54]54. Hartman V, Zhang X, Poddar R, et al. Developing and evaluating large language model-generated emergency medicine handoff notes. JAMA Netw Open . 2024;7(12):e2448723. https://doi.org/ 10.1001/jamanetworkopen.2024.48723. PMID:39625719; PMCID:PMC11615705. · DOI · Paper page
  49. [55]55. . Burford KG, Itzkowitz NG, Ortega AG, Teitler JO, Rundle AG. Use of Generative AI to Identify Helmet Status Among Patients With Micromobility-Related Injuries From Unstructured Clinical Notes. JAMA Netw Open. 2024 Aug 1;7(8):e2425981 . https://doi.org/ 10.1001/jamanetworkopen.2024.25981. PMID: 39136946; PMCID: PMC11322845. · DOI · Paper page
  50. [56]56. Shah SV. Accuracy, Consistency, and Hallucination of Large Language Models When Analyzing Unstructured Clinical Notes in Electronic Medical Records. JAMA Netw Open. 2024 Aug 1;7(8):e2425953. https://doi.org/ 10.1001/jamanetworkopen.2024.25953. PMID: 39136951. · DOI · Paper page
  51. [57]57. Garcia P, Ma SP, Shah S, et al. Artificial Intelligence -Generated Draft Replies to Patient Inbox Messages. JAMA Netw Open. 2024;7(3):e243201 . https://doi.org/ 10.1001/jamanetworkopen.2024.3201 · DOI
  52. [58]58. Chen D, Parsa R, Hope A, et al. Physician and artificial intelligence chatbot responses to cancer questions from social media. JAMA Oncol . 2024;10(7):956-960 . https://doi.org/ 10.1001/jamaoncol.2024.0836. · DOI · Paper page
  53. [59]59. Yalamanchili A, Sengupta B, Song J, et al. Quality of large language model responses to radiation oncology patient care questions. JAMA Netw Open . 2024;7(4):e244630. https://doi.org/ 10.1001/jamanetworkopen.2024.4630. · DOI
  54. [60]60. Yu F, Moehring A, Banerjee O, et al. Heterogeneity and predictors of the effects of AI assistance on radiologists. Nat Med . 2024;30:837-849 . https://doi.org/ 10.1038/s41591-024-02850-w. · DOI · Paper page
  55. [61]61. Tu T, Azizi S, Driess D, et al. Towards generalist biomedical AI. NEJM AI . 2024;1(3). https://doi.org/ 10.1056/AIoa2300138. · DOI · Paper page
  56. [62]62. Goh E, Gallo R, Hom J, et al. Large language model influence on diagnostic reasoning: A randomized clinical trial. JAMA Netw Open . 2024;7(10):e2440969 . https://doi.org/ 10.1001/jamanetworkopen.2024.40969. · DOI · Paper page
  57. [63]63. Bhargava A, López-Espina C, Schmalz L, et al. FDA-authorized AI/ML tool for sepsis prediction: development and validation. NEJM AI . 2024;1(12) . https://doi.org/ 10.1056/AIoa2400867. · DOI
  58. [64]64. Hoang YN, Chen YL, Ho DK, et al. Consistency and accuracy of artificial intelligence for providing nutritional information. JAMA Netw Open . 2023;6(12):e2350367. https://doi.org/ 10.1001/jamanetworkopen.2023.50367. · DOI
  59. [65]65. Carnevali, D., Zhong, L., GonzálezAlmela, E. et al. A deep learning method that identifies cellular heterogeneity using nanoscale nuclear features. Nat Mach Intell 6, 1021-1033 (2024). https://doi.org/10.1038/s42256 -024-00883-x · DOI
  60. [66]66. van der Laak J, Litjens G, Ciompi F. Deep learning in histopathology: the path to the clinic. Nat Med. 2021 May;27(5):775-784 . https://doi.org/ 10.1038/s41591-021-01343-4. Epub 2021 May 14. PMID: 33990804. · DOI · Paper page
  61. [67]67. Shmatko A, Ghaffari Laleh N, Gerstung M, Kather JN. Artificial intelligence in histopathology: enhancing cancer research and clinical oncology. Nat Cancer. 2022 Sep;3(9):1026-1038 . https://doi.org/ 10.1038/s43018022-00436-4. Epub 2022 Sep 22. PMID: 36138135. · DOI
  62. [68]68. ReisFilho JS, Kather JN. Overcoming the challenges to implementation of artificial intelligence in pathology. JNCI J Natl Cancer Inst . 2023;115(6):608-612 . https://doi.org/ 10.1093/jnci/djad048. · DOI · Paper page
  63. [69]69. Steimetz E, Minkowitz J, Gabutan EC, et al. Use of artificial intelligence chatbots in interpretation of pathology reports. JAMA Netw Open . 2024;7(5):e2412767. https://doi.org/ 10.1001/jamanetworkopen.2024.12767. · DOI
  64. [70]70. Wang, X., Zhao, J., Marostica, E. et al. A pathology foundation model for cancer diagnosis and prognosis prediction. Nature 634, 970978 (2024). https://doi.org/10.1038/s41586 -024-07894-z · DOI
  65. [71]71. Gjesvik J, Moshina N, Lee CI, Miglioretti DL, Hofvind S. Artificial Intelligence Algorithm for Subclinical Breast Cancer Detection. JAMA Netw Open. 2024 Oct 1;7(10):e2437402. https://doi.org/ 10.1001/jamanetworkopen.2024.37402. PMID: 39361281; PMCID: PMC11450515. · DOI
  66. [72]72. Lee JJ, Zepeda A, Arbour G, et al. Automated identification of breast cancer relapse in computed tomography reports using natural language processing. JCO Clin Cancer Inform . 2024;8:e2400107. https://doi.org/ 10.1200/CCI.24.00107. · DOI
  67. [73]73. Mihalache A, Huang RS, Popovic MM, Patil NS, Pandya BU, Shor R, Pereira A, Kwok JM, Yan P, Wong DT, Kertes PJ, Muni RH. Accuracy of an Artificial Intelligence Chatbot's Interpretation of Clinical Ophthalmic Images. JAMA Ophthalmol. 2024 Apr 1;142(4):321-326. https://doi.org/ 10.1001/jamaophthalmol.2024.0017. PMID: 38421670; PMCID: PMC10905373. · DOI
  68. [74]74. Chung P, Fong CT, Walters AM, et al. Large language model capabilities in perioperative risk prediction and prognostication. JAMA Surg . 2024;159(8):928-937 . https://doi.org/ 10.1001/jamasurg.2024.1621. · DOI
  69. [75]75. Xiao M, Molina KC, Aggarwal NR, et al. A Machine Learning Method for Allocating Scarce COVID-19 Monoclonal Antibodies. JAMA Health Forum. 2024;5(9):e242884. https://doi.org/ 10.1001/jamahealthforum.2024.2884 · DOI
  70. [76]76. Sunami K, Naito Y, Saigusa Y, et al. A Learning Program for Treatment Recommendations by Molecular Tumor Boards and Artificial Intelligence. JAMA Oncol. 2024;10(1):95 -102. https://doi.org/ 10.1001/jamaoncol.2023.5120 · DOI
  71. [77]77. Williams CYK, Zack T, Miao BY, et al. Use of a large language model to assess clinical acuity of adults in the emergency department. JAMA Netw Open . 2024;7(5):e248895. https://doi.org/ 10.1001/jamanetworkopen.2024.8895. · DOI · Paper page
  72. [78]78. Andriola C, Ellis RP, Siracuse JJ, et al. A Novel Machine Learning Algorithm for Creating Risk-Adjusted Payment Formulas. JAMA Health Forum. 2024;5(4):e240625. https://doi.org/ 10.1001/jamahealthforum.2024.0625 · DOI
  73. [79]79. Soroush A, Glicksberg BS, Zimlichman E, et al. Large language models are poor medical coders benchmarking of medical code querying. NEJM AI . 2024;1(5) . https://doi.org/ 10.1056/AIdbp2300040. · DOI · Paper page
  74. [80]80. Hantel A, Walsh TP, Marron JM, et al. Perspectives of oncologists on the ethical implications of using artificial intelligence for cancer care. JAMA Netw Open . 2024;7(3):e244077. https://doi.org/ 10.1001/jamanetworkopen.2024.4077. PMID:38546644; PMCID:PMC10979310. · DOI
  75. [81]81. Platt J, Nong P, Carmona G, Kardia S. Attitudes toward notification of use of artificial intelligence in health care. JAMA Netw Open . 2024;7(12):e2450102 . https://doi.org/ 10.1001/jamanetworkopen.2024.50102. · DOI
  76. [84]84. Menz BD, Modi ND, Sorich MJ, Hopkins AM. Health disinformation use case highlighting the urgent need for artificial intelligence vigilance: Weapons of mass disinformation. JAMA Intern Med . 2024;184(1):92-96. https://doi.org/ 10.1001/jamainternmed.2023.5947. PMID:37955873. Disclaimer/Publisher's Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. · DOI · Paper page

Cited by

Sorted by citations

Authors
Pavlik, E. J., Land Woodward, J., Lawton, F., Swiecki-Sikora, A. L., Ramaiah, D. D., & Rives, T. A.
Affiliations
University of Kentucky (US)
Corresponding author
Pavlik, Edward J.
ORCID
Venue
Diagnostics
Publisher
MDPI AG
Published
2025-02-07
Volume
15
Issue
6
Pages
735-735
ISSN
2075-4418 (ISSN-L)
DOI
10.20944/preprints202502.0521.v1
PMC
PMC11941301
OpenAlex
W4408526199
Cited by
8
References
0
Citations by year
2026: 5 · 2025: 3
FWCI
0.47
Type
journal article
Language
en
Open access
Licence
Locations
  • Diagnostics (publishedVersion, cc-by)
  • Preprints.org (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
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, generative AI, Dunning-Kruger effect, AI accuracy, differential diagnosis, cancer treatment recommendations, clinical imaging, gynecologic oncology, clinical decision-making

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