U.S. flag

An official website of the Department of Health and Human Services

SHARE:

linkedInFacebookXEmail AHRQPrintShare
Diagnostic Safety and Quality

Understanding the AI Wave: Foundational Knowledge for Improving Diagnosis and Beyond

References

  1. Lawal L. Why strategy beats speed in introducing AI for healthcare. World Economic Forum March 20, 2025. Accessed May 15, 2025. https://www.weforum.org/stories/2025/03/ai-healthcare-strategy-speed/.
  2. Bodnari A, Travis J. Scaling enterprise AI in healthcare: the role of governance in risk mitigation frameworks. npj Digit Med 2025;8(1):272. doi:10.1038/s41746-025-01700-4.
  3. Sagona M, Dai T, Macis M, Darden M. Trust in AI-assisted health systems and AI’s trust in humans. npj Health Syst 2025;2(1):10. doi:10.1038/s44401-025-00016-5.
  4. Adler-Milstein J, Aggarwal N, Ahmed M, et al. Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis. NAM Perspect 2022;22(9). doi:10.31478/202209c.
  5. Hassan M, Kushniruk A, Borycki E. Barriers to and Facilitators of Artificial Intelligence Adoption in Health Care: Scoping Review. JMIR Hum Factors 2024;11:e48633. doi:10.2196/48633.
  6. Berlin G, Murphy M, Hammer S, et al. The pulse of nurses’ perspectives on AI in healthcare delivery. McKinsey & Company October 1, 2024. Accessed April 7, 2025. https://www.mckinsey.com/industries/healthcare/our-insights/the-pulse-of-nurses-perspectives-on-ai-in-healthcare-delivery.
  7. Choudhury A, Asan O. Impact of accountability, training, and human factors on the use of artificial intelligence in healthcare: Exploring the perceptions of healthcare practitioners in the US. Hum Factors in Healthc 2022;2:100021. doi:10.1016/j.hfh.2022.100021.
  8. McCarthy J. What is artificial intelligence? Stanford University November 12, 2007. Accessed April 7, 2025. https://www-formal.stanford.edu/jmc/whatisai.pdf.
  9. Hirani R, Noruzi K, Khuram H, et al. Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities. Life 2024;14(5):557. doi:10.3390/life14050557.
  10. Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electron Mark 2021;31(3):685-695. doi:10.1007/s12525-021-00475-2.
  11. Fleuren LM, Klausch TLT, Zwager CL, et al. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Med 2020;46(3):383-400. doi:10.1007/s00134-019-05872-y.
  12. Bartek MA, Saxena RC, Solomon S, et al. Improving Operating Room Efficiency: Machine Learning Approach to Predict Case-Time Duration. J Am Coll Surg 2019;229(4):346-354e3. doi:10.1016/j.jamcollsurg.2019.05.029.
  13. ShahabiKargar Z, Khanna S, Good N, et al. Predicting Procedure Duration to Improve Scheduling of Elective Surgery. In: Pham DN, Park SB, eds. PRICAI 2014: Trends in Artificial Intelligence Vol 8862. Lecture Notes in Computer Science. Springer International Publishing; 2014:998-1009. doi:10.1007/978-3-319-13560-1_86.
  14. Kaya U, Yılmaz A, Aşar S. Sepsis Prediction by Using a Hybrid Metaheuristic Algorithm: A Novel Approach for Optimizing Deep Neural Networks. Diagnostics 2023;13(12):2023. doi:10.3390/diagnostics13122023.
  15. Callaway E. What’s next for AlphaFold and the AI protein-folding revolution. Nature 2022;604(7905):234-238. doi:10.1038/d41586-022-00997-5.
  16. Vaswani A, Shazeer N, Parmar N, et al. Attention Is All You Need. Published online August 2, 2023. doi:10.48550/arXiv.1706.03762.
  17. Nielsen MA. Neural Networks and Deep Learning Determination Press; 2015. Accessed April 11, 2025. http://neuralnetworksanddeeplearning.com.
  18. But What Is a Neural Network? Deep Learning Chapter 1. Published 2017. Accessed April 11, 2025. Available at: https://www.youtube.com/watch?v=aircAruvnKk.
  19. Lee JH, Hong H, Nam G, et al. Effect of Human-AI Interaction on Detection of Malignant Lung Nodules on Chest Radiographs. Radiology 2023;307(5):e222976. doi:10.1148/radiol.222976.
  20. Kim HE, Kim HH, Han BK, et al. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: A retrospective, multireader study. Lancet Digit Health 2020;2(3):e138-e148. doi:10.1016/S2589-7500(20)30003-0.
  21. Yoon JH, Kim EK. Deep Learning-Based Artificial Intelligence for Mammography. Korean J Radiol 2021;22(8):1225. doi:10.3348/kjr.2020.1210.
  22. Thian YL, Li Y, Jagmohan P, Sia D, Chan VEY, Tan RT. Convolutional Neural Networks for Automated Fracture Detection and Localization on Wrist Radiographs. Radiol Artif Intell 2019;1(1):e180001. doi:10.1148/ryai.2019180001.
  23. Joseph S, Selvaraj J, Mani I, et al. Diagnostic Accuracy of Artificial Intelligence-Based Automated Diabetic Retinopathy Screening in Real-World Settings: A Systematic Review and Meta-Analysis. Am J Ophthalmol 2024;263:214-230. doi:10.1016/j.ajo.2024.02.012.
  24. Burdick H, Pino E, Gabel-Comeau D, et al. Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: A prospective multicentre clinical outcomes. evaluation of real-world patient data from US hospitals. BMJ Health Care Inform 2020;27(1):e100109. doi:10.1136/bmjhci-2019-100109.
  25. Lubitz SA, Faranesh AZ, Selvaggi C, et al. Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study. Circulation 2022;146(19):1415-1424. doi:10.1161/CIRCULATIONAHA.122.060291.
  26. Perez MV, Mahaffey KW, Hedlin H, et al. Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. N Engl J Med 2019;381(20):1909-1917. doi:10.1056/NEJMoa1901183.
  27. Johnson KB, Wei W, Weeraratne D, et al. Precision Medicine, AI, and the Future of Personalized Health Care. Clin Transl Sci 2021;14(1):86-93. doi:10.1111/cts.12884.
  28. Shah SJ, Devon-Sand A, Ma SP, et al. Ambient artificial intelligence scribes: physician burnout and perspectives on usability and documentation burden. J Am Med Inform Assoc 2025;32(2):375-380. doi:10.1093/jamia/ocae295.
  29. Ma SP, Liang AS, Shah SJ, et al. Ambient artificial intelligence scribes: utilization and impact on documentation time. J Am Med Inform Assoc 2025;32(2):381-385. doi:10.1093/jamia/ocae304.
  30. Garcia P, Ma SP, Shah S, et al. Artificial Intelligence–Generated Draft Replies to Patient Inbox Messages. JAMA Netw Open 2024;7(3):e243201. doi:10.1001/jamanetworkopen.2024.3201.
  31. Yan S, Knapp W, Leong A, et al. Prompt engineering on leveraging large language models in generating response to InBasket messages. J Am Med Inform Assoc 2024;31(10):2263-2270. doi:10.1093/jamia/ocae172.
  32. 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. doi:10.1001/jamanetworkopen.2024.40969.
  33. Celi LA, Cellini J, Charpignon ML, et al. Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review. Fraser HS, ed. PLOS Digit Health 2022;1(3):e0000022. doi:10.1371/journal.pdig.0000022.
  34. Norori N, Hu Q, Aellen FM, et al. Addressing bias in big data and AI for health care: A call for open science. Patterns 2021;2(10):100347. doi:10.1016/j.patter.2021.100347.
  35. Metz C, Kang C, Frenkel S, et al. How tech giants cut corners to harvest data for A.I. The New York Times Published April 6, 2024. Accessed April 18, 2025. https://www.nytimes.com/2024/04/06/technology/tech-giants-harvest-data-artificial-intelligence.html.
  36. Zack T, Lehman E, Suzgun M, et al. Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: A model evaluation study. Lancet Digit Health 2024;6(1):e12-e22. doi:10.1016/S2589-7500(23)00225-X.
  37. Yang Y, Liu X, Jin Q, et al. Unmasking and quantifying racial bias of large language models in medical report generation. Commun Med 2024;4(1):176. doi:10.1038/s43856-024-00601-z.
  38. Kim J, Cai ZR, Chen ML, et al. Assessing Biases in Medical Decisions via Clinician and AI Chatbot Responses to Patient Vignettes. JAMA Netw Open 2023;6(10):e2338050. doi:10.1001/jamanetworkopen.2023.38050.
  39. Omiye JA, Lester JC, Spichak S, et al. Large language models propagate race-based medicine. npj Digit Med 2023;6(1):195. doi:10.1038/s41746-023-00939-z.
  40. Weiser B. Here’s What Happens When Your Lawyer Uses ChatGPT. The New York Times https://www.nytimes.com/2023/05/27/nyregion/avianca-airline-lawsuit-chatgpt.html. May 27, 2023. Accessed April 18, 2025.
  41. Huang L, Yu W, Ma W, et al. A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions. ACM Trans Inf Syst 2025;43(2):1-55. doi:10.1145/3703155.
  42. Briganti G. How ChatGPT works: A mini review. Eur Arch Otorhinolaryngol 2024;281(3):1565-1569. doi:10.1007/s00405-023-08337-7.
  43. Biro J, Handley JL, Cobb NK, et al. Accuracy and Safety of AI-Enabled Scribe Technology: Instrument Validation Study. J Med Internet Res 2025;27:e64993. doi:10.2196/64993.
  44. Chen S, Guevara M, Moningi S, et al. The effect of using a large language model to respond to patient messages. Lancet Digit Health 2024;6(6):e379-e381. doi:10.1016/S2589-7500(24)00060-8.
  45. Kelly BS, Judge C, Bollard SM, et al. Radiology artificial intelligence: A systematic review and evaluation of methods (RAISE). Eur Radiol 2022;32(11):7998-8007. doi:10.1007/s00330-022-08784-6.
  46. Biro JM, Handley JL, Malcolm McCurry J, et al. Opportunities and risks of artificial intelligence in patient portal messaging in primary care. npj Digit Med 2025;8(1):1-6. doi:10.1038/s41746-025-01586-2.
  47. Parasuraman R, Molloy R, Singh IL. Performance consequences of automation-induced “complacency.” Int J Aviat Psychol 1993;3(1):1-23. doi:10.1207/s15327108ijap0301_1.
  48. Molloy R, Parasuraman R. Monitoring an Automated System for a Single Failure: Vigilance and Task Complexity Effects. Hum Factors 1996;38(2):311-322. doi:10.1177/001872089606380211.
  49. Adamson RE. Functional fixedness as related to problem solving: a repetition of three experiments. J Exp Psychol 1952;44(4):288-291. doi:10.1037/h0062487.
  50. Klayman J. Varieties of Confirmation Bias. In: Busemeyer J, Hastie R, Medin DL, eds. Psychology of Learning and Motivation Vol 32. Academic Press; 1995:385-418. doi:10.1016/S0079-7421(08)60315-1.
  51. Skitka LJ, Mosier KL, Burdick M. Does automation bias decision-making? Int J Hum Comput Stud 1999;51(5):991-1006. doi:10.1006/ijhc.1999.0252.
  52. Goddard K, Roudsari A, Wyatt JC. Automation bias: A systematic review of frequency, effect mediators, and mitigators. J Am Med Inform Assoc 2012;19(1):121-127. doi:10.1136/amiajnl-2011-000089.
  53. Croskerry P. Diagnostic Failure: A Cognitive and Affective Approach. In: Henriksen K, Battles JB, Marks ES, Lewin DI, eds. Advances in Patient Safety: From Research to Implementation (Volume 2: Concepts and Methodology) Advances in Patient Safety. Agency for Healthcare Research and Quality (US); 2005. Accessed April 28, 2025. http://www.ncbi.nlm.nih.gov/books/NBK20487/.
  54. Prinzel LJ. The Relationship of Self-Efficacy and Complacency in Pilot-Automation Interaction; 2002. Accessed May 15, 2025. https://ntrs.nasa.gov/citations/20020076395.
  55. Responsible AI Guide and Checklists. CHAI - Coalition for Health AI. February 26, 2025. Accessed April 27, 2025. https://chai.org/responsible-ai-guide/.
  56. Economou N, Elmore M, Callahan A, et al. Responsible AI Guide. Published online June 26, 2024. Accessed May 16, 2025. https://chai.org/responsible-ai-guide/.
  57. Reddy S. Generative AI in healthcare: an implementation science informed translational path on application, integration and governance. Implement Sci 2024;19(1):27. doi:10.1186/s13012-024-01357-9.
  58. Reddy S, Allan S, Coghlan S, et al. A governance model for the application of AI in health care. J Am Med Inform Assoc 2020;27(3):491-497. doi:10.1093/jamia/ocz192.
Page last reviewed July 2025
Page originally created June 2025

Internet Citation: References. Content last reviewed July 2025. Agency for Healthcare Research and Quality, Rockville, MD.
https://www.ahrq.gov/diagnostic-safety/resources/issue-briefs/dxsafety-ai-wave-references.html

Click to copy citation