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Abstract
Artificial intelligence (AI) is transforming healthcare and holds immense potential for the medicolegal field. It also presents significant challenges for these systems and their participants. This article overviews core AI concepts such as machine learning and large language models. It highlights current medical applications spanning clinical decision support, computer vision, robotics, and predictive analytics. AI can aid research, summarize documents, draft reports, and enhance quality and efficiency in a medicolegal practice. Limitations must be recognized and managed. Human expertise remains irreplaceable for nuanced analysis, and oversight is crucial. With thoughtful adoption, AI can augment medicolegal evaluations and enhance quality and efficiency. But human skills like critical thinking, judgment, and compassion must persist at the heart of this profoundly human profession.
Contributor Notes
In preparation for this article, OpenAI Chat GPT-4 was accessed in September 2023 to research issues. The authors wrote the content. The accuracy of Key Concepts was assessed by peers and by Chat GPT-4 on October 6, 2023. Chat GPT-4 and Anthropic Claude 2 were used to produce the examples in September 2023.
- Abstract
- Introduction
- Rise of AI in Workers' Compensation
- Rise of LLMs
- Understanding ChatGPT
- Using LLMs in the Medicolegal Field
- Crafting Effective Prompts for LLMs
- Navigating AI in the Medicolegal Field: Privacy, Ethics, and Responsible Adoption
- Practical Guidance for Responsible AI Integration
- Enhancing the Independent Medical Evaluation Process With AI
- AI Tools to Enhance Medicolegal Practice
- The Future of AI in Medicolegal Practice
- Conclusion
- References
-
1.
AMA Guides to the Evaluation of Disease and Injury Causation, 2nd Ed, Chapter 11.
-
2.
Pourmemari et al. Diabetes Research and Clinical Practice. 2018;143:166–174.
-
3.
Caixas et al. European Journal of Endocrinology. 1998;138(3):310–314.
-
1.
Will ChatGPT transform healthcare? Nat Med. 2023;29(3):505–506. doi: 10.1038/s41591-023-02289-5.
-
2.
Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719–731. doi: 10.1038/s41551-018-0305-z.
-
3.
Dave T, Athaluri SA, Singh S. ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations. Front Artif Intell. 2023;6:1169595. doi: 10.3389/frai.2023.1169595.
-
4.
Winn Z. MIT News website. “Study finds ChatGPT boosts worker productivity for some writing tasks.” July 14, 2023. Accessed October 20, 2023. https://news.mit.edu/2023/study-finds-chatgpt-boosts-worker-productivity-writing-0714
-
5.
Nelson H. HER Intelligence website. “Artificial Intelligence (AI) Use Cases to Mitigate Clinician Burnout.” Accessed October 20, 2023. https://ehrintelligence.com/features/artificial-intelligence-ai-use-cases-to-mitigate-clinician-burnout
-
6.
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. doi: 10.2196/48659.
-
7.
Mansell N, Mulligan A, Aguilar Calero M. Clinician of the future: survey data. Elsevier Data Repository. 2023. Version 1. doi: 10.17632/hnt5563ysh.1.
-
8.
Harris E. Large language models answer medical questions accurately, but can't match clinicians' knowledge. JAMA. 2023;330(9):792–794. doi: 10.1001/jama.2023.14311.
-
9.
Parikh RB, Helmchen LA. Paying for artificial intelligence in medicine. NPJ Digit Med. 2022;5(1):63. doi: 10.1038/s41746-022-00609-6.
-
10.
Bommasani R, Hudson DA, Adeli E, et al. On the opportunities and risks of foundation models. arXiv. 2021. Submitted August 16, 2021. Accessed October 24, 2023. doi: 10.48550/arXiv.2108.07258.
-
11.
Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Presentation at: 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA. 2017.
-
12.
Strong E, DiGiammarino A, Weng Y, et al. Chatbot vs medical student performance on free-response clinical reasoning examinations. JAMA Intern Med. 2023;183(9):1028–1030. doi: 10.1001/jamainternmed.2023.2909.
-
13.
Huang J, Tan M. The role of ChatGPT in scientific communication: writing better scientific review articles. Am J Cancer Res. 2023;13(4):1148–1154.
-
14.
Emsley R. ChatGPT: these are not hallucinations - they're fabrications and falsifications. Schizophrenia (Heidelb). 2023;9(1):52. doi: 10.1038/s41537-023-00379-4.
-
15.
Liévin V, Egeberg Hother C, Winther O. Can large language models reason about medical questions? arXiv. 2022. Submitted July 17, 2022. Accessed October 24, 2023. doi: 10.48550/arxiv.2207.08143.
-
16.
Rudin C. Stop explaining black box machine learning models for high-stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1(5):206–215.
-
17.
Hong G, Smith M, Lin S. The AI will see you now: feasibility and acceptability of a conversational AI medical interviewing system. JMIR Form Res. 2022;6(6):e37028. doi: 10.2196/37028.
-
18.
Kanjee Z, Crowe B, Rodman A. Accuracy of a generative artificial intelligence model in a complex diagnostic challenge. JAMA. 2023;330(1):78–80. doi: 10.1001/jama.2023.
-
19.
Shen Y, Heacock L, Elias J, et al. ChatGPT and other large language models are double-edged swords. Radiology. 2023;307(2):e230163. doi: 10.1148/radiol.230163.
-
20.
Tabone W, de Winter J. Using ChatGPT for human-computer interaction research: a primer. R Soc Open Sci. 2023;10(9):231053. doi: 10.1098/rsos.231053.
-
21.
Rabaan AA, Bakhrebah MA, Alotaibi J, et al. Unleashing the power of artificial intelligence for diagnosing and treating infectious diseases: a comprehensive review. J Infect Public Health. 2023;16(11):1837–1847. doi: 10.1016/j.jiph.2023.08.021.
-
22.
Yoo JH. Let's look on the bright side of ChatGPT. J Korean Med Sci. 2023;38(27):e231. doi: 10.3346/jkms.2023.38.e231.
-
23.
Thoppilan R, De Freitas D, Hall L, et al. LaMDA: Language Models for Dialog Applications. arXiv. 2022. Submitted January 20, 2022. Accessed October 24, 2023. doi: 10.48550/arXiv.2201.08239
-
24.
Fatani B. ChatGPT for future medical and dental research. Cureus. 2023;15(4):e37285. doi: 10.7759/cureus.37285.
-
25.
Harada Y, Katsukura S, Kawamura R, Shimizu T. Efficacy of artificial-intelligence-driven differential-diagnosis list on the diagnostic accuracy of physicians: an open-label randomized controlled study. Int J Environ Res Public Health. 2021;18(4):2086. doi: 10.3390/ijerph18042086.
-
26.
Hirosawa T, Harada Y, Yokose M, et al. Diagnostic accuracy of differential-diagnosis lists generated by generative pretrained transformer 3 chatbot for clinical vignettes with common chief complaints: a pilot study. Int J Environ Res Public Health. 2023;20(4):3378. doi: 10.3390/ijerph20043378.
-
27.
Azer SA, Guerrero APS. The challenges imposed by artificial intelligence: are we ready in medical education? BMC Med Educ. 2023;23(1):680. doi: 10.1186/s12909-023-04660-z.
-
28.
Çalışkan SA, Demir K, Karaca O. Artificial intelligence in medical education curriculum: an e-Delphi study for competencies. PLoS One. 2022;17(7):e0271872. doi: 10.1371/journal.pone.0271872.
-
29.
Hutson M. Could AI help you to write your next paper? Nature. 2022;611(7934):192–193. doi: 10.1038/d41586-022-03479-w.
-
30.
Sallam M. ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthcare (Basel). 2023;11(6):887. doi: 10.3390/healthcare11060887.
-
31.
Chi EA, Chi G, Tsui CT, et al. Development and validation of an artificial intelligence system to optimize clinician review of patient records. JAMA Netw Open. 2021;4(7):e2117391. doi: 10.1001/jamanetworkopen.2021.17391.
-
32.
Lee JY. Can an artificial intelligence chatbot be the author of a scholarly article? J Educ Eval Health Prof. 2023;20:6. doi: 10.3352/jeehp.2023.20.6.
-
33.
Yeo YH, Samaan JS, Ng WH, et al. Assessing the performance of ChatGPT in answering questions regarding cirrhosis and hepatocellular carcinoma. Clin Mol Hepatol. 2023;29(3):721–732. doi: 10.3350/cmh.2023.0089.
-
34.
Bhattacharyya M, Miller VM, Bhattacharyya D, Miller LE. High rates of fabricated and inaccurate references in ChatGPT-generated medical content. Cureus. 2023;15(5):e39238. doi: 10.7759/cureus.39238.
-
35.
Petersson L, Larsson I, Nygren JM, et al. Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden. BMC Health Serv Res. 2022;22(1):850. doi: 10.1186/s12913-022-08215-8.
-
36.
Babl FE, Babl MP. Generative artificial intelligence: can ChatGPT write a quality abstract? Emerg Med Australas. 2023;35(5):809–811. doi: 10.1111/1742-6723.14233.
-
37.
Lee P, Bubeck S, Petro J. Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine. N Engl J Med. 2023;388(13):1233–1239. doi: 10.1056/NEJMsr2214184.
-
38.
Ouyang L, Wu P, Jiang Z, et al. Training language models to follow instructions with human feedback. arXiv. 2022. Submitted March 4, 2022. Accessed October 10, 2023. doi: 10.48550/arXiv.2203.02155.
-
39.
Meyer AND, Payne VL, Meeks DW, et al. Physicians' diagnostic accuracy, confidence, and resource requests: a vignette study. JAMA Intern Med. 2013;173(21):1952–1958. doi: 10.1001/jamainternmed.2013.10081.
-
40.
Ruckenstein M, Dow Schüll N. The datafication of health. Annu Rev Sociol. 2017;43:261–278.
-
41.
Basil NN, Ambe S, Ekhator C, Fonkem E. Health records database and inherent security concerns: a review of the literature. Cureus. 2022;14(10):e30168. doi: 10.7759/cureus.30168.
-
42.
The US Department of Health and Human Services website. Disclosures for Workers' Compensation Purposes. 45 CFR 164.512(1). Accessed October 10, 2023. https://www.hhs.gov/hipaa/for-professionals/privacy/guidance/disclosures-workers-compensation/index.html.
-
43.
Flanagin A, Bibbins-Domingo K, Berkwits M, Christiansen SL. Nonhuman “authors” and implications for the integrity of scientific publication and medical knowledge. JAMA. 2023;329(8):637–639. doi: 10.1001/jama.2023.1344.
-
44.
Flanagin A, Kendall-Taylor J, Bibbins-Domingo K. Guidance for authors, peer reviewers, and editors on use of AI, language models, and chatbots. JAMA. 2023;330(8):702–703. doi: 10.1001/jama.2023.12500.
-
45.
Jeyaraman M, Ramasubramanian S, Balaji S, et al. ChatGPT in action: harnessing artificial intelligence potential and addressing ethical challenges in medicine, education, and scientific research. World J Methodol. 2023;13(4):170–178. doi: 10.5662/wjm.v13.i4.170.
-
46.
Kawamura R, Harada Y, Sugimoto S, et al. Incidence of diagnostic errors among unexpectedly hospitalized patients using an automated medical history-taking system with a differential diagnosis generator: retrospective observational study. JMIR Med Inform. 2022;10(1):e35225. doi: 10.2196/35225.
-
47.
Char DS, Shah NH, Magnus D. Implementing machine learning in health care: addressing ethical challenges. N Engl J Med. 2018;378(11):981–983.
-
48.
Seth I, Xie Y, Rodwell A, et al. Exploring the role of a large language model on carpal tunnel syndrome management: an observation study of ChatGPT. J Hand Surg Am. 2023;48(10):1025–1033. doi: 10.1016/j.jhsa.2023.07.003.
-
49.
Shah NH, Entwistle D, Pfeffer MA. Creation and adoption of large language models in medicine. JAMA. 2023;330(9):866–869. doi: 10.1001/jama.2023.14217.
-
50.
Lundh A, Lexchin J, Mintzes B, et al. Industry sponsorship and research outcome. Cochrane Database Syst Rev. 2017;2(2):MR000033. doi: 10.1002/14651858.MR000033.pub3.
-
51.
Sendak MP, Ratliff W, Sarro D, et al. Real-world integration of a sepsis deep learning technology into routine clinical care: implementation study. JMIR Med Inform. 2020;8(7):e15182.
-
52.
Roman A, Al-Sharif L, Al Gharyani M. The expanding role of ChatGPT (Chat-Generative Pre-Trained Transformer) in neurosurgery: a systematic review of literature and conceptual framework. Cureus. 2023;15(8):e43502. doi: 10.7759/cureus.43502.
-
53.
James CA, Wachter RM, Woolliscroft JO. Preparing clinicians for a clinical world influenced by artificial intelligence. JAMA. 2022;327(14):1333–1334. doi: 10.1001/jama.2022.3580.
-
54.
Wornow M, Xu Y, Thapa R, et al. The shaky foundations of large language models and foundation models for electronic health records. NPJ Digit Med. 2023;6(1):135. doi: 10.1038/s41746-023-00879-8.
-
55.
Nayak A, Alkaitis MS, Nayak K, et al. Comparison of history of present illness summaries generated by a chatbot and senior internal medicine residents. JAMA Intern Med. 2023;183(9):1026–1027. doi: 10.1001/jamainternmed.2023.2561.
-
56.
Temsah O, Khan SA, Chaiah Y, et al. Overview of early ChatGPT's presence in medical literature: insights from a hybrid literature review by ChatGPT and human experts. Cureus. 2023;15(4):e37281. doi: 10.7759/cureus.37281.
-
57.
Krittanawong C, Zhang H, Wang Z, et al. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2017;69(21):2657–2664.
-
58.
Suppadungsuk S, Thongprayoon C, Krisanapan P, et al. Examining the validity of ChatGPT in identifying relevant nephrology literature: findings and implications. J Clin Med. 2023;12(17):5550. doi: 10.3390/jcm12175550.
-
59.
Cheng J, Dong L, Lapata M. Summarizing text on any aspects: a knowledge-informed weakly-supervised approach. arXiv. 2022. Submitted April 21, 2022. Accessed October 10, 2023. doi: 10.48550/arXiv.2204.10389.
-
60.
Krittanawong C. The rise of artificial intelligence and the uncertain future for physicians. Eur J Intern Med. 2018;48:e13–e14. doi: 10.1016/j.ejim.2017.06.017.
-
61.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. doi: 10.1038/s41591-018-0300-7.
-
62.
Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med. 2023;388(13):1201–1208. doi: 10.1056/NEJMra2302038.
-
63.
Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022;28(1):31–38. doi: 10.1038/s41591-021-01614-0.
-
64.
Murphy K, Di Ruggiero E, Upshur R, et al. Artificial intelligence for good health: a scoping review of the ethics literature. BMC Med Ethics. 2021;22(1):14. doi: 10.1186/s12910-021-00577-8.
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