Will Generative Artificial Intelligence Deliver on Its Promise in Health Care?

Wachter, Robert M.; Brynjolfsson, Erik

doi:10.1001/jama.2023.25054

Cited by 37 publications

(9 citation statements)

References 34 publications

(40 reference statements)

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“…Empathy can help family members to understand and motivate patients, thus boosting their confidence in treatment. The GPT-4 itself seems to have an advantage over clinical doctors in terms of empathy 34 , 35 . In the present study, we found that GPT-agents had significantly enhanced empathy compared to the base GPT-4.…”

Section: Discussionmentioning

confidence: 99%

GPT-agents based on medical guidelines can improve the responsiveness and explainability of outcomes for traumatic brain injury rehabilitation

Zhenzhu,

Jingfeng,

Wei

et al. 2024

Sci Rep

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This study explored the application of generative pre-trained transformer (GPT) agents based on medical guidelines using large language model (LLM) technology for traumatic brain injury (TBI) rehabilitation-related questions. To assess the effectiveness of multiple agents (GPT-agents) created using GPT-4, a comparison was conducted using direct GPT-4 as the control group (GPT-4). The GPT-agents comprised multiple agents with distinct functions, including “Medical Guideline Classification”, “Question Retrieval”, “Matching Evaluation”, “Intelligent Question Answering (QA)”, and “Results Evaluation and Source Citation”. Brain rehabilitation questions were selected from the doctor-patient Q&A database for assessment. The primary endpoint was a better answer. The secondary endpoints were accuracy, completeness, explainability, and empathy. Thirty questions were answered; overall GPT-agents took substantially longer and more words to respond than GPT-4 (time: 54.05 vs. 9.66 s, words: 371 vs. 57). However, GPT-agents provided superior answers in more cases compared to GPT-4 (66.7 vs. 33.3%). GPT-Agents surpassed GPT-4 in accuracy evaluation (3.8 ± 1.02 vs. 3.2 ± 0.96, p = 0.0234). No difference in incomplete answers was found (2 ± 0.87 vs. 1.7 ± 0.79, p = 0.213). However, in terms of explainability (2.79 ± 0.45 vs. 07 ± 0.52, p < 0.001) and empathy (2.63 ± 0.57 vs. 1.08 ± 0.51, p < 0.001) evaluation, the GPT-agents performed notably better. Based on medical guidelines, GPT-agents enhanced the accuracy and empathy of responses to TBI rehabilitation questions. This study provides guideline references and demonstrates improved clinical explainability. However, further validation through multicenter trials in a clinical setting is necessary. This study offers practical insights and establishes groundwork for the potential theoretical integration of LLM-agents medicine.

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Section: Discussionmentioning

confidence: 99%

GPT-agents based on medical guidelines can improve the responsiveness and explainability of outcomes for traumatic brain injury rehabilitation

Zhenzhu,

Jingfeng,

Wei

et al. 2024

Sci Rep

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“…The adoption of Artificial Intelligence (AI) in healthcare is rising, catalyzed by the emergence of Large Language Models (LLMs) like OpenAI’s ChatGPT 1 2 3 4 . Unlike predictive AI, generative AI produces original content such as sound, image, and text 5 .…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Testing and Evaluation of Healthcare Applications of Large Language Models (LLMs)

Bedi,

Liu,

Orr-Ewing

et al. 2024

Preprint

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Importance: Large Language Models (LLMs) can assist in a wide range of healthcare-related activities. Current approaches to evaluating LLMs make it difficult to identify the most impactful LLM application areas. Objective: To summarize the current evaluation of LLMs in healthcare in terms of 5 components: evaluation data type, healthcare task, Natural Language Processing (NLP)/Natural Language Understanding (NLU) task, dimension of evaluation, and medical specialty. Data Sources: A systematic search of PubMed and Web of Science was performed for studies published between 01-01-2022 and 02-19-2024. Study Selection: Studies evaluating one or more LLMs in healthcare. Data Extraction and Synthesis: Three independent reviewers categorized 519 studies in terms of data used in the evaluation, the healthcare tasks (the what) and the NLP/NLU tasks (the how) examined, the dimension(s) of evaluation, and the medical specialty studied. Results: Only 5% of reviewed studies utilized real patient care data for LLM evaluation. The most popular healthcare tasks were assessing medical knowledge (e.g. answering medical licensing exam questions, 44.5%), followed by making diagnoses (19.5%), and educating patients (17.7%). Administrative tasks such as assigning provider billing codes (0.2%), writing prescriptions (0.2%), generating clinical referrals (0.6%) and clinical notetaking (0.8%) were less studied. For NLP/NLU tasks, the vast majority of studies examined question answering (84.2%). Other tasks such as summarization (8.9%), conversational dialogue (3.3%), and translation (3.1%) were infrequent. Almost all studies (95.4%) used accuracy as the primary dimension of evaluation; fairness, bias and toxicity (15.8%), robustness (14.8%), deployment considerations (4.6%), and calibration and uncertainty (1.2%) were infrequently measured. Finally, in terms of medical specialty area, most studies were in internal medicine (42%), surgery (11.4%) and ophthalmology (6.9%), with nuclear medicine (0.6%), physical medicine (0.4%) and medical genetics (0.2%) being the least represented. Conclusions and Relevance: Existing evaluations of LLMs mostly focused on accuracy of question answering for medical exams, without consideration of real patient care data. Dimensions like fairness, bias and toxicity, robustness, and deployment considerations received limited attention. To draw meaningful conclusions and improve LLM adoption, future studies need to establish a standardized set of LLM applications and evaluation dimensions, perform evaluations using data from routine care, and broaden testing to include administrative tasks as well as multiple medical specialties. Keywords: Large Language Models, Generative Artificial Intelligence, Healthcare, Dimensions of Evaluation, Evaluation Metrics.

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“…The recent introduction of large language models (LLMs) such as ChatGPT has led to renewed focus on the use of natural language processing (NLP) to improve both quality and efficiency in healthcare. 15 LLMs possess a range of capabilities which may be applied to the clinical domain, one of which is text summarization. Previous reports have evaluated the potential use of LLMs in summarizing scientific literature, radiology reports, patient problem lists and doctor-patient conversations, with varying success.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Large Language Models for Drafting Emergency Department Discharge Summaries

Williams,

Bains,

Tang

et al. 2024

Preprint

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ImportanceLarge language models (LLMs) possess a range of capabilities which may be applied to the clinical domain, including text summarization. As ambient artificial intelligence scribes and other LLM-based tools begin to be deployed within healthcare settings, rigorous evaluations of the accuracy of these technologies are urgently needed.ObjectiveTo investigate the performance of GPT-4 and GPT-3.5-turbo in generating Emergency Department (ED) discharge summaries and evaluate the prevalence and type of errors across each section of the discharge summary.DesignCross-sectional study.SettingUniversity of California, San Francisco ED.ParticipantsWe identified all adult ED visits from 2012 to 2023 with an ED clinician note. We randomly selected a sample of 100 ED visits for GPT-summarization.ExposureWe investigate the potential of two state-of-the-art LLMs, GPT-4 and GPT-3.5-turbo, to summarize the full ED clinician note into a discharge summary.Main Outcomes and MeasuresGPT-3.5-turbo and GPT-4-generated discharge summaries were evaluated by two independent Emergency Medicine physician reviewers across three evaluation criteria: 1) Inaccuracy of GPT-summarized information; 2) Hallucination of information; 3) Omission of relevant clinical information. On identifying each error, reviewers were additionally asked to provide a brief explanation for their reasoning, which was manually classified into subgroups of errors.ResultsFrom 202,059 eligible ED visits, we randomly sampled 100 for GPT-generated summarization and then expert-driven evaluation. In total, 33% of summaries generated by GPT-4 and 10% of those generated by GPT-3.5-turbo were entirely error-free across all evaluated domains. Summaries generated by GPT-4 were mostly accurate, with inaccuracies found in only 10% of cases, however, 42% of the summaries exhibited hallucinations and 47% omitted clinically relevant information. Inaccuracies and hallucinations were most commonly found in the Plan sections of GPT-generated summaries, while clinical omissions were concentrated in text describing patients’ Physical Examination findings or History of Presenting Complaint.Conclusions and RelevanceIn this cross-sectional study of 100 ED encounters, we found that LLMs could generate accurate discharge summaries, but were liable to hallucination and omission of clinically relevant information. A comprehensive understanding of the location and type of errors found in GPT-generated clinical text is important to facilitate clinician review of such content and prevent patient harm.

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Will Generative Artificial Intelligence Deliver on Its Promise in Health Care?

Cited by 37 publications

References 34 publications

GPT-agents based on medical guidelines can improve the responsiveness and explainability of outcomes for traumatic brain injury rehabilitation

GPT-agents based on medical guidelines can improve the responsiveness and explainability of outcomes for traumatic brain injury rehabilitation

A Systematic Review of Testing and Evaluation of Healthcare Applications of Large Language Models (LLMs)

Evaluating Large Language Models for Drafting Emergency Department Discharge Summaries

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