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. 2025 Feb 4;8(2):e2457879. doi: 10.1001/jamanetworkopen.2024.57879

Large Language Models for Chatbot Health Advice Studies

A Systematic Review

Bright Huo 1,✉, Amy Boyle 2, Nana Marfo 3, Wimonchat Tangamornsuksan 4, Jeremy P Steen 5, Tyler McKechnie 1, Yung Lee 1, Julio Mayol 6, Stavros A Antoniou 7, Arun James Thirunavukarasu 8, Stephanie Sanger 9, Karim Ramji 1, Gordon Guyatt 10
PMCID: PMC11795331  PMID: 39903463

Key Points

Question

What do studies report when evaluating the performance of large language models (LLMs) providing health advice?

Findings

In this systematic review of 137 articles, 99.3% of the studies assessed closed-source models and did not provide enough information to identify the LLM. Most (64.5%) studies used subjective means as the ground truth to define the successful performance of the LLM, while less than a third addressed the ethical, regulatory, and patient safety implications of clinically integrating LLMs.

Meaning

The findings of this study suggest that the extent of reporting varies considerably among studies evaluating the clinical accuracy of LLMs providing health advice.

Abstract

Importance

There is much interest in the clinical integration of large language models (LLMs) in health care. Many studies have assessed the ability of LLMs to provide health advice, but the quality of their reporting is uncertain.

Objective

To perform a systematic review to examine the reporting variability among peer-reviewed studies evaluating the performance of generative artificial intelligence (AI)–driven chatbots for summarizing evidence and providing health advice to inform the development of the Chatbot Assessment Reporting Tool (CHART).

Evidence Review

A search of MEDLINE via Ovid, Embase via Elsevier, and Web of Science from inception to October 27, 2023, was conducted with the help of a health sciences librarian to yield 7752 articles. Two reviewers screened articles by title and abstract followed by full-text review to identify primary studies evaluating the clinical accuracy of generative AI-driven chatbots in providing health advice (chatbot health advice studies). Two reviewers then performed data extraction for 137 eligible studies.

Findings

A total of 137 studies were included. Studies examined topics in surgery (55 [40.1%]), medicine (51 [37.2%]), and primary care (13 [9.5%]). Many studies focused on treatment (91 [66.4%]), diagnosis (60 [43.8%]), or disease prevention (29 [21.2%]). Most studies (136 [99.3%]) evaluated inaccessible, closed-source LLMs and did not provide enough information to identify the version of the LLM under evaluation. All studies lacked a sufficient description of LLM characteristics, including temperature, token length, fine-tuning availability, layers, and other details. Most studies (136 [99.3%]) did not describe a prompt engineering phase in their study. The date of LLM querying was reported in 54 (39.4%) studies. Most studies (89 [65.0%]) used subjective means to define the successful performance of the chatbot, while less than one-third addressed the ethical, regulatory, and patient safety implications of the clinical integration of LLMs.

Conclusions and Relevance

In this systematic review of 137 chatbot health advice studies, the reporting quality was heterogeneous and may inform the development of the CHART reporting standards. Ethical, regulatory, and patient safety considerations are crucial as interest grows in the clinical integration of LLMs.

This systematic review evaluates the reporting quality of studies on the development and use of chatbot health advice services.

Introduction

Large language models (LLMs) have expanded the potential for integrating artificial intelligence (AI) in medicine. LLMs are AI systems that are pretrained through a variety of word prediction tasks across enormous volumes of text taken from datasets, books, articles, and internet sources.1,2 With additional fine-tuning involving varying amounts of human feedback, LLMs acquire natural language processing (NLP) capabilities and can generate appropriate text outputs using layperson terminology in response to free-text prompts.3 Many patients and physicians use the internet to access health information,4 and generative AI-driven chatbots present a convenient new way to access answers to medical questions.5 Early data suggest that generative AI-driven chatbots can even respond to patients with expressions of empathy.6

Patients have reported interest in seeking medical advice from chatbots,7 leading to a rapid expansion in literature assessing the ability of chatbots to offer sound health advice to physicians and patients: chatbot health advice studies (CHAS).8 This research has investigated the ability of chatbots to summarize evidence and provide health advice about screening, diagnosis, treatment, and disease prevention.9 However, chatbots are typically not designed for medical application and currently lack regulation for their use in medicine.10 Furthermore, generative AI-driven chatbots present unique risks of hallucinating (ie, confident but incorrect) false results and propagating misleading information and advice.11 Thus, it is unsurprising that CHAS report varying levels of chatbot performance, raising concerns about their limitations and risks.1,12,13

CHAS also present their own unique problems. Currently, these studies are inconsistent in their design, analysis, and reporting.14,15,16 With a lack of standardized reporting, the assessment of the quality of CHAS and the interpretation of their findings remain challenging.17 These studies have the potential to provide useful assessment of LLM performance and insight into the patient safety implications of chatbot advice, yet the apparent limitations of the studies themselves necessitate clarifying and standardizing current approaches. In this systematic review, we investigated chatbot selection, query approach, and performance evaluation. The information gathered from this systematic review may contribute to the development of a novel chatbot assessment reporting tool (CHART) to standardize reporting methods in chatbot assessment research.18

Methods

This systematic review adheres to the reporting standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline.19 In this study, we refer to LLMs, generative AI-driven chatbots, and chatbots synonymously. Our protocol was prospectively registered on Open Sciences Framework.20

Search Strategy and Selection Criteria

We set out to characterize the approach of studies that assess the performance of generative AI-driven chatbots for summarizing evidence and providing health advice. To capture all relevant studies, an academic health sciences librarian (S.S.) with expertise in systematic reviews aided the study team in designing a comprehensive search strategy. The detailed literature search syntax can be found in the eAppendix in Supplement 1. Records were retrieved from MEDLINE via Ovid, Embase via Elsevier, and Web of Science on October 27, 2023. The study team completed 2 rounds of screening, first by title and abstract and then by full text. In all cases, 2 independent researchers (W.T. and J.P.S.) conducted screening, with a third independent researcher casting a deciding vote to resolve cases of disagreement. We excluded irrelevant studies and nonprimary studies. The Box illustrates our eligibility criteria.

Box. Inclusion and Exclusion Criteria.

Inclusion Criteria

  • Studies assessing the performance of chatbots for summarizing clinical evidence and providing health advice.

Exclusion Criteria

  • Systematic reviews and literature/narrative reviews.

  • Editorials, commentaries, opinions, studies published as correspondence articles, and studies of any type outlining earlier or potential future applications of chatbots.

  • Cross-sectional studies assessing opinions of future work for chatbot research.

  • Abstracts.

Inclusion and Exclusion Criteria

Data extraction was conducted with Covidence and aimed to capture basic study details (clinical specialty, topic, intervention, and comparator) in addition to the following: (1) how LLMs are selected for use in CHAS, (2) clinical purposes for which LLMs have been evaluated, (3) methods applied, (4) approach to performance evaluation (ie, summarizing evidence alone or summarizing evidence to inform recommendation), (5) reporting practices used, and (6) the presence of ethical considerations of the clinical application of generative AI-driven chatbots.

Statistical Analysis

The study team performed data analysis using descriptive statistics. We summarized findings pertaining to the methodologic approach of CHAS using frequencies as counts and percentages including the types of LLMs evaluated, the query strategy, and their performance evaluation. A narrative synthesis with no meta-analysis was planned, due to anticipated heterogeneity in study subjects and designs. We describe the full details related to the literature search and data extraction variables in our study protocol.20 The quality of the evidence was not evaluated due to the lack of high-quality, validated risk-of-bias tools available for CHAS.

Results

Study Characteristics

The study team reviewed 7752 articles that yielded 137 eligible CHAS (Figure). Table 1 lists all evaluated articles,6,13,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155 including their relevant specialty and study aims.

Figure. Overview of Literature Search.

Figure.

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There were no ongoing studies or studies awaiting classification. LLM indicates large language model.

Table 1. Study Characteristics.

Study Topic specialty Health advice aim
Al-Ashwal et al,21 2023 Medicine Treatment
Alessandri-Bonetti et al,22 2024 Medicine Diagnosis; treatment; general information
Ali et al,23 2023 Surgery Treatment; general information
Ali et al,24 2023 Medicine General information
Altamimi et al,25 2023 Primary care (FM, EM) Disease prevention; treatment; general information
Athavale et al,26 2023 Medicine Disease prevention; treatment; general information
Ayers et al,27 2023 Primary care (FM, EM) Disease prevention; treatment; general information
Ayers et al,6 2023 Primary care (FM, EM) Disease prevention; diagnosis; treatment; general information
Ayoub et al,28 2023 Medicine Differential diagnosis; diagnosis; treatment
Ayoub et al,29 2023 Surgery Treatment; general information
Balel et al,30 2023 Surgery Screening; diagnosis; treatment; general information
Bellinger et al,31 2024 Surgery Diagnosis; treatment; general information
Benirschke et al,32 2024 Pathology General information
Bernstein et al,33 2023 Medicine Treatment; general information
Biswas et al,34 2023 Primary care (FM, EM) Disease prevention; screening; treatment; general information
Caglar et al,35 2024 Surgery Treatment; general information
Cakir et al,36 2024 Medicine Diagnosis; treatment; general information
Campbell et al,37 2023 Medicine Diagnosis; treatment; general information
Caruccio et al,38 2024 Primary care (FM, EM) Diagnosis
Chee et al,39 2023 Medicine Differential diagnosis; diagnosis; treatment
Chen et al,40 2023 Medicine Diagnosis; treatment
Chervenak et al,41 2023 Medicine Disease prevention; treatment; general information
Chiesa-Estomba et al,42 2024 Surgery Treatment
Chowdhury et al,43 2023 Surgery General information
Cocci et al,44 2024 Surgery Diagnosis; treatment; general information
Coskun et al,45 2024 Medicine Treatment; general information
Coskun et al,46 2023 Primary care (FM, EM) Disease prevention; screening; diagnosis; treatment; general information
Cox et al,47 2023 Surgery General information
Davis et al,13 2023 Surgery Diagnosis; treatment; general information
Deiana et al,482023 Primary care (FM, EM) Disease prevention; general information
Dubin et al,49 2023 Surgery Treatment; general information
Dwyer et al,50 2023 Surgery Treatment; general information
Emile et al,51 2023 Surgery Disease prevention; screening; diagnosis; treatment; general information
Endo et al,52 2023 Surgery General information
Farhat et al,53 2024 Psychiatry Treatment; general information
Franco D’Souza et al,54 2023 Psychiatry Differential diagnosis; diagnosis; treatment; general information
Fraser et al,55 2023 Primary care (FM, EM) Diagnosis
Gabriel et al,56 2023 Surgery General information
Galido et al,57 2023 Psychiatry Differential diagnosis; treatment
Gebrael et al,58 2023 Primary care (FM, EM) Differential diagnosis; diagnosis; treatment
Goodman et al,59 2023 Medicine Disease prevention; screening; differential diagnosis; diagnosis; treatment; general information
Gordon et al,60 2024 Radiology General information
Gracias et al,61 2024 Surgery Disease prevention; treatment; general information
Gravel et al,62 2023 Medicine Treatment; general information
Gwak et al,63 2023 Primary care (FM, EM) Disease prevention; differential diagnosis; treatment; general information
Haemmerli et al,64 2023 Medicine Diagnosis; treatment
Harskamp et al,65 2024 Medicine Diagnosis; treatment; general information
Haver et al,66 2023 Radiology Disease prevention; screening
Haver et al,67 2023 Radiology Disease prevention; screening; general information
Hirosawa et al,68 2023 Medicine Differential diagnosis; diagnosis
Hirosawa et al,69 2023 Medicine Differential diagnosis
Hirosawa et al,70 2023 Medicine Differential diagnosis; diagnosis
Hopkins et al,71 2023 Medicine General information
Hristidis et al,72 2023 Medicine General information
Hurley et al,73 2024 Surgery Treatment; general information
Janopaul-Naylor et al,74 2024 Medicine Prognosis, treatment
Johnson et al,75 2023 Mixed Treatment
Kao et al,76 2023 Medicine Differential diagnosis; diagnosis
Kataoka et al,77 2021 Medicine Treatment; general information
King et al,78 2024 Medicine Treatment; general information
Koh et al,79 2023 Medicine General information
Krusche et al,80 2024 Medicine Differential diagnosis; diagnosis
Kuroiwa et al,81 2023 Surgery Differential diagnosis; diagnosis; general information
Kusunose et al,82 2023 Medicine General information
Lahat et al,83 2023 Medicine Diagnosis; treatment; general information
Lam et al,84 2024 Medicine Treatment; general information
Lechien et al,85 2024 Surgery Differential diagnosis; diagnosis; treatment
Lee et al,86 2023 Surgery General information
Levartovsky et al,87 2023 Medicine Treatment
Levkovich et al,88 2023 Psychiatry Treatment
Levkovich et al,89 2023 Psychiatry Disease prevention; diagnosis
Li et al,90 2023 Surgery Screening; differential diagnosis; diagnosis; treatment
Li et al,91 2023 Surgery General information
Lim et al,92 2023 Surgery Disease prevention; diagnosis; treatment; general information
Lim et al,93 20244 Medicine Screening; general information
Liu et al,94 2023 Medicine Screening; general information
Liu et al,95 2023 Surgery Treatment
Liu et al,96 2023 Surgery General information
Long et al,97 2024 Primary care (FM, EM) General information
Lower et al,98 2023 Surgery Diagnosis; treatment; general information
Luykx et al,99 2023 Surgery Treatment; general information
Lyons et al,100 2024 Psychiatry Diagnosis; treatment; general information
Maillard et al,101 2024 Surgery Differential diagnosis; diagnosis
Manolitsis et al,102 2023 Medicine Diagnosis; treatment
Mehnen et al,103 2023 Surgery Disease prevention; treatment; general information
Mesnier et al,104 2023 Medicine Differential diagnosis; diagnosis
Mika et al,105 2023 Medicine Diagnosis; treatment; general information
Mishra et al,106 2023 Surgery Treatment; general information
Momenaei et al,107 2023 Surgery Treatment
Moshirfar et al,108 2023 Surgery Diagnosis; treatment; general information
Musheyev et al,109 2024 Surgery General information
Nastasi et al,110 2023 Medicine Disease prevention; treatment; general information
Nielsen et al,111 2023 Medicine Differential diagnosis; diagnosis; treatment; general information
O’Hagan et al,112 2023 Surgery Diagnosis; treatment; general information
Padovan et al,113 2024 Medicine Diagnosis; treatment; general information
Pan et al,114 2023 Medicine General information
Potapenko et al,115 2023 Medicine General information
Potapenko et al,116 2023 Surgery Disease prevention; treatment; general information
Qu et al,117 2023 Surgery Disease prevention; treatment; general information
Rahsepar et al,118 2023 Surgery Differential diagnosis; diagnosis; treatment
Rao et al,119 2023 Radiology Disease prevention; screening; general information
Rao et al,120 2023 Medicine Differential diagnosis; diagnosis; treatment
Rasmussen et al,121 2023 Medicine Differential diagnosis; diagnosis; treatment
Rau et al,122 2023 Radiology Screening; general information
Rizwan et al,123 2023 Surgery Disease prevention; treatment; general information
Rogasch et al,124 2023 Radiology Diagnosis
Rojas-Carabali et al,125 2024 Medicine Diagnosis; treatment
Rosen et al,126 2023 Radiology Treatment
Rosen et al,127 2024 Surgery Differential diagnosis; diagnosis; treatment
Samaan et al,128 2023 Primary care (FM, EM) Diagnosis
Samaan et al,129 2023 Radiology Diagnosis
Schulte et al,130 2023 Medicine Disease prevention; treatment; general information
Seth et al,131 2023 Surgery General information
Seth et al,132 2023 Surgery Diagnosis; treatment
Seth et al,133 2023 Surgery General information
Sezgin et al,134 2023 Surgery Treatment; general information
Shao et al,135 2023 Surgery Diagnosis; treatment
Sorin et al,136 2023 Psychiatry General information
Stevenson et al,137 2024 Surgery Disease prevention; diagnosis; treatment; general information
Stroop et al,138 2024 Medicine Treatment
Suresh et al,139 2023 Medicine Diagnosis; general information
Szczesniewski et al,140 2023 Surgery Diagnosis; treatment; general information
Vaira et al,141 2024 Surgery Diagnosis; treatment; general information
Van Bulck et al,142 2024 Surgery Treatment; general information
Wagner et al,143 2024 Surgery Disease prevention; diagnosis; treatment; general information
Walker et al,144 2023 Medicine Disease prevention; treatment
Wang et al,145 2023 Radiology Differential diagnosis; diagnosis; treatment; general information
Wang et al,146 2023 Surgery Treatment; general information
Xie et al,147 2023 Medicine Treatment
Yeo et al,148 2023 Surgery Treatment; general information
Yildiz et al,149 2023 Surgery General information
Yun et al,150 2023 Medicine Disease prevention; diagnosis; treatment; general information
Zhou et al,151 2023 Medicine Diagnosis; treatment
Zhou et al,152 2023 Surgery Treatment; general information
Zhou et al,153 2024 Surgery Treatment
Zhu et al,154 2023 Medicine Differential diagnosis; diagnosis; treatment; general information
Zúñiga Salazar et al,155 2023 Surgery Diagnosis; treatment; general information
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Abbreviations: EM, emergency medicine; FM, family medicine.

Overall, of the 137 articles reviewed, 51 (37.2%) studies queried LLMs for advice relevant for medical topics, 55 (40.1%) on surgical topics, 13 (9.5%) on primary care topics, 9 (6.6%) on radiology topics, and 7 (5.1%) on psychiatry topics (Table 1). Articles most commonly examined advice regarding treatment (91 [66.4%]), diagnosis (60 [43.8%]), and differential diagnosis (23 [16.8%]). Overall, 16 studies (11.7%) explained why the specific LLMs were selected to be studied. Almost all studies (135 [99.3%]) assessed the ability of the OpenAI Chat Generative Pretrained Transformer (ChatGPT) to provide health advice, and 136 of the LLMs studied (99.3%) were inaccessible (proprietary/closed-source) models. A single study (0.7%) fully described the version of the LLM under evaluation, and no studies described the characteristics of the LLM in comprehensive detail including temperature (a parameter to control the randomness of LLM output), token length, fine-tuning availability, penalties, add-on availability, language, and layers related to the LLM being studied. More than one-quarter (36 [26.3%]) of the studies described none of the characteristics of the LLM being examined (Table 2). Sources used to develop prompts included expert opinion (43 [31.4%]), professional society websites (28 [20.4%]), and clinical practice guidelines (24 [17.5%]).

Table 2. Study Methods and Performance Evaluation Variables for Extraction.

Variable No. (%) (N = 137)
Specialty
Medicine 51 (37.2)21,22,24,26,28,33,35,37,39,40,41,45,59,62,64,65,68,69,70,71,72,74,76,77,78,79,80,82,83,84,87,93,101,103,104,109,110,112,113,114,119,123,128,130,136,137,142,145,148,149,152
Mixeda 1 (0.7)75
Pathology 1 (0.7)32
Primary careb 13 (9.5)6,25,27,34,38,46,48,55,58,63,96,126,155
Psychiatry 7 (5.1)53,54,57,88,89,99,134
Radiology 9 (6.6)60,66,67,118,120,122,124,127,143
Surgery 55 (40.1)13,23,30,31,36,42,43,44,47,49,50,51,52,56,61,73,81,85,86,90,91,92,94,95,97,98,100,102,105,106,107,108,111,115,117,121,125,129,131,132,133,135,138,139,140,141,144,146,147,150,151,153,154
Aim
Disease prevention 29 (21.2)6,25,26,27,34,41,46,48,51,59,61,63,66,67,89,92,102,110,115,116,118,121,128,135,141,142,148,154,155
Screening 12 (8.8)30,34,46,51,59,66,67,90,93,118,120,154
Differential diagnosis 23 (16.8)28,39,54,57,58,59,63,68,69,70,76,80,81,85,90,100,103,110,117,119,125,143,152
Diagnosis 60 (43.8)6,13,22,28,30,31,35,37,38,39,40,44,46,51,54,55,58,59,64,65,68,70,76,80,81,83,85,89,90,92,97,99,100,101,103,104,107,110,111,112,117,119,122,123,125,126,127,130,133,135,137,138,139,141,143,148,149,152,153,155
Treatment 91 (66.4)6,13,21,22,23,25,26,27,28,30,31,33,34,35,36,37,39,40,41,42,44,45,46,49,50,51,53,54,57,58,59,61,62,63,64,65,73,75,77,78,83,84,85,87,88,92,94,97,98,99,101,102,104,105,106,107,110,111,112,115,116,117,119,121,123,124,125,128,130,132,133,135,136,138,139,140,141,142,143,144,145,146,148,149,150,151,152,153,154,155
General information 85 (62.0)6,22,23,24,25,26,30,31,33,34,35,36,37,41,44,45,46,47,48,49,50,51,52,53,54,59,60,61,62,63,65,67,71,72,73,77,78,79,81,83,84,86,91,92,93,95,96,97,98,99,102,104,105,107,109,110,111,112,113,114,115,116,118,120,121,128,131,132,134,135,138,139,140,141,143,144,146,147,148,150,152,153
Selection of generative AI-driven chatbots
Follow-up on earlier study 8 (5.8)55,66,67,80,120,143,149,152
Location/availability 4 (2.9)25,31,91,118
Accessibility (open vs closed) 4 (2.9)33,37,123,147
Not described 121 (88.3)6,13,21,22,24,27,28,29,30,33,34,35,36,38,39,40,41,42,43,44,45,46,47,48,49,51,52,53,54,56,57,58,59,60,61,62,63,64,65,68,69,70,71,72,73,74,75,76,78,79,81,82,83,84,85,86,87,88,89,90,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,110,111,112,113,114,115,116,117,118,119,121,122,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,144,145,146,148,150,151,152,153,155
LLM
ChatGPT 135 (98.5)6,13,21,22,23,24,25,26,27,28,29,30,31,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,146,147,148,149,150,151,153,154
Google Bard 11 (8.0)21,22,38,45,70,92,118,132,134,137,155
Bing Chat 8 (5.8)74,100,109,114
Novel LLM 4 (2.9)50,77,97,152
Other LLM 4 (2.9)26,114,145,154
Accessibility
Accessible (open) 2 (1.5)50,77
Inaccessible (closed) 136 (99.3)6,13,21,22,23,24,25,26,27,28,29,30,31,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155
LLM characteristics
Temperature 2 (1.5)82,145
Token length 3 (2.2)97,102,122
Fine-tuning availability 3 (2.2)44,145,152
Penalties 0 (0.0)
Add-on availability 0 (0.0)
Date accessed/trained 64 (46.7)6,13,22,24,27,30,31,33,34,35,36,37,41,42,43,44,45,46,50,56,59,64,66,67,68,69,70,71,73,74,80,81,82,84,86,88,89,100,105,106,107,109,110,111,112,114,115,117,118,119,120,124,128,129,130,134,136,137,140,141,142,143,144,148,152,153
LLM versionc 1 (0.7)145
Language 10 (7.3)28,65,69,82,128,135,136,137,140,149
Layers 0 (0.0)
None described 36(26.3)21,29,39,47,49,51,52,53,57,60,62,72,76,77,78,79,83,90,91,92,94,95,96,103,104,110,113,116,121,123,126,127,133,138,147,150,151
Sources of prompts
Expert opinion 43 (31.4)21,26,27,28,30,45,50,59,60,61,64,68,70,72,75,76,77,81,98,101,104,107,108,110,113,115,117,118,121,123,124,131,132,133,135,137,138,141,142,149,150,154
Guidelines 24 (17.5)25,29,41,47,59,65,75,82,90,91,93,94,102,120,122,125,130,135,139,145,146,147,154
Professional society website 28 (20.4)22,24,25,33,34,35,36,46,48,51,56,67,69,74,78,81,84,92,97,103,122,128,129,134,146,147,148,154
Social media (patient questions) 8 (5.8)6,35,36,55,78,128,129,155
Textbook 4 (2.9)54,117,119,131
Website (non–evidence-based) 12 (8.8)24,31,33,65,72,86,95,105,109,114,119,153
Websites/forums (patient questions) 12 (8.8)6,13,31,33,36,41,49,73,83,105,117,128
None described 37 (27.0)23,37,38,39,40,42,43,44,52,53,57,58,62,63,66,71,79,80,85,87,88,89,96,99,100,106,111,112,116,126,127,136,140,143,144,151,152
Prompt engineering/testing
Yes 3 (2.2)44,81,153
No 136 (99.3)6,13,21,22,23,24,25,26,27,28,29,30,31,33,34,35,36,37,38,39,40,41,42,43,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,154,155
Standardized prompts
Yes 20 (14.6)21,22,24,29,31,33,37,43,47,60,62,81,92,100,106,114,117,125,130,153
No 119 (86.9)6,13,23,25,26,27,28,30,33,34,35,36,38,39,40,41,42,44,45,46,48,49,50,51,52,53,54,55,56,57,58,59,61,63,64,65,67,68,69,70,71,72,73,74,75,76,77,78,79,80,82,83,84,85,86,87,88,89,90,91,93,94,95,96,97,98,99,101,102,103,104,105,107,108,109,110,111,112,113,115,116,118,119,120,121,122,123,124,126,127,128,129,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,154,155
Structures of prompts
Narrative 136 (99.3)6,13,22,23,24,25,26,27,28,29,30,31,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,55,56,57,58,59,60,61,62,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155
MCQ 2 (1.5)21,54
Mixed narrative plus MCQ 1 (0.7)97
Prompt inclusion
Yes, in-text, partial 17 (12.4)6,38,43,49,50,52,67,68,69,70,75,97,102,110,134,145,155
Yes, in-text, full 44 (32.2)23,25,26,27,37,42,47,48,51,53,56,57,58,60,61,63,65,66,71,73,76,84,87,91,94,95,98,107,112,117,118,123,124,126,127,131,133,135,147,149,150,151,153,154
Yes, in supplementary file, partial 12 (8.8)33,41,83,89,93,101,110,114,119,122,136,138,144
Yes, in supplementary file, full 49(35.7)13,24,28,29,30,31,33,34,35,36,39,45,46,54,59,62,64,72,74,77,80,81,86,88,90,92,96,99,100,104,105,106,109,111,115,116,119,120,121,125,129,132,137,139,141,142,143,146,148
No 17 (12.4)21,22,40,44,55,78,79,82,85,93,103,108,113,128,130,140,152
Chatbot response inclusion
Yes, in-text, partial 29 (21.2)6,23,27,29,38,43,44,49,50,52,66,67,68,69,70,72,75,85,94,97,100,102,103,106,116,118,127,145,155
Yes, in-text, full 23 (16.8)25,47,48,53,56,57,58,61,63,65,71,84,87,91,95,98,123,126,131,133,147,149,153
Yes, in supplementary file, partial 16 (11.7)33,40,41,77,83,86,89,93,101,107,109,112,114,122,136,144
Yes, in supplementary file, full 42 (30.7)13,24,26,30,31,33,34,39,45,46,54,59,62,64,73,74,81,90,92,96,99,104,105,111,115,117,119,120,121,124,125,129,132,134,135,137,139,141,142,146,148
No 29 (21.7)21,22,28,35,36,37,42,51,55,60,76,78,79,80,82,88,93,108,110,113,128,130,138,140,143,150,151,152,154
Query strategy
Date 54 (39.4)6,13,22,27,30,33,34,35,42,45,46,47,49,50,52,55,56,58,63,64,66,69,71,72,74,81,82,84,86,88,92,99,105,106,107,108,109,110,111,112,114,117,118,119,124,130,134,136,137,140,141,143,153,154
Location 5 (3.6)47,49,50,58,72
Querierd 36 (26.3)13,24,25,27,31,33,37,41,42,44,47,48,55,58,59,60,62,68,69,70,72,75,81,88,89,100,104,110,114,118,120,129,130,131,135,136
No. of prompts/windows 32 (23.4)24,28,30,31,33,37,43,44,48,57,58,64,67,69,78,89,91,103,107,108,109,112,114,115,119,124,125,129,130,143,148
No. of users 8 (5.8)41,62,70,104,115,118,120,129
No. of queries 48 (35.0)36,46,47,51,53,58,67,69,71,76,78,81,86,88,93,94,97,99,103,105,106,107,114,115,119,121,122,124,132,134,143,146,148
Use of check queriese 3 (2.2)47,59,115
Not described 40 (29.2)21,23,26,29,38,39,40,54,61,65,73,77,79,80,83,85,87,90,95,96,98,101,102,113,116,123,126,127,128,133,138,139,142,144,145,147,149,151,152,155
Performance evaluation
No. of evaluators 97 (70.8)6,13,23,24,26,27,28,29,31,33,34,35,36,37,40,42,43,44,45,46,48,51,52,56,59,60,61,62,64,65,66,67,68,69,70,72,73,74,75,76,78,81,82,83,84,85,86,90,91,92,93,94,95,96,98,99,100,101,104,106,107,110,111,112,114,115,116,118,119,120,121,122,124,125,128,129,131,132,133,134,135,136,137,138,140,141,142,144,145,146,147,148,149,150
Randomized order 0 (0.0)
Blinding 16 (11.7)6,27,28,29,31,55,69,72,85,95,99,101,109,112,113,114
Standardization/training 18 (13.1)27,28,29,31,33,43,44,60,72,84,90,92,106,110,113,129,134,150
Not described 39 (28.4)21,22,25,30,38,39,41,47,49,50,53,54,57,58,63,71,77,79,80,87,88,89,97,102,103,105,108,110,117,123,126,127,130,139,143,151,152,153,154,155
Performance definition
Evidence summary 0 (0.0)
Evidence-based 0 (0.0)
Partially evidence-based 0 (0.0)
Evidence summary and recommendations 21 (15.3)22,40,41,44,51,65,85,90,101,109,110,114,120,122,127,130,132,139,140,145,146
Evidence-based 4 (2.9)22,51,85,139
Partially evidence-based 15 (10.9)40,44,65,90,109,110,114,120,122,127,130,132,140,144,146
Traditional textbook 1 (0.7)54
Electronic compendiumf 2 (1.5)97,119
Organization website 1 (0.7)60
Investigators without reference to source 89 (65.0)6,13,21,23,24,25,26,27,28,29,30,31,33,34,35,36,42,43,45,46,48,55,56,58,59,61,62,64,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,86,87,89,93,95,96,98,99,100,103,104,105,106,107,111,112,113,115,116,117,118,119,121,124,125,126,128,129,131,133,134,136,137,138,141,142,147,148,149,150,151,152,155
Investigator panel 3 (2.2)50,110,123
Primary article 3 (2.2)37,88,91
Not reported 14 (10.2)38,39,47,49,53,57,63,94,102,108,135,143,144,153,154
Power calculation
Yes 3 (2.2)6,55,68,76,127
No 134 (97.8)13,21,22,23,24,25,26,27,28,29,30,31,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,56,57,58,59,60,61,62,63,64,65,66,67,69,70,71,72,73,74,75,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155
Ethics 45 (32.8)6,21,25,33,34,38,43,44,46,48,54,55,56,58,59,61,65,72,73,75,78,80,87,88,89,94,98,99,100,102,113,117,122,123,125,126,127,131,132,134,138,141,145,149
Patient safety 44 (32.8)26,28,29,30,33,38,40,42,43,47,53,55,56,57,58,60,61,62,78,96,97,98,100,101,102,104,110,113,126,129,131,132,133,134,135,136,137,138,139,141,146,149,153
Regulation of LLMs 22 (16.1)13,26,27,38,46,48,58,61,70,71,78,81,82,87,94,104,110,119,120,126,127
Not reported 61 (44.5)22,23,24,31,35,36,37,39,41,45,49,50,51,52,63,64,66,67,68,69,74,76,77,79,83,84,85,86,90,91,92,93,95,103,105,106,107,108,109,111,112,114,115,116,118,121,124,128,130,140,142,143,144,147,148,150,151,152,154,155
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Abbreviations: AI, artificial intelligence; Chat GPT, OpenAI Chat Generative Pretrained Transformer; LLM, large language model; MCQ, multiple-choice question.

a

Medicine and surgery.

b

Family medicine and emergency medicine.

c

For studies using ChatGPT, 59 articles reported the model number (3.5 vs 4).

d

Individuals who performed the query.

e

UpToDate, DynaMed.

f

Check queries: repeating prompts/analysis to ensure consistency before submission for publication.

Performance Evaluation

Table 2 presents details of our study performance evaluation. More than one-quarter (37 [27.0%]) of the studies did not explain the source of their prompts or describe at least one aspect of their query strategy (40 [29.2%]). Few studies noted the date (54 [39.4%]) and location (5 [3.6%]) of their query. Study performance was suboptimal in noting the number of chat windows (32 [23.4%]) and the number of queries (48 [35.0%]). Studies performed poorly (20 [14.6%]) in using standardized prompts to evaluate their LLMs. Overall, 136 articles (99.3%) did not describe a prompt testing/engineering phase, while 93 (67.9%) shared the full transcript of their prompts and 65 (47.4%) included the full transcript of chatbot responses.

With respect to performance evaluation of chatbots, few studies described a standardized evaluation process (18 [13.1%]) or blinding (16 [11.7%]), and many studies did not describe a structured approach to performance evaluation (39 [28.5%]). Rather than using clinical practice guideline recommendations as in 21 studies (15.3%), most investigators evaluated LLMs based on their opinion without a reference standard (89 [65.0%]). Less than one-third of the studies addressed the ethical (45 [32.8%]) and patient safety (44 [32.1%]) implications of the clinical integration of LLMs, while 22 (16.1%) addressed the lack of regulation of LLMs (Table 2).

Discussion

This systematic review identified large heterogeneity in the methods of CHAS and a need for improved reporting standards. Many studies failed to adequately report the methods of chatbot performance evaluation. Moreover, most studies that described an approach to evaluation relied on anecdotal opinion rather than systematic summaries of available evidence or official guidelines. No study described the characteristics of the LLM under evaluation in sufficient detail to reproduce experiments, and many failed to describe any aspect of the LLM characteristics. Most reports did not include reasons for chatbot selection, and they seldom described a prompt engineering phase. Authors infrequently shared prompt and chatbot response transcripts. Less than half mentioned ethical issues, patient safety, or regulation of LLMs.

Reporting of LLM Characteristics and Study Method

Generative AI-driven chatbots constitute the intervention in these studies, yet there is a lack of critical information about their characteristics. As these publications are largely prepared by clinicians, study teams may not have access to expertise in machine learning. Without full knowledge of the intervention being applied, it is difficult to interpret LLM performance.9,17,156

LLMs are trained to learn associations between words in large training text datasets,2 transform algorithm inputs to outputs, and mimic human language using NLP.3 This process enables LLMs to predict the next word in a sequence, and preset model characteristics, such as temperature, token length, fine-tuning availability, penalties, add-on availability, language, and layers, further impact their functionality.157,158,159 Just as sufficient detail about medical or surgical interventions must be reported in traditional studies to reproduce experiments and guide critical appraisal, LLM characteristics are similarly vital. Physicians may not be AI experts, but they should understand the principles and limitations of their LLM interventions and may benefit from involving expert LLM researchers.160

Most included studies did not report a prompt engineering phase. However, the generated output depends greatly on the initial input. LLMs have in-context learning capabilities that allow them to adapt to the given prompt, so prompts can be intentionally designed to support performance on further tasks, such as patient communication, administrative tasks, risk assessments, or clinical decision-making.160,161 Prompt engineering improves chatbot performance by tailoring LLM output to be focused and helpful for a specific task.162,163 These crucial concepts underscore the importance of including experts in machine learning, computer science, and engineering. In the push for the clinical integration of LLMs in medicine, multidisciplinary stakeholder collaboration between physicians and expert LLM and NLP researchers is essential.164

Additionally, the existing literature base emphasizes a structured approach to performance evaluation.165 However, most studies did not provide transcripts of chatbot prompts and responses. Inclusion of obtained responses and the details of subsequent analysis is important to maximize transparency, internal validity, and the external appraisal of chatbot assessment study findings. Authors frequently apply subjective measures of chatbot performance, such as expert opinion, in CHAS, limiting the generalizability of their methods and conclusions. A shift toward objective measures, preferably basing accuracy and relevance with respect to high-quality evidence, such as clinical practice guidelines, is necessary. Many studies describe this as defining a ground truth or evaluation dataset, and it is an essential step in LLM performance evaluation.166,167 Currently, few studies use clinical practice guidelines to define successful performance, and several chatbots have experienced inconsistent accuracy by these standards.168,169 This is especially important when considering the implications of using these LLMs on patient safety.

Ethics, Patient Safety, and LLM Regulation

Artificial intelligence–linked medical devices are considered software as a medical device and are regulated under federal departments such as Health Canada and the US Food and Drug Administration.170 However, LLM applications can be excluded from this regulation if they were not designed for medical purposes or intended to replace clinical judgment.11,171,172 Although no devices that use generative AI or LLMs have been approved by the Food and Drug Administration,173 LLMs are being used in medical applications through this gap in regulation.11 Some LLMs have already been integrated into electronic medical record systems,174 such as the Nuance DAX Copilot, a generative AI-program embedded in an electronic health record software called Epic, which records patient interactions and automatically produces clinical documentation.175,176 To safely implement generative AI-driven chatbots in practice, there is a need for updated regulation that addresses the differences between emerging generative AI and previous forms of AI.11 While other regulated AI medical technologies, such as radiology image analysis177 or diagnosis,178 are trained on domain-specific medical data for targeted uses, LLMs have broader complexity, applicability, and an ability to adapt in real time, which complicates their regulation.2,179,180

Additionally, the specific issues of algorithmic bias, hallucinations, and risks of privacy breaches must be addressed by regulatory standards,181,182 as these concepts have ethical implications in patient care.183 Several examples of bias have been reported among LLMs.184 These have included the propagation of biases based on race and ethnicity, gender, educational level, social class, sexuality, and age.184,185,186 Many of these biases stem from the data used to train LLMs.184 Additionally, LLMs have been known to produce hallucinations.2,187 Large language model performance is largely influenced by the content used for pretraining and fine-tuning, yet LLMs are generally not trained exclusively with high-quality medical literature or real-world clinical data.188 While clinical practice is informed by evidence from peer-reviewed research, LLMs replicate their input data and thus may propagate online health misinformation and prejudices,189,190 compromising patient safety. Moreover, the lack of transparency surrounding the development and external validation of closed-source or proprietary LLMs presents additional challenges to their evaluation for health care purposes.179,180 Concerns have also been raised related to data privacy of patient health information among proprietary LLMs.182,191 There is a potential for misuse of patient data, medicolegal implications, and loss of physician-patient trust.181,192

Taken together, these factors raise major issues as we move from the technological development of LLMs to their clinical validation for use in health care settings.182 The World Health Organization released guidance on ethics and governance of AI for health,182 which overlaps substantially with LLMs, which are unimodal models. This document outlines key messages for stakeholders involved in the health care integration of generative AI. For instance, the World Health Organization calls for developers to commit to ethical principles of inclusiveness, use higher-quality data during model development, adhere to data protection laws, be transparent about model training data, and involve key stakeholders in the design and development of LLMs, including health care professionals, patients, laypersons, and vulnerable persons.182 Moreover, developers may focus on the explainability of their LLMs, a concept that aims to elucidate how models arrive at outputs given a specific input.193 In addition, governments should develop clear regulatory policies and legislation to capture many of the concepts discussed herein, such as target product profiles, design and development standards, audits, disclosure requirements, data protection, and public infrastructure for the development of LLMs. Regulators may further look toward incentivizing researchers toward the development of standardized metrics for the clinical validation of health care applications of generative AI models such as LLMs.193

Limitations

This systematic review has limitations. First, the reporting used in CHAS may change beyond the time of this review due to the dynamic nature of this field. By engineering a comprehensive search strategy, we aimed to capture an accurate representation of the literature. Second, much of the literature evaluated in this review used hypothetical patient cases. CHAS should focus on patient-centered, prospective research using LLMs in the clinical setting. Multidisciplinary collaboration between clinicians and LLM/NLP experts may help facilitate this, while also improving the understanding and reporting of LLM characteristics in these studies. In addition, there is a paucity of literature that comments in detail on the ethical and regulatory considerations of the clinical integration of LLMs that impact patient safety. Clinicians, patients, and regulators should be involved in collective efforts to agree and enforce appropriate deployment strategies for LLM technology in medicine.

Standardized frameworks are under development to establish detailed reporting standards to help authors design and report these studies.18 The CHART reporting tool may guide transparent reporting of CHAS and promote the publication of CHAS with higher methodologic rigor. Future research should address the lack of quality appraisal tools available to evaluate CHAS. Moreover, standardized metrics to validate the health care application of generative AI models are needed to facilitate their evaluation for adoption in clinical pathways. Interdisciplinary work among hospital managers, policymakers, regulatory bodies, and physicians of varying specialty backgrounds should be conducted to respond to the rapid advancement in LLM technology and the push for clinical integration by producing regulatory frameworks to support the safe clinical integration of LLMs.

Conclusions

In this systematic review of 137 CHAS, we noted that detailed and transparent reporting of key aspects often was not included in the studies, such as LLM characteristics, prompt engineering, query strategy, and performance evaluation. An emphasis should be placed on high-quality methods to justify the deployment of novel applications of LLMs and related technologies in clinical practice.

Supplement 1.

eAppendix. Literature Search Syntax

jamanetwopen-e2457879-s001.pdf (107.1KB, pdf)
Supplement 2.

Data Sharing Statement

jamanetwopen-e2457879-s002.pdf (14.2KB, pdf)

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Supplementary Materials

Supplement 1.

eAppendix. Literature Search Syntax

jamanetwopen-e2457879-s001.pdf (107.1KB, pdf)
Supplement 2.

Data Sharing Statement

jamanetwopen-e2457879-s002.pdf (14.2KB, pdf)

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