A Case Study Demonstrating Applications of ChatGPT in the Clinical Management of Treatment-Resistant Schizophrenia&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;

Galido, Pearl Valentine; Butala, Saloni; Chakerian, Meg; Agustines, Davin

doi:10.7759/cureus.38166

Cited by 13 publications

(16 citation statements)

References 16 publications

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“…The unreliability of LLMs may be attributed to limitations in data collection sources [58,49]. There are concerns about the model’s limitation in medical knowledge [37] since the general-purpose nature of ChatGPT may affect its reliability in self-diagnosis [3].…”

Section: Resultsmentioning

confidence: 99%

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A Systematic Review of ChatGPT and Other Conversational Large Language Models in Healthcare

Wang,

Wan,

et al. 2024

Preprint

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Background: The launch of the Chat Generative Pre-trained Transformer (ChatGPT) in November 2022 has attracted public attention and academic interest to large language models (LLMs), facilitating the emergence of many other innovative LLMs. These LLMs have been applied in various fields, including healthcare. Numerous studies have since been conducted regarding how to employ state-of-the-art LLMs in health-related scenarios to assist patients, doctors, and public health administrators. Objective: This review aims to summarize the applications and concerns of applying conversational LLMs in healthcare and provide an agenda for future research on LLMs in healthcare. Methods: We utilized PubMed, ACM, and IEEE digital libraries as primary sources for this review. We followed the guidance of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRIMSA) to screen and select peer-reviewed research articles that (1) were related to both healthcare applications and conversational LLMs and (2) were published before September 1st, 2023, the date when we started paper collection and screening. We investigated these papers and classified them according to their applications and concerns. Results: Our search initially identified 820 papers according to targeted keywords, out of which 65 papers met our criteria and were included in the review. The most popular conversational LLM was ChatGPT from OpenAI (60), followed by Bard from Google (1), Large Language Model Meta AI (LLaMA) from Meta (1), and other LLMs (5). These papers were classified into four categories in terms of their applications: 1) summarization, 2) medical knowledge inquiry, 3) prediction, and 4) administration, and four categories of concerns: 1) reliability, 2) bias, 3) privacy, and 4) public acceptability. There are 49 (75%) research papers using LLMs for summarization and/or medical knowledge inquiry, and 58 (89%) research papers expressing concerns about reliability and/or bias. We found that conversational LLMs exhibit promising results in summarization and providing medical knowledge to patients with a relatively high accuracy. However, conversational LLMs like ChatGPT are not able to provide reliable answers to complex health-related tasks that require specialized domain expertise. Additionally, no experiments in our reviewed papers have been conducted to thoughtfully examine how conversational LLMs lead to bias or privacy issues in healthcare research. Conclusions: Future studies should focus on improving the reliability of LLM applications in complex health-related tasks, as well as investigating the mechanisms of how LLM applications brought bias and privacy issues. Considering the vast accessibility of LLMs, legal, social, and technical efforts are all needed to address concerns about LLMs to promote, improve, and regularize the application of LLMs in healthcare.

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Section: Resultsmentioning

confidence: 99%

“…Diagnosis. ChatGPT has exhibited the potential to achieve high accuracy in diagnosing specific diseases [14,58], providing diagnostic suggestions in simulated situations [35,59] or using given lab reports for diagnosis [60]. ChatGPT has been evaluated in dental [6], allergy [52], and mental disorders diagnoses [61].…”

Section: Predictionmentioning

confidence: 99%

A Systematic Review of ChatGPT and Other Conversational Large Language Models in Healthcare

Wang,

Wan,

et al. 2024

Preprint

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“…In the ‘Conclusion’ sections of many studies, only a select few researched why the deployment of the LLM did not produce satisfactory results (e.g. ineffective prompt engineering) 82 . A deeper examination of failure modes and why the exercise was deemed unsuccessful or inaccurate (e.g.…”

Section: Discussionmentioning

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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“…Those tasks represented a sample of proposed LLM uses that were synthesized from the literature and included, but were not limited to, optimizing alerts for clinical decision support, providing a differential diagnosis, writing a discharge summary, recommending treatment options, translating radiology reports into layperson language, writing scientific manuscripts, and generating personalized study plans for students or trainees among others. 2 6 7 8 15 16 17 18 19 20 21 22 23 24 25 …”

Section: Methodsmentioning

confidence: 99%

A Survey of Clinicians' Views of the Utility of Large Language Models

Spotnitz,

Idnay,

Gordon

et al. 2024

Appl Clin Inform

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Objective: Large language models (LLMs) like ChatGPT are powerful algorithms that have been shown to produce human-like text from input data. A number of potential clinical applications of this technology have been proposed and evaluated by biomedical informatics experts. However, few have surveyed healthcare providers for their opinions about whether the technology is fit for use. Materials and Methods: We distributed a validated mixed-methods survey to gauge practicing clinicians’ comfort with LLMs for a breadth of tasks in clinical practice, research and education, which were selected from the literature. Results: A total of 30 clinicians fully completed the survey. Of the 23 tasks, 16 were rated positively by more than 50% of the respondents. Based on our qualitative analysis, healthcare providers considered LLMs to have excellent synthesis skills and efficiency. However, our respondents had concerns that LLMs could generate false information and propagate training data bias. Discussion: Our survey respondents were most comfortable with scenarios that allow LLMs to function in an assistive role, like a physician extender or trainee. Conclusion: In a uniquely rigorous and comprehensive mixed-methods survey of clinicians about LLM use, healthcare providers were encouraging of having LLMs in healthcare for many tasks, and especially in assistive roles. There is a need for continued human-centered development of both LLMs and artificial intelligence (AI) in general.

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A Case Study Demonstrating Applications of ChatGPT in the Clinical Management of Treatment-Resistant Schizophrenia

Cited by 13 publications

References 16 publications

A Systematic Review of ChatGPT and Other Conversational Large Language Models in Healthcare

A Systematic Review of ChatGPT and Other Conversational Large Language Models in Healthcare

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

A Survey of Clinicians' Views of the Utility of Large Language Models

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