Large Language Model Influence on Diagnostic Reasoning

JAMA Network Open, Год журнала: 2024, Номер 7(10), С. e2440969 - e2440969

Опубликована: Окт. 28, 2024

Importance Large language models (LLMs) have shown promise in their performance on both multiple-choice and open-ended medical reasoning examinations, but it remains unknown whether the use of such tools improves physician diagnostic reasoning. Objective To assess effect an LLM physicians’ compared with conventional resources. Design, Setting, Participants A single-blind randomized clinical trial was conducted from November 29 to December 29, 2023. Using remote video conferencing in-person participation across multiple academic institutions, physicians training family medicine, internal or emergency medicine were recruited. Intervention either access addition resources only, stratified by career stage. allocated 60 minutes review up 6 vignettes. Main Outcomes Measures The primary outcome a standardized rubric based differential diagnosis accuracy, appropriateness supporting opposing factors, next evaluation steps, validated graded via blinded expert consensus. Secondary outcomes included time spent per case (in seconds) final accuracy. All analyses followed intention-to-treat principle. secondary exploratory analysis evaluated standalone comparing between alone group resource group. Results Fifty (26 attendings, 24 residents; median years practice, 3 [IQR, 2-8]) participated virtually as well at 1 site. score 76% (IQR, 66%-87%) for 74% 63%-84%) resources-only group, adjusted difference 2 percentage points (95% CI, −4 8 points; P = .60). 519 371-668) seconds, 565 456-788) seconds −82 −195 31; .20) seconds. scored 16 2-30 .03) higher than Conclusions Relevance In this trial, availability aid did not significantly improve demonstrated groups, indicating need technology workforce development realize potential physician-artificial intelligence collaboration practice. Trial Registration ClinicalTrials.gov Identifier: NCT06157944

Язык: Английский

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Large Language Models in Healthcare and Medical Domain: A Review

Informatics, Год журнала: 2024, Номер 11(3), С. 57 - 57

Опубликована: Авг. 7, 2024

The deployment of large language models (LLMs) within the healthcare sector has sparked both enthusiasm and apprehension. These exhibit remarkable ability to provide proficient responses free-text queries, demonstrating a nuanced understanding professional medical knowledge. This comprehensive survey delves into functionalities existing LLMs designed for applications elucidates trajectory their development, starting with traditional Pretrained Language Models (PLMs) then moving present state in sector. First, we explore potential amplify efficiency effectiveness diverse applications, particularly focusing on clinical tasks. tasks encompass wide spectrum, ranging from named entity recognition relation extraction natural inference, multimodal document classification, question-answering. Additionally, conduct an extensive comparison most recent state-of-the-art domain, while also assessing utilization various open-source highlighting significance applications. Furthermore, essential performance metrics employed evaluate biomedical shedding light limitations. Finally, summarize prominent challenges constraints faced by offering holistic perspective benefits shortcomings. review provides exploration current landscape healthcare, addressing role transforming areas that warrant further research development.

Язык: Английский

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A Survey of Large Language Models for Healthcare: From Data, Technology, and Applications to Accountability and Ethics

Опубликована: Янв. 1, 2024

Язык: Английский

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The TRIPOD-LLM reporting guideline for studies using large language models

Nature Medicine, Год журнала: 2025, Номер unknown

Опубликована: Янв. 8, 2025

Язык: Английский

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A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics

Information Fusion, Год журнала: 2025, Номер unknown, С. 102963 - 102963

Опубликована: Янв. 1, 2025

Язык: Английский

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Open-Source Large Language Models in Radiology: A Review and Tutorial for Practical Research and Clinical Deployment

Radiology, Год журнала: 2025, Номер 314(1)

Опубликована: Янв. 1, 2025

Open-source large language models and multimodal foundation offer several practical advantages for clinical research objectives in radiology over their proprietary counterparts but require further validation before widespread adoption.

Язык: Английский

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Use of AI in Cardiac CT and MRI: A Scientific Statement from the ESCR, EuSoMII, NASCI, SCCT, SCMR, SIIM, and RSNA

Radiology, Год журнала: 2025, Номер 314(1)

Опубликована: Янв. 1, 2025

Artificial intelligence (AI) offers promising solutions for many steps of the cardiac imaging workflow, from patient and test selection through image acquisition, reconstruction, interpretation, extending to prognostication reporting. Despite development AI algorithms, tools are at various stages face challenges clinical implementation. This scientific statement, endorsed by several societies in field, provides an overview current landscape applications CT MRI. Each section is organized into questions statements that address key including ethical, legal, environmental sustainability considerations. A technology readiness level range 1 9 summarizes maturity reflects progression preliminary research document aims bridge gap between burgeoning developments limited

Язык: Английский

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Leveraging large language models to foster equity in healthcare

Journal of the American Medical Informatics Association, Год журнала: 2024, Номер 31(9), С. 2147 - 2150

Опубликована: Март 20, 2024

Abstract Objectives Large language models (LLMs) are poised to change care delivery, but their impact on health equity is unclear. While marginalized populations have been historically excluded from early technology developments, LLMs present an opportunity our approach developing, evaluating, and implementing new technologies. In this perspective, we describe the role of in supporting equity. Materials Methods We apply National Institute Minority Health Disparities (NIMHD) research framework explore use for Results opportunities how can improve across individual, family organizational, community, population health. emerging concerns including biased data, limited diffusion, privacy. Finally, highlight recommendations focused prompt engineering, retrieval augmentation, digital inclusion, transparency, bias mitigation. Conclusion The potential support depends making a focus start.

Язык: Английский

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Exploring the Role of ChatGPT-4, BingAI, and Gemini as Virtual Consultants to Educate Families about Retinopathy of Prematurity

Children, Год журнала: 2024, Номер 11(6), С. 750 - 750

Опубликована: Июнь 20, 2024

Large language models (LLMs) are becoming increasingly important as they being used more frequently for providing medical information. Our aim is to evaluate the effectiveness of electronic artificial intelligence (AI) large (LLMs), such ChatGPT-4, BingAI, and Gemini in responding patient inquiries about retinopathy prematurity (ROP).

Язык: Английский

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A toolbox for surfacing health equity harms and biases in large language models

Nature Medicine, Год журнала: 2024, Номер unknown

Опубликована: Сен. 23, 2024

Язык: Английский

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