Large language models in patient education: a scoping review of applications in medicine

Frontiers in Medicine, Journal Year: 2024, Volume and Issue: 11

Published: Oct. 29, 2024

Large Language Models (LLMs) are sophisticated algorithms that analyze and generate vast amounts of textual data, mimicking human communication. Notable LLMs include GPT-4o by Open AI, Claude 3.5 Sonnet Anthropic, Gemini Google. This scoping review aims to synthesize the current applications potential uses in patient education engagement.

Language: Английский

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ChatGPT-4 Omni’s superiority in answering multiple-choice oral radiology questions

BMC Oral Health, Journal Year: 2025, Volume and Issue: 25(1)

Published: Feb. 1, 2025

This study evaluates and compares the performance of ChatGPT-3.5, ChatGPT-4 Omni (4o), Google Bard, Microsoft Copilot in responding to text-based multiple-choice questions related oral radiology, as featured Dental Specialty Admission Exam conducted Türkiye. A collection was sourced from open-access question bank Turkish Exam, covering years 2012 2021. The included 123 questions, each with five options one correct answer. accuracy levels ChatGPT-4o, were compared using descriptive statistics, Kruskal-Wallis test, Dunn's post hoc Cochran's Q test. responses generated by four chatbots exhibited statistically significant differences (p = 0.000). ChatGPT-4o achieved highest at 86.1%, followed Bard 61.8%. ChatGPT-3.5 demonstrated an rate 43.9%, while recorded a 41.5%. showcases superior advanced reasoning capabilities, positioning it promising educational tool. With regular updates, has potential serve reliable source information for both healthcare professionals general public. Not applicable.

Language: Английский

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Tailoring glaucoma education using large language models: Addressing health disparities in patient comprehension

Medicine, Journal Year: 2025, Volume and Issue: 104(2), P. e41059 - e41059

Published: Jan. 10, 2025

This study evaluates the efficacy of GPT-4, a Large Language Model, in simplifying medical literature for enhancing patient comprehension glaucoma care. GPT-4 was used to transform published abstracts from 3 journals (n = 62) and education materials (Patient Educational Model [PEMs], n 9) 5th-grade reading level. also prompted generate de novo educational outputs at 6 different levels (5th Grade, 8th High School, Associate’s, Bachelor’s Doctorate). Readability both transformed quantified using Flesch Kincaid Grade Level (FKGL) Reading Ease (FKRE) Score. Latent semantic analysis (LSA) cosine similarity applied assess content consistency materials. The transformation resulted FKGL decreasing by an average 3.21 points (30%, P < .001) FKRE increasing 28.6 (66%, .001). For PEMs, decreased 2.38 (28%, .0272) increased 12.14 (19%, .0459). LSA revealed high consistency, with 0.861 across all 0.937 signifying topical themes were quantitatively shown be consistent. shows that effectively simplifies information about glaucoma, making it more accessible while maintaining textual content. improved readability scores generated demonstrate its usefulness levels.

Language: Английский

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Comparative Efficacy of AI LLMs in Clinical Social Work: ChatGPT-4, Gemini, Copilot

Research on Social Work Practice, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 17, 2025

Purpose This study examines the comparative efficacy of three AI large language models (LLMs)—ChatGPT-4, Gemini, and Microsoft Copilot—in clinical social work. Method By presenting scenarios varying complexities, assessed their performance using Ateşman Readability Index a Likert-type accuracy scale. Results showed that Gemini had highest accuracy, while Copilot excelled in readability. Significant differences were found scores ( p = .003), although readability not statistically significant .054). No correlation was between case complexity either or Discussion Despite differences, none fully met all standards, indicating areas for further improvement. The findings suggest LLMs offer promise work, they require refinement to better meet field's needs.

Language: Английский

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Do people prefer AI-generated patient educational materials over traditional ones?

Patient Education and Counseling, Journal Year: 2025, Volume and Issue: 134, P. 108672 - 108672

Published: Jan. 20, 2025

Language: Английский

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Assessing the performance of Microsoft Copilot, GPT-4 and Google Gemini in ophthalmology

Canadian Journal of Ophthalmology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

To evaluate the performance of large language models (LLMs), specifically Microsoft Copilot, GPT-4 (GPT-4o and GPT-4o mini), Google Gemini (Gemini Advanced), in answering ophthalmological questions assessing impact prompting techniques on their accuracy. Prospective qualitative study. Advanced). A total 300 from StatPearls were tested, covering a range subspecialties image-based tasks. Each question was evaluated using 2 techniques: zero-shot forced (prompt 1) combined role-based plan-and-solve+ 2). With prompting, demonstrated significantly superior overall performance, correctly 72.3% outperforming all other models, including Copilot (53.7%), mini (62.0%), (54.3%), Advanced (62.0%) (p < 0.0001). Both showed notable improvements with Prompt over 1, elevating Copilot's accuracy lowest (53.7%) to second highest (72.3%) among LLMs. While newer iterations LLMs, such as Advanced, outperformed less advanced counterparts Gemini), this study emphasizes need for caution clinical applications these models. The choice influences highlighting necessity further research refine LLMs capabilities, particularly visual data interpretation, ensure safe integration into medical practice.

Language: Английский

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Application of GenAI in Clinical Administration Support

Published: Jan. 1, 2025

Language: Английский

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Artificial Intelligence-Based Chatbots’ Ability to Interpret Mammography Images: A Comparison of Chat-GPT 4o and Claude 3.5

European Journal of Therapeutics, Journal Year: 2025, Volume and Issue: 31(1), P. 28 - 34

Published: Feb. 28, 2025

Objectives: The aim of this study is to compare the ability artificial intelligence-based chatbots, ChatGPT-4o and Claude 3.5, interpret mammography images. focuses on evaluating their accuracy consistency in BI-RADS classification breast parenchymal type assessment. It also aims explore potential these technologies reduce radiologists’ workload identify limitations medical image analysis. Methods: A total 53 images obtained between January July 2024 were analyzed, focusing same anonymized provided both chatbots under identical prompts. Results: results showed rates for ranging from 18.87% 26.42% 18.7% 3.5. When categories grouped into benign group(BI-RADS 1,2) malignant 4,5), combined was 57.5% (initial evaluation) 55% (second evaluation), compared 47.5% Breast 30.19% 22.64% ChatGPT-4o, Conclusions: findings indicate that demonstrate limited reliability interpreting These highlight need further optimization, larger datasets, advanced training processes improve performance

Language: Английский

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Assessing the performance of large language models (GPT-3.5 and GPT-4) and accurate clinical information for pediatric nephrology

Pediatric Nephrology, Journal Year: 2025, Volume and Issue: unknown

Published: March 5, 2025

Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering significant advancements providing accurate clinical information. However, the performance and applicability of AI models specialized fields such pediatric nephrology remain underexplored. This study is aimed at evaluating ability two AI-based language models, GPT-3.5 GPT-4, to provide reliable information nephrology. The were evaluated on four criteria: accuracy, scope, patient friendliness, applicability. Forty specialists with ≥ 5 years experience rated GPT-4 responses 10 questions using 1-5 scale via Google Forms. Ethical approval was obtained, informed consent secured from all participants. Both demonstrated comparable across criteria, no statistically differences observed (p > 0.05). exhibited slightly higher mean scores parameters, but negligible (Cohen's d < 0.1 for criteria). Reliability analysis revealed low internal consistency both (Cronbach's alpha ranged between 0.019 0.162). Correlation indicated relationship participants' professional their evaluations (correlation coefficients - 0.026 0.074). While provided foundational level support, neither model superior addressing unique challenges findings highlight need domain-specific training integration updated guidelines enhance reliability fields. underscores potential while emphasizing importance human oversight further refinements applications.

Language: Английский

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Leveraging artificial intelligence chatbots for anemia prevention: A comparative study of ChatGPT-3.5, copilot, and Gemini outputs against Google Search results

PEC Innovation, Journal Year: 2025, Volume and Issue: 6, P. 100390 - 100390

Published: April 5, 2025

This study evaluated the understandability, actionability, and readability of text on anemia generated by artificial intelligence (AI) chatbots. cross-sectional compared texts ChatGPT-3.5, Microsoft Copilot, Google Gemini at three levels: "normal," "6th grade," "PEMAT-P version." Additionally, retrieved from top eight Search results for relevant keywords were included comparison. All written in Japanese. The Japanese version PEMAT-P was used to assess understandability while jReadability readability. A systematic comparison conducted identify strengths weaknesses each source. Texts 6th-grade level (n = 26, 86.7 %) 27, 90.0 %), as well ChatGPT-3.5 normal 21, 80.8 achieved significantly higher scores (≥70 actionability 17, 25.4 %, p < 0.001). For readability, Copilot demonstrated percentages "very readable" "somewhat difficult" levels than (p 0.000-0.007). is first objectively quantitatively evaluate educational materials prevention. By utilizing jReadability, superiority terms through measurable data. innovative approach highlights potential AI chatbots a novel method providing public health information addressing disparities. AI-generated found be more readable easier understand traditional web-based texts, with demonstrating highest understandability. Moving forward, improvements prompts will necessary enhance integration visual elements that encourage actionable responses

Language: Английский

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