Christoph R Buhr, Harry Smith, Tilman Huppertz, Katharina Bahr-Hamm, Christoph Matthias, Clemens Cuny, Jan Phillipp Snijders, Benjamin Philipp Ernst, Andrew Blaikie, Tom Kelsey, Sebastian Kuhn, Jonas Eckrich
{"title":"评估耳鼻喉科领域不同大型语言模型的未知潜在质量和局限性。","authors":"Christoph R Buhr, Harry Smith, Tilman Huppertz, Katharina Bahr-Hamm, Christoph Matthias, Clemens Cuny, Jan Phillipp Snijders, Benjamin Philipp Ernst, Andrew Blaikie, Tom Kelsey, Sebastian Kuhn, Jonas Eckrich","doi":"10.1080/00016489.2024.2352843","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Large Language Models (LLMs) might offer a solution for the lack of trained health personnel, particularly in low- and middle-income countries. However, their strengths and weaknesses remain unclear.</p><p><strong>Aims/objectives: </strong>Here we benchmark different LLMs (Bard 2023.07.13, Claude 2, ChatGPT 4) against six consultants in otorhinolaryngology (ORL).</p><p><strong>Material and methods: </strong>Case-based questions were extracted from literature and German state examinations. Answers from Bard 2023.07.13, Claude 2, ChatGPT 4, and six ORL consultants were rated blindly on a 6-point Likert-scale for medical adequacy, comprehensibility, coherence, and conciseness. Given answers were compared to validated answers and evaluated for hazards. A modified Turing test was performed and character counts were compared.</p><p><strong>Results: </strong>LLMs answers ranked inferior to consultants in all categories. Yet, the difference between consultants and LLMs was marginal, with the clearest disparity in conciseness and the smallest in comprehensibility. Among LLMs Claude 2 was rated best in medical adequacy and conciseness. Consultants' answers matched the validated solution in 93% (228/246), ChatGPT 4 in 85% (35/41), Claude 2 in 78% (32/41), and Bard 2023.07.13 in 59% (24/41). Answers were rated as potentially hazardous in 10% (24/246) for ChatGPT 4, 14% (34/246) for Claude 2, 19% (46/264) for Bard 2023.07.13, and 6% (71/1230) for consultants.</p><p><strong>Conclusions and significance: </strong>Despite consultants superior performance, LLMs show potential for clinical application in ORL. Future studies should assess their performance on larger scale.</p>","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessing unknown potential-quality and limitations of different large language models in the field of otorhinolaryngology.\",\"authors\":\"Christoph R Buhr, Harry Smith, Tilman Huppertz, Katharina Bahr-Hamm, Christoph Matthias, Clemens Cuny, Jan Phillipp Snijders, Benjamin Philipp Ernst, Andrew Blaikie, Tom Kelsey, Sebastian Kuhn, Jonas Eckrich\",\"doi\":\"10.1080/00016489.2024.2352843\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Large Language Models (LLMs) might offer a solution for the lack of trained health personnel, particularly in low- and middle-income countries. However, their strengths and weaknesses remain unclear.</p><p><strong>Aims/objectives: </strong>Here we benchmark different LLMs (Bard 2023.07.13, Claude 2, ChatGPT 4) against six consultants in otorhinolaryngology (ORL).</p><p><strong>Material and methods: </strong>Case-based questions were extracted from literature and German state examinations. Answers from Bard 2023.07.13, Claude 2, ChatGPT 4, and six ORL consultants were rated blindly on a 6-point Likert-scale for medical adequacy, comprehensibility, coherence, and conciseness. Given answers were compared to validated answers and evaluated for hazards. A modified Turing test was performed and character counts were compared.</p><p><strong>Results: </strong>LLMs answers ranked inferior to consultants in all categories. Yet, the difference between consultants and LLMs was marginal, with the clearest disparity in conciseness and the smallest in comprehensibility. Among LLMs Claude 2 was rated best in medical adequacy and conciseness. Consultants' answers matched the validated solution in 93% (228/246), ChatGPT 4 in 85% (35/41), Claude 2 in 78% (32/41), and Bard 2023.07.13 in 59% (24/41). Answers were rated as potentially hazardous in 10% (24/246) for ChatGPT 4, 14% (34/246) for Claude 2, 19% (46/264) for Bard 2023.07.13, and 6% (71/1230) for consultants.</p><p><strong>Conclusions and significance: </strong>Despite consultants superior performance, LLMs show potential for clinical application in ORL. 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Assessing unknown potential-quality and limitations of different large language models in the field of otorhinolaryngology.
Background: Large Language Models (LLMs) might offer a solution for the lack of trained health personnel, particularly in low- and middle-income countries. However, their strengths and weaknesses remain unclear.
Aims/objectives: Here we benchmark different LLMs (Bard 2023.07.13, Claude 2, ChatGPT 4) against six consultants in otorhinolaryngology (ORL).
Material and methods: Case-based questions were extracted from literature and German state examinations. Answers from Bard 2023.07.13, Claude 2, ChatGPT 4, and six ORL consultants were rated blindly on a 6-point Likert-scale for medical adequacy, comprehensibility, coherence, and conciseness. Given answers were compared to validated answers and evaluated for hazards. A modified Turing test was performed and character counts were compared.
Results: LLMs answers ranked inferior to consultants in all categories. Yet, the difference between consultants and LLMs was marginal, with the clearest disparity in conciseness and the smallest in comprehensibility. Among LLMs Claude 2 was rated best in medical adequacy and conciseness. Consultants' answers matched the validated solution in 93% (228/246), ChatGPT 4 in 85% (35/41), Claude 2 in 78% (32/41), and Bard 2023.07.13 in 59% (24/41). Answers were rated as potentially hazardous in 10% (24/246) for ChatGPT 4, 14% (34/246) for Claude 2, 19% (46/264) for Bard 2023.07.13, and 6% (71/1230) for consultants.
Conclusions and significance: Despite consultants superior performance, LLMs show potential for clinical application in ORL. Future studies should assess their performance on larger scale.
期刊介绍：
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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