Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-10079
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dc.contributor.authorBuhr, Christoph Raphael-
dc.contributor.authorSmith, Harry-
dc.contributor.authorHuppertz, Tilman-
dc.contributor.authorBahr-Hamm, Katharina-
dc.contributor.authorMatthias, Christoph-
dc.contributor.authorBlaikie, Andrew-
dc.contributor.authorKelsey, Tom-
dc.contributor.authorKuhn, Sebastian-
dc.contributor.authorEckrich, Jonas-
dc.date.accessioned2024-02-20T08:41:15Z-
dc.date.available2024-02-20T08:41:15Z-
dc.date.issued2023-
dc.identifier.urihttps://openscience.ub.uni-mainz.de/handle/20.500.12030/10097-
dc.description.abstractBackground: Large language models (LLMs), such as ChatGPT (Open AI), are increasingly used in medicine and supplement standard search engines as information sources. This leads to more “consultations” of LLMs about personal medical symptoms. Objective: This study aims to evaluate ChatGPT’s performance in answering clinical case–based questions in otorhinolaryngology (ORL) in comparison to ORL consultants’ answers. Methods: We used 41 case-based questions from established ORL study books and past German state examinations for doctors. The questions were answered by both ORL consultants and ChatGPT 3. ORL consultants rated all responses, except their own, on medical adequacy, conciseness, coherence, and comprehensibility using a 6-point Likert scale. They also identified (in a blinded setting) if the answer was created by an ORL consultant or ChatGPT. Additionally, the character count was compared. Due to the rapidly evolving pace of technology, a comparison between responses generated by ChatGPT 3 and ChatGPT 4 was included to give an insight into the evolving potential of LLMs. Results: Ratings in all categories were significantly higher for ORL consultants (P<.001). Although inferior to the scores of the ORL consultants, ChatGPT’s scores were relatively higher in semantic categories (conciseness, coherence, and comprehensibility) compared to medical adequacy. ORL consultants identified ChatGPT as the source correctly in 98.4% (121/123) of cases. ChatGPT’s answers had a significantly higher character count compared to ORL consultants (P<.001). Comparison between responses generated by ChatGPT 3 and ChatGPT 4 showed a slight improvement in medical accuracy as well as a better coherence of the answers provided. Contrarily, neither the conciseness (P=.06) nor the comprehensibility (P=.08) improved significantly despite the significant increase in the mean amount of characters by 52.5% (n= (1470-964)/964; P<.001). Conclusions: While ChatGPT provided longer answers to medical problems, medical adequacy and conciseness were significantly lower compared to ORL consultants’ answers. LLMs have potential as augmentative tools for medical care, but their “consultation” for medical problems carries a high risk of misinformation as their high semantic quality may mask contextual deficits.en_GB
dc.language.isoengde
dc.rightsCC BY*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subject.ddc610 Medizinde_DE
dc.subject.ddc610 Medical sciencesen_GB
dc.titleChatGPT versus consultants : blinded evaluation on answering otorhinolaryngology case–based questionsen_GB
dc.typeZeitschriftenaufsatzde
dc.identifier.doihttp://doi.org/10.25358/openscience-10079-
jgu.type.contenttypeScientific articlede
jgu.type.dinitypearticleen_GB
jgu.type.versionPublished versionde
jgu.type.resourceTextde
jgu.organisation.departmentFB 04 Medizinde
jgu.organisation.number2700-
jgu.organisation.nameJohannes Gutenberg-Universität Mainz-
jgu.rights.accessrightsopenAccess-
jgu.journal.titleJMIR medical educationde
jgu.journal.volume9de
jgu.pages.alternativee49183de
jgu.publisher.year2023-
jgu.publisher.nameJMIR Publicationsde
jgu.publisher.placeTorontode
jgu.publisher.issn2369-3762de
jgu.organisation.placeMainz-
jgu.subject.ddccode610de
jgu.publisher.doi10.2196/49183de
jgu.organisation.rorhttps://ror.org/023b0x485-
jgu.subject.dfgLebenswissenschaftende
Appears in collections:DFG-491381577-G

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