Human vs machine: The future of decision-making in plastic and reconstructive surgery

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dc.contributor.authorDuran, Alpay
dc.contributor.authorDemiröz, Anıl
dc.contributor.authorÇörtük, Oğuz
dc.contributor.authorOk, Bora
dc.contributor.authorÖzten, Mustafa
dc.contributor.authorEroğlu, Sinem
dc.date.accessioned2025-01-29T12:25:47Z
dc.date.available2025-01-29T12:25:47Z
dc.date.issued2025en_US
dc.departmentFakülteler, Tıp Fakültesien_US
dc.description.abstractBackground: Artificial intelligence (AI)-driven technologies offer transformative potential in plastic surgery, spanning pre-operative planning, surgical procedures, and post-operative care, with the promise of improved patient outcomes. Objectives: To compare the web-based ChatGPT-4o (omni; OpenAI, San Francisco, CA) and Gemini Advanced (Alphabet Inc., Mountain View, CA), focusing on their data upload feature and examining outcomes before and after exposure to CME articles, particularly regarding their efficacy relative to human participants. Methods: Participants and LLMs completed 22 multiple-choice questions to assess baseline knowledge of CME topics. Initially, both LLMs and participants answered without article access. In incognito mode, the LLMs repeated the tests over 6 days. After accessing the articles, responses from both LLMs and participants were extracted and analyzed. Results: There was a significant increase in mean scores after the article was read in the resident group, indicating a significant rise. In the LLM groups, the ChatGPT-4.o (omni) group showed no significant difference between pre- and post-article scores, but the Gemini Advanced group demonstrated a significant increase. It can be stated that the ChatGPT-4.o and Gemini Advanced groups have higher accuracy means compared to the resident group in both pre and post-article periods. Conclusions: The analysis between human participants and LLMs indicates promising implications for the incorporation of LLMs in medical education. As these models increase in sophistication, they offer the potential to serve as supplementary tools within traditional learning environments. This could aid in bridging the gap between theoretical knowledge and practical implementation.en_US
dc.identifier.citationDuran, A., Demiröz, A., Çörtük, O., Ok, B., Özten, M., Eroğlu, S. (2024). Human vs machine: The future of decision-making in plastic and reconstructive surgery. Aesthetic Surgery Journal. 10.1093/asj/sjaf015en_US
dc.identifier.issn1090-820X
dc.identifier.issn1527-330X
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5157
dc.indekslendigikaynakPubMed
dc.institutionauthorEroğlu, Sinem
dc.language.isoen
dc.relation.ispartofAesthetic Surgery Journal
dc.relation.isversionof10.1093/asj/sjaf015en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPlastic Surgeryen_US
dc.subjectReconstructive Surgeryen_US
dc.titleHuman vs machine: The future of decision-making in plastic and reconstructive surgery
dc.typeArticle

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