{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T23:09:46Z","timestamp":1793660986092,"version":"4.5.1"},"reference-count":33,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T00:00:00Z","timestamp":1709078400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The International Journal of Lower Extremity Wounds"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:p>Type 2 diabetes is a significant global health concern. It often causes diabetic foot ulcers (DFUs), which affect millions of people and increase amputation and mortality rates. Despite existing guidelines, the complexity of DFU treatment makes clinical decisions challenging. Large language models such as chat generative pretrained transformer (ChatGPT), which are adept at natural language processing, have emerged as valuable resources in the medical field. However, concerns about the accuracy and reliability of the information they provide remain. We aimed to assess the accuracy of various artificial intelligence (AI) chatbots, including ChatGPT, in providing information on DFUs based on established guidelines. Seven AI chatbots were asked clinical questions (CQs) based on the DFU guidelines. Their responses were analyzed for accuracy in terms of answers to CQs, grade of recommendation, level of evidence, and agreement with the reference, including verification of the authenticity of the references provided by the chatbots. The AI chatbots showed a mean accuracy of 91.2% in answers to CQs, with discrepancies noted in grade of recommendation and level of evidence. Claude-2 outperformed other chatbots in the number of verified references (99.6%), whereas ChatGPT had the lowest rate of reference authenticity (66.3%). This study highlights the potential of AI chatbots as tools for disseminating medical information and demonstrates their high degree of accuracy in answering CQs related to DFUs. However, the variability in the accuracy of these chatbots and problems like AI hallucinations necessitate cautious use and further optimization for medical applications. This study underscores the evolving role of AI in healthcare and the importance of refining these technologies for effective use in clinical decision-making and patient education.<\/jats:p>","DOI":"10.1177\/15347346241236811","type":"journal-article","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T02:47:15Z","timestamp":1709174835000},"page":"462-468","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":23,"title":["Appropriateness of Artificial Intelligence Chatbots in Diabetic Foot Ulcer Management"],"prefix":"10.1177","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3734-2085","authenticated-orcid":false,"given":"Makoto","family":"Shiraishi","sequence":"first","affiliation":[{"name":"The University of Tokyo Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haesu","family":"Lee","sequence":"additional","affiliation":[{"name":"The University of Tokyo Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koji","family":"Kanayama","sequence":"additional","affiliation":[{"name":"The University of Tokyo Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuta","family":"Moriwaki","sequence":"additional","affiliation":[{"name":"The University of Tokyo Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mutsumi","family":"Okazaki","sequence":"additional","affiliation":[{"name":"The University of Tokyo Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/414782a"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2023.10578"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1177\/1534734611428728"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1177\/15347346211014392"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1177\/15347346221109057"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1080\/07853890.2016.1231932"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-019-8133-y"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.2337\/dc19-1614"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1111\/iwj.13540"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvs.2015.10.003"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000198"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1002\/ctm2.1216"},{"key":"e_1_3_2_14_2","article-title":"Deep reinforcement learning from human preferences","volume":"30","author":"Christiano PF","year":"2017","unstructured":"Christiano PF, Leike J, Brown T, Martic M, Legg S, Amodei D. 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