{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:44:32Z","timestamp":1791999872891,"version":"4.4.0"},"reference-count":26,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["associationofanaesthetists-publications.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Anaesthesia"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Large Language Models are emerging as powerful tools in healthcare, particularly for complex, domain\u2010specific tasks. This study describes the development and evaluation of PEri\u2010operative AI CHatbot (PEACH). It was developed by embedding 35 institutional peri\u2010operative protocols into a secure large language model environment, with iterative prompt engineering and internal testing to ensure clinical relevance and accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The system was tested with a silent deployment using real\u2010world data. Accuracy, safety and usability were assessed. Accuracy was evaluated by comparing the responses from PEACH against institutional guidelines and expert consensus. Deviations and hallucinations were categorised based on potential harm, and user feedback was evaluated using the Davis' Technology Acceptance Model. Updates to PEACH were made after the initial silent deployment to make minor amendments to one of the protocols.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In total, 240 real\u2010world clinical iterations were evaluated. First\u2010generation accuracy was 97.5% (78\/80), with an overall accuracy of 96.7% (232\/240) across three iterations. In the updated PEACH, accuracy improved to 97.9% (235\/240), with a statistically significant difference from the null hypothesis of 95% accuracy (p\u2009=\u20090.018). Hallucinations and deviations were minimal (1\/240 and 2\/240, respectively). There was high usability, with clinicians noting that PEACH expedited decisions in 95% of cases. The \u03ba statistic for inter\u2010rater reliability for PEACH was 0.772 and 0.893 between three iterations, compared with 0.610 and 0.784 for experienced peri\u2010operative physicians.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>PEACH is an accurate, adaptable tool that enhances consistency and efficiency in peri\u2010operative decision\u2010making. Future research should explore scalability across specialties and its impact on clinical outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/anae.16755","type":"journal-article","created":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T01:28:42Z","timestamp":1758331722000},"page":"62-71","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Real\u2010world deployment and evaluation of\n                    <scp>PEri<\/scp>\n                    \u2010operative\n                    <scp>AI CHatbot<\/scp>\n                    (\n                    <scp>PEACH<\/scp>\n                    ): a large language model chatbot for peri\u2010operative medicine"],"prefix":"10.1111","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7193-4749","authenticated-orcid":false,"given":"Yu He","family":"Ke","sequence":"first","affiliation":[{"name":"Department of Anaesthesiology Singapore General Hospital  Singapore"},{"name":"Data Science and Artificial Intelligence Lab Singapore General Hospital  Singapore"},{"name":"Duke\u2010NUS Medical School  Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyuan","family":"Jin","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Office Singhealth  Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kabilan","family":"Elangovan","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Office Singhealth  Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bryan Wen Xi","family":"Ong","sequence":"additional","affiliation":[{"name":"Open Government Products  Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chin Yang","family":"Oh","sequence":"additional","affiliation":[{"name":"Open Government Products  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Yang","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Anaesthesiology Singapore General Hospital  Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel Shu Wei","family":"Ting","sequence":"additional","affiliation":[{"name":"Duke\u2010NUS Medical School  Singapore"},{"name":"Artificial Intelligence Office Singhealth  Singapore"},{"name":"Department of Surgical Retina Singapore National Eye Centre  Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Liu","sequence":"additional","affiliation":[{"name":"Duke\u2010NUS Medical School  Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hairil Rizal","family":"Abdullah","sequence":"additional","affiliation":[{"name":"Department of Anaesthesiology Singapore General Hospital  Singapore"},{"name":"Data Science and Artificial Intelligence Lab Singapore General Hospital  Singapore"},{"name":"Duke\u2010NUS Medical School  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