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Comprehensive evaluation strategies are still missing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>This quantitative exploratory study aimed to develop an approach to comprehensively assess the domain-specific customised delirium LLM (DELSTAR) ability, quality and performance to accurately address complex clinical and practice research questions on delirium that typically require extensive literature searches and meta-analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>DELSTAR, focused on delirium-associated medications, was implemented as a \u2018Custom GPT\u2019 for quality assessment and as a Python-based software pipeline for performance testing on closed and leading open models. Quality metrics included statement accuracy and data credibility; performance metrics covered F1-Score, sensitivity\/specificity, precision, AUC, and AUC-ROC curves.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      DELSTAR demonstrated more accurate and comprehensive information compared to information retrieved by traditional systematic literature reviews (SLRs) (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05) and accessed Application Programmer Interfaces (API), private databases, and high-quality sources despite mainly relying on less reliable internet sources. GPT-3.5 and GPT-4o emerged as the most reliable foundation models. In Dataset 2, GPT-4o (F1-Score: 0.687) and Llama3-70b (F1-Score: 0.655) performed best, while in Dataset 3, GPT-3.5 (F1-Score: 0.708) and GPT-4o (F1-Score: 0.665) led. None consistently met desired threshold values across all metrics.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>DELSTAR demonstrated potential as a clinical pharmacy research assistant, surpassing traditional SLRs in quality. Improvements are needed in high-quality data use, citation, and performance optimisation. GPT-4o, GPT-3.5, and Llama3-70b were the most suitable foundation models, but fine-tuning DELSTAR is essential to enhance sensitivity, especially critical in pharmaceutical contexts.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11096-025-01900-8","type":"journal-article","created":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T09:49:06Z","timestamp":1744278546000},"page":"1053-1063","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Evaluating a customised large language model (DELSTAR) and its ability to address medication-related questions associated with delirium: a quantitative exploratory study"],"prefix":"10.1007","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7426-3684","authenticated-orcid":false,"given":"Katharina Teresa","family":"Spagl","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward William","family":"Watson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7235-0665","authenticated-orcid":false,"given":"Adam","family":"Jatowt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3670-2357","authenticated-orcid":false,"given":"Anita Elaine","family":"Weidmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,10]]},"reference":[{"key":"1900_CR1","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1007\/s11096-017-0547-y","volume":"39","author":"GM Kassie","year":"2017","unstructured":"Kassie GM, Kalisch Ellett LM, Nguyen TA, et al. 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