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A retrospective workflow compiled a standardized question set, generated responses in both models, and used blinded assessment by two independent raters. Outputs were scored across clinical applicability via a 7-point Likert scale, evidence reliability and completeness with DISCERN, and readability using FRES and SMOG. Results showed strong inter-rater reliability and better applicability and information quality for DeepSeek-R1, while neither met professional information standards.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/integrating-large-language-models-into-clinical-pharmacy-education-applications-in-perioperative-medication-management-for-gastric-cancer/411098/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/integrating-large-language-models-into-clinical-pharmacy-education-applications-in-perioperative-medication-management-for-gastric-cancer/411098.png","ImageObject",300,407,{"name":92,"@type":93},"Putri","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-28",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the objective of the study?","Question",{"text":112,"@type":113},"To evaluate the performance of ChatGPT-4o and DeepSeek-R1 in perioperative medication therapy management for gastric cancer, focusing on reliability and practicality as auxiliary tools in clinical pharmacy education.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were the models assessed in the study?",{"text":117,"@type":113},"A retrospective design created a standardized question set from perioperative medication management issues, then submitted it to both models. Two blinded assessors rated outputs using a predefined framework spanning clinical applicability, information quality (DISCERN), and readability (FRES and SMOG).",{"name":119,"@type":110,"acceptedAnswer":120},"Which model performed better, and what were the key limitations?",{"text":121,"@type":113},"DeepSeek-R1 showed superior clinical applicability and information quality scores. However, neither model achieved the excellence threshold for information quality (≥65), and the generated information did not meet professional standards for drug therapy management.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},411098,1790874051,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},962085571259,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","OPEN ACCESS  \nEDITED BY  \nAntonietta G. Gravina,  \nTYPE Original Research PUBLISHED 18 December 2025 DOI 10.3389/fmed.2025.1710500  \nIntegrating large language models into clinical pharmacy education: applications in  \nUniversity of Campania Luigi Vanvitelli, Italy  \nREVIEWED BY  \nNorah Othman Abanmy,  \nKing Saud University, Saudi Arabia Elena Sblendorio,  \nUniversity of Rome Tor Vergata, Italy  \n*CORRESPONDENCE  \nQingqing Li  \n [liqing902725@163.com](liqing902725@163.com)  \nRECEIVED 22 September 2025  \nREVISED 17 November 2025  \nACCEPTED 30 November 2025  \nPUBLISHED 18 December 2025  \nperioperative medication management for gastric cancer  \nYan Wang 1, Shuwei Luan 1, Nan Shang 1, Xin Zhang 1 and Qingqing Li 2*  \n1Department of Pharmacy, First Hospital of Shanxi Medical University, The First Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi, China, 2Department of Pharmacy, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, The Third Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi, China  \nCITATION  \nWang Y, Luan S, Shang N, Zhang X and  \nLi Q (2025) Integrating large language models into clinical pharmacy education: applications in perioperative medication management for gastric cancer.  \nFront. Med. 12:1710500 .  \ndoi: 10.3389/fmed.2025.1710500  \nCOPYRIGHT  \n© 2025 Wang, Luan, Shang, Zhang and Li.  \nThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nObjective: This study aims to evaluate the performance of ChatGPT-4o and DeepSeek-R1 in perioperative medication therapy management for gastric cancer, assessing their reliability and practicality as auxiliary tools in clinical pharmacy education.  \nMethods: This study utilized a retrospective design to collate issues pertaining to perioperative medication management in gastric cancer, from which a standardized question set was developed. The set was concurrently submitted to both ChatGPT-4o and DeepSeek-R1 to generate model responses. Two independent assessors, blinded to the model sources, evaluated the outputs according to a predefined framework covering three core domains: (1) Clinical applicability, assessed via a 7-point Likert scale; (2) Information quality, evaluated using the DISCERN instrument for evidence reliability and content completeness; and (3) Readability, measured through the Flesch Reading Ease Score (FRES) and the SMOG Index.  \nResults: In the 24-item evaluation of perioperative drug therapy for gastric cancer, both models exhibited high inter-rater reliability, with Cronbach’s α values of 0.880 for DeepSeek-R1 and 0.852 for ChatGPT-4o. DeepSeek-R1 demonstrated superior performance in clinical applicability (Likert score: 5.63 ± 0.94 vs. 5.10 ± 0.78, p \u003C 0.001) and information quality (DISCERN score: 54.50 ± 6.71 vs. 50.56 ± 6.08), although neither model reached the excellence threshold (≥65 points) . Readability assessment revealed moderately complex text difficulty, with Flesch Reading Ease scores below 30 and SMOG indices indicating a reading level of ≥17 years, which remains appropriate for undergraduate clinical pharmacy education.  \nConclusion: Both ChatGPT-4o and DeepSeek-R1 have demonstrated potential in addressing issues related to perioperative medication management for gastric cancer, with their generated responses showing good practical Applicability and readability suitable for the clinical pharmacy professional community. However, it should be noted that the quality of information provided by both models does not currently meet professional standards for drug therapy managemen","cbCaimcCpMsHQMKV","https://ap.wps.com/l/cbCaimcCpMsHQMKV","pdf",451534,"English","# Objective\n# Methods\n# Results\n# Conclusion\n# Introduction","[{\"question\":\"What is the objective of the study?\",\"answer\":\"To evaluate the performance of ChatGPT-4o and DeepSeek-R1 in perioperative medication therapy management for gastric cancer, focusing on reliability and practicality as auxiliary tools in clinical pharmacy education.\"},{\"question\":\"How were the models assessed in the study?\",\"answer\":\"A retrospective design created a standardized question set from perioperative medication management issues, then submitted it to both models. Two blinded assessors rated outputs using a predefined framework spanning clinical applicability, information quality (DISCERN), and readability (FRES and SMOG).\"},{\"question\":\"Which model performed better, and what were the key limitations?\",\"answer\":\"DeepSeek-R1 showed superior clinical applicability and information quality scores. However, neither model achieved the excellence threshold for information quality (≥65), and the generated information did not meet professional standards for drug therapy management.\"}]","Integrating large language models into clinical pharmacy education: applications in perioperative medication management for gastric cancer | PDF",1790587419,25]