{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:03:45Z","timestamp":1792789425998,"version":"4.5.0"},"reference-count":46,"publisher":"Walter de Gruyter GmbH","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"j_cclm-2026-0435_abs_001\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Large language models (LLMs) show promise for interpreting laboratory reports, yet real-world validation remains limited. This study evaluated five advanced LLMs in interpreting urinalysis reports for kidney diseases using real-world clinical data, providing empirical evidence for the utility of LLM-assisted result interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_cclm-2026-0435_abs_002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively collected 120 urinalysis reports from patients with primary glomerular diseases and secondary nephropathies. The testing platforms included the Sysmex UF5000 and Mindray EU8600. Five LLMs (ChatGPT-5, Claude-4.5, Gemini-2.5, DeepSeek-V3.1, Qwen-3) were tasked with interpreting reports across five functional dimensions. Four certified laboratory technologists and four licensed physicians evaluated outputs using a 5-point Likert scale across six quality dimensions. Statistical analyses employed Friedman and Wilcoxon signed-rank\u00a0tests.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_cclm-2026-0435_abs_003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>All five LLMs demonstrated clinical utility in interpreting urinalysis reports. Proprietary LLMs achieved higher overall scores (Claude-4.5: 4.78\u00a0\u00b1\u00a00.47; ChatGPT-5: 4.73\u00a0\u00b1\u00a00.50; Gemini-2.5: 4.69\u00a0\u00b1\u00a00.54) compared to open-source LLMs (DeepSeek-V3.1: 4.58\u00a0\u00b1\u00a00.66; Qwen-3: 4.57\u00a0\u00b1\u00a00.69). Across functional dimensions, the models performed proficiently in identifying abnormal parameters and analyzing their correlations, but suboptimally in interpreting instrument flags. Instrument-dependent variability was observed (Sysmex vs. Mindray, p&lt;0.001). In quality assessments, Claude-4.5 exhibited the best overall performance, ChatGPT-5 excelled in accuracy and clarity, and Gemini-2.5 demonstrated strong practicality. Regarding safety, Claude-4.5 exhibited the lowest hallucination rate (7.5\u202f%). Common hallucinations included misinterpretation, definition errors, and over-interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_cclm-2026-0435_abs_004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>LLMs demonstrate significant capability in urinalysis interpretation, though proprietary models currently excel in reasoning and hallucination resistance. Instrument-specific flag interpretation and hallucination mitigation remain critical challenges requiring Retrieval-Augmented Generation (RAG) integration and human oversight.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/cclm-2026-0435","type":"journal-article","created":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T07:09:46Z","timestamp":1777100986000},"page":"1826-1837","source":"Crossref","is-referenced-by-count":2,"title":["Performance evaluation of five large language models for assisting in the interpretation of urinalysis reports for kidney diseases: a real-world study"],"prefix":"10.1515","volume":"64","author":[{"given":"Tao","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Clinical Laboratory , Yongkang Maternity and Child Care Hospital , Yongkang , Zhejiang , China"},{"name":"College of Medical Technology and Information Engineering, Zhejiang Chinese Medical University , Hangzhou , Zhejiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinglun","family":"Qi","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine , The First Affiliated Hospital, Zhejiang University School of Medicine , Hangzhou , Zhejiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohui","family":"Guo","sequence":"additional","affiliation":[{"name":"The First School of Clinical Medicine, Zhejiang Chinese Medical University , Hangzhou , Zhejiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianfei","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine , The First Affiliated Hospital, Zhejiang University School of Medicine , Hangzhou , Zhejiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lina","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine , The First Affiliated Hospital, Zhejiang University School of Medicine , Hangzhou , Zhejiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dagan","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine , The First Affiliated Hospital, Zhejiang University School of Medicine , Hangzhou , Zhejiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"2026070213034366978_j_cclm-2026-0435_ref_001","doi-asserted-by":"crossref","unstructured":"Li, Q, Zhan, L, Cai, X. 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