{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T02:28:55Z","timestamp":1786328935942,"version":"build-2736575974"},"reference-count":38,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T00:00:00Z","timestamp":1778025600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T00:00:00Z","timestamp":1778025600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Vox Sanguinis"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Objectives<\/jats:title>\n                    <jats:p>Large language models (LLMs) show promise for clinical decision support but remain vulnerable to factual errors. Retrieval\u2010augmented generation (RAG) mitigates this limitation by grounding outputs in authoritative domain knowledge. Therapeutic plasma exchange (TPE) requires consistent, guideline\u2010driven decisions based on the 2023 American Society for Apheresis (ASFA) recommendations. This study aimed to evaluate whether an RAG\u2010based framework could improve accuracy, reliability and standardization of decision support for TPE, compared to conventional LLMs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We built a hybrid RAG pipeline combining BAAI\/bge\u2010base\u2010en\u2010v1.5 embeddings with Chroma and BM25, coupled with structured prompts that encode ASFA categories and grades, Health Insurance Review and Assessment (HIRA) service criteria, and plasma volume computation rules. Thirty de\u2010identified real\u2010world consultation cases were converted into standardized queries. Across six RAG and three non\u2010RAG generative pre\u2010trained transformer (GPT)\u2010series model configurations, each case was answered five times (1,350 outputs). Performance was assessed by item\u2010level accuracy for six elements (diagnosis, ASFA category, grade, insurance applicability, plasma volume, and replacement fluid) and reproducibility on 14 disease\u2010name prompts. Response time and output length were also analyzed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>RAG configurations consistently outperformed non\u2010RAG baselines across items, with the largest gains in plasma\u2010volume calculation and ASFA classification. Reproducibility was markedly higher with RAG across repeated runs. Among all configurations, RAG GPT\u20104.1\u2010mini showed the most balanced and superior performance, delivering high accuracy with low latency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A guideline\u2010grounded RAG approach substantially enhances the accuracy, stability and standardization of TPE consultation compared with conventional LLMs. This RAG\u2010TPE framework demonstrates the feasibility of reliable, clinically oriented decision support in transfusion medicine, warranting further evaluation in prospective clinical workflows.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/vox.70280","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T01:55:26Z","timestamp":1778118926000},"page":"1192-1200","source":"Crossref","is-referenced-by-count":0,"title":["An automatic consult reply system for therapeutic plasma exchange using retrieval\u2010augmented generation"],"prefix":"10.1111","volume":"121","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6230-1242","authenticated-orcid":false,"given":"Jong Kwon","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Laboratory Medicine Hallym University Sacred Heart Hospital, Hallym University College of Medicine  Anyang Republic of Korea"},{"name":"Department of Laboratory Medicine and Genetics, Samsung Medical Center Sungkyunkwan University School of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sooin","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine and Genetics Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine  Bucheon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6467-427X","authenticated-orcid":false,"given":"Sholhui","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine Ewha Womans University School of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qute","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine Chungnam National University Sejong Hospital  Sejong Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3201-5728","authenticated-orcid":false,"given":"Sang\u2010Hyun","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, Asan Medical Center University of Ulsan College of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6861-3282","authenticated-orcid":false,"given":"Duck","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine and Genetics, Samsung Medical Center Sungkyunkwan University School of Medicine  Seoul Republic of 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