{
    "abstract": "Large language models (LLMs) have the potential for clinical decision support; however, their use in specific tasks, such as determining the RhD blood type for transfusion, remains underexplored. Therefore, we evaluated the accuracy of six LLMs in addressing RhD blood type-related issues in Korean healthcare.\nFifteen multiple-choice and true/false questions, based on real-world transfusion scenarios and reviewed by specialists, were developed. The questions were administered twice to six LLMs (Clova X, Gemini 1.0, Gemini 1.5, ChatGPT-3.5, GPT-4.0, and GPT-4o) in both Korean and English. Results were compared against the performance of 22 transfusion medicine experts. For particularly challenging questions, prompt engineering was applied, and the questions were reevaluated.\nGPT-4o demonstrated the highest accuracy rate in Korean (0.6), with significant differences compared with those of Clova X and Gemini (\nGPT-4o showed the best overall performance among the models tested and may be beneficial in RhD blood product transfusion decision-making. However, its performance suggests that it may serve best in a supportive role rather than as a primary decision-making tool.",
    "authors": [
        {
            "affiliation": "Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.",
            "firstname": "Jong Kwon",
            "initials": "JK",
            "lastname": "Lee"
        },
        {
            "affiliation": "Department of Laboratory Medicine and Genetics, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Korea.",
            "firstname": "Sooin",
            "initials": "S",
            "lastname": "Choi"
        },
        {
            "affiliation": "Department of Laboratory Medicine, Ewha Womans University College of Medicine, Seoul, Korea.",
            "firstname": "Sholhui",
            "initials": "S",
            "lastname": "Park"
        },
        {
            "affiliation": "Department of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.",
            "firstname": "Sang-Hyun",
            "initials": "SH",
            "lastname": "Hwang"
        },
        {
            "affiliation": "Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.\nDepartment of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.",
            "firstname": "Duck",
            "initials": "D",
            "lastname": "Cho"
        }
    ],
    "conclusions": null,
    "copyrights": null,
    "doi": "10.3343/alm.2024.0588",
    "journal": "Annals of laboratory medicine",
    "keywords": [
        "Clinical decision support",
        "Large language models (LLMs)",
        "RhD blood type",
        "Transfusion"
    ],
    "methods": null,
    "publication_date": "2025-04-28",
    "pubmed_id": "40289855",
    "results": "GPT-4o demonstrated the highest accuracy rate in Korean (0.6), with significant differences compared with those of Clova X and Gemini (",
    "title": "Evaluation of Six Large Language Models for Clinical Decision Support: Application in Transfusion Decision-making for RhD Blood-type Patients.",
    "xml": "<Element 'PubmedArticle' at 0x788506fac680>"
}