Patient-Readable Radiology Report Summaries Generated via Large Language Model: Safety and Quality.
| Autor: | Sterling, Nicholas W., Brann, Felix, Frisch, Stephanie O., Schrager, Justin D. |
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| Předmět: |
SAFETY
INTELLECT PATIENT education ABSTRACTING READABILITY (Literary style) COMPUTED tomography TRANSLATIONS HOSPITAL radiological services NATURAL language processing MAGNETIC resonance imaging ULTRASONIC imaging DESCRIPTIVE statistics EXPERIENCE MATHEMATICAL models X-rays RESEARCH REPORT writing THEORY QUALITY assurance COMPARATIVE studies PATIENTS' attitudes PATIENT participation |
| Zdroj: | Journal of Patient Experience; 8/1/2024, p1-4, 4p |
| Abstrakt: | Complex medical terminology utilized in clinical documentation can present barriers to patients understanding their medical findings. We aimed to generate easy-to-understand summaries of clinical radiology reports using large language models (LLMs) and evaluate their safety and quality. Eight board-certified physician reviewers evaluated 1982 LLM-generated radiology report summaries (computed tomography, magnetic resonance imaging, ultrasound, and x-ray) for safety and quality, using predefined rating criteria and the corresponding original radiology reports for reference. Physician reviewers determined 99.2% (1967 out of 1982) of the LLM-generated summaries to be safe. The reviewers scored the quality of the LLM-generated summaries from "5—Very Good" to "1—Very Poor," respectively, as follows: 80.6%, 11.1%, 5.7%, 1.7%, and 0.9%. Safety varied significantly across imaging modality (P =.002). Large language models can be used to generate safe and high-quality summaries of clinical radiology reports. Further investigation is warranted to determine the impact of LLM-generated summaries on patient perception of understanding, knowledge of their medical conditions, and overall experience. [ABSTRACT FROM AUTHOR] |
| Databáze: | Complementary Index |
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