{"title":"Enhancing Diagnostic Accuracy and Efficiency with GPT-4-Generated Structured Reports: A Comprehensive Study","authors":"","doi":"10.1007/s40846-024-00849-9","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <span> <h3>Purpose</h3> <p>Interpreting free-text contrast-enhanced ultrasound (CEUS) reports can lead to diagnostic errors and prolonged patient waiting times. Generative Pre-trained Transformer (GPT)-4, a state-of-the-art natural language processing model, may improve diagnostic efficiency by generating structured medical reports from unstructured data. This experimental study investigates the impact of GPT-4-generated structured reports on doctors’ diagnostic efficiency in liver nodule CEUS examinations, comparing their performance with that of doctors using conventional free-text reports.</p> </span> <span> <h3>Methods</h3> <p>A total of 159 CEUS reports were collected and structured using GPT-4, and 30 doctors of varying experience levels participated in the study. The performance of doctors using free-text reports was compared with those using structured reports in terms of diagnostic efficiency and accuracy.</p> </span> <span> <h3>Results</h3> <p>The study revealed significant improvements in diagnostic efficiency (20 vs. 17 min) and accuracy (73% vs. 79%) for doctors using GPT-4-generated structured reports compared to traditional free-text reports. This trend was consistent across all experience levels. Qualitative insights from frontline ultrasound doctors provided valuable feedback on the strengths and weaknesses of GPT-4-generated structured reports.</p> </span> <span> <h3>Conclusion</h3> <p>GPT-4-generated structured reports show potential in enhancing diagnostic efficiency and accuracy in liver nodule CEUS examinations. Despite certain limitations, refining GPT-4 or similar natural language processing models in future iterations can yield greater benefits. Future research should explore broader clinical applications and investigate GPT models and natural language processing techniques in areas such as decision support, patient communication, and medical research, ultimately contributing to improved patient care and healthcare outcomes.</p> </span> <span> <h3>Clinical Relevance</h3> <p>This study suggests that GPT-4-generated structured reports enhance diagnostic efficiency and accuracy in liver nodule CEUS examinations, potentially improving patient care and outcomes by reducing diagnostic errors and patient wait times.</p> </span>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":"10 1","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical and Biological Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s40846-024-00849-9","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose
Interpreting free-text contrast-enhanced ultrasound (CEUS) reports can lead to diagnostic errors and prolonged patient waiting times. Generative Pre-trained Transformer (GPT)-4, a state-of-the-art natural language processing model, may improve diagnostic efficiency by generating structured medical reports from unstructured data. This experimental study investigates the impact of GPT-4-generated structured reports on doctors’ diagnostic efficiency in liver nodule CEUS examinations, comparing their performance with that of doctors using conventional free-text reports.
Methods
A total of 159 CEUS reports were collected and structured using GPT-4, and 30 doctors of varying experience levels participated in the study. The performance of doctors using free-text reports was compared with those using structured reports in terms of diagnostic efficiency and accuracy.
Results
The study revealed significant improvements in diagnostic efficiency (20 vs. 17 min) and accuracy (73% vs. 79%) for doctors using GPT-4-generated structured reports compared to traditional free-text reports. This trend was consistent across all experience levels. Qualitative insights from frontline ultrasound doctors provided valuable feedback on the strengths and weaknesses of GPT-4-generated structured reports.
Conclusion
GPT-4-generated structured reports show potential in enhancing diagnostic efficiency and accuracy in liver nodule CEUS examinations. Despite certain limitations, refining GPT-4 or similar natural language processing models in future iterations can yield greater benefits. Future research should explore broader clinical applications and investigate GPT models and natural language processing techniques in areas such as decision support, patient communication, and medical research, ultimately contributing to improved patient care and healthcare outcomes.
Clinical Relevance
This study suggests that GPT-4-generated structured reports enhance diagnostic efficiency and accuracy in liver nodule CEUS examinations, potentially improving patient care and outcomes by reducing diagnostic errors and patient wait times.
期刊介绍：
The purpose of Journal of Medical and Biological Engineering, JMBE, is committed to encouraging and providing the standard of biomedical engineering. The journal is devoted to publishing papers related to clinical engineering, biomedical signals, medical imaging, bio-informatics, tissue engineering, and so on. Other than the above articles, any contributions regarding hot issues and technological developments that help reach the purpose are also included.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
