Decoding medical jargon: The use of AI language models (ChatGPT-4, BARD, microsoft copilot) in radiology reports
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View abstract on PubMed
Summary
This summary is machine-generated.Artificial Intelligence (AI) language models simplify radiology reports for better patient understanding. While effective, their accuracy in urgency classification requires further development for comprehensive patient support.
Area Of Science
- Radiology and Medical Imaging
- Artificial Intelligence in Healthcare
- Health Communication
Background
- Radiology reports often contain complex medical jargon, hindering patient comprehension.
- Effective patient-provider communication is crucial for informed health decisions and engagement.
- AI language models offer a potential solution for translating complex medical information.
Purpose Of The Study
- To evaluate the effectiveness of AI language models (ChatGPT-4, BARD, Microsoft Copilot) in simplifying radiology reports.
- To assess AI-generated content for readability, understandability, and actionability.
- To determine the accuracy of AI-driven urgency classifications for radiology findings.
Main Methods
- Thirty radiology reports were processed using ChatGPT-4, BARD, and Microsoft Copilot.
- Readability was assessed using Flesch Reading Ease and Flesch-Kincaid Grade Level.
- Understandability was measured using PEMAT, and urgency classification accuracy was evaluated.
- Statistical analysis included ANOVA and Chi-Square tests to compare model performance.
Main Results
- All AI models successfully translated medical jargon into patient-friendly language.
- BARD demonstrated superior readability scores; ChatGPT-4 and BARD led in understandability (scores >70%).
- AI models showed variability in urgency recommendation accuracy, with no statistically significant differences observed.
Conclusions
- AI language models can significantly improve patient comprehension of radiology reports.
- Patient engagement in health decisions may be enhanced through simplified medical information.
- Further refinement of AI models is necessary for accurate urgency assessment and comprehensive patient support.
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