DeepSeek-V3 and ChatGPT-4o excelled in accurately answering patient questions about interventional radiology procedures, suggesting LLMs' growing role in clinical communication.
Key Details
- 1Study evaluated four LLMs (ChatGPT-4o, DeepSeek-V3, OpenBioLLM-8b, BioMistral-7b) on 107 real-world patient questions covering TAPE, CT-guided HDR brachytherapy, and BEST.
- 2Questions and their answers were independently scored for accuracy by two board-certified radiologists using a Likert scale.
- 3DeepSeek-V3 achieved the highest mean scores for BEST (4.49) and CT-HDR (4.24), while matching ChatGPT-4o on TAPE (4.20 vs 4.17).
- 4OpenBioLLM-8b and BioMistral-7b scored significantly lower and produced potentially hazardous responses.
- 5LLMs' responses show promise for supporting—but not replacing—comprehensive medical consultations.
- 6Future studies should include patient feedback and focus on alignment with clinical guidelines.
Why It Matters
Accurate, scalable patient education via AI could improve understanding and consent for complex interventional radiology procedures, potentially enhancing workflow efficiency and patient outcomes. However, inconsistent model performance highlights the need for careful integration and validation.
Source
AuntMinnie
Related News
•AuntMinnie
Study: Computer Vision Models Best LLMs in Chest CT Breast Abnormality Detection
Computer vision models (CVMs) surpass large language models (LLMs) in accurately labeling incidental breast abnormalities on chest CT scans.
•Cardiovascular Business
Radiology Maintains Lead in FDA-Cleared AI Algorithms, Cardiology Follows
Radiology remains the top specialty for FDA-cleared AI, with cardiology as a strong second, particularly in cardiovascular imaging.
•AuntMinnie
Deep Learning Models Rival Radiologists for Pancreatic Cancer Detection on CT
Deep-learning models achieved comparable or superior accuracy to experienced radiologists in detecting pancreatic cancer on CT scans, especially for small tumors.