Advanced large language models like GPT-4 accurately identify thoracic diseases in chest CT reports, enhancing pre-operative surgical planning.
Key Details
- 1Five LLMs (GPT-4, Claude-3.5, Qwen-Max, GPT-3.5-Turbo, Gemini-Pro) compared using 13,489 real-world chest CT reports.
- 2GPT-4 achieved up to 75% accuracy in identifying 13 common chest diseases with multiple-choice prompts.
- 3Multiple-choice prompts significantly improved model accuracy compared to open-ended questions.
- 4Fine-tuning GPT-3.5-Turbo increased its accuracy from 42% to 65% in challenging cases.
- 5No single LLM was best for all diseases, suggesting a tailored approach may be optimal.
- 6Future research will use explainable AI tools to increase transparency and reliability.
Why It Matters
The study demonstrates that modern LLMs can act as accurate 'second readers' for radiology reports, possibly reducing diagnostic errors and alleviating radiologist workload. Fine-tuning and prompt design further boost performance, potentially making AI support accessible even in resource-limited settings.
Source
EurekAlert
Related News
•EurekAlert
NIH Invests Additional $12.6M in USC-Led Imaging AI for Alzheimer's
NIH has renewed and expanded its support for a USC-led consortium developing AI to decode and treat Alzheimer's using imaging and genomic data.
•EurekAlert
AI Predicts Risks for Outpatient Stem Cell Therapy in Myeloma
Researchers use machine learning to predict adverse events during stem cell therapy for multiple myeloma, improving outpatient safety.
•EurekAlert
AI-Enhanced CT Heart Fat Measurement Boosts Cardiovascular Risk Prediction
AI-derived measurement of heart fat from CT scans significantly improves long-term cardiovascular disease risk prediction.