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
AI Tool Predicts Financial Toxicity Risk in Cancer Patients
Researchers developed a machine learning model to proactively identify cancer patients at high risk of financial stress from treatment.
•EurekAlert
Researchers Develop All-Optical Synapse for Neuromorphic Imaging Systems
A new artificial synapse, controlled entirely by light, enables in-sensor neuromorphic processing for more efficient and noise-resistant imaging systems.
•EurekAlert
AI-Simulation Approach Achieves 90% Faster Brain MRI with Minimal Data
A simulation-based AI method can reconstruct brain MRI scans with only 10% of the usual data, greatly reducing scan times.