ChatGPT-4 demonstrated near-perfect accuracy in extracting clinical variables from MRI and CT scans of pancreatic cyst patients, equalling manual chart review by radiologists.
Key Details
- 1Study involved 991 patients with 3,198 MRI and CT scans from Memorial Sloan Kettering (2010–2024).
- 2ChatGPT-4 extracted 9 critical clinical variables with accuracy rates of 92%–99%.
- 3AI matched the manual approach, considered the gold standard, for classifying high-risk variables such as cyst size and solid component presence.
- 4Efficiency and cost-effectiveness cited as major advantages of the AI approach.
- 5The study was published in the Journal of the American College of Surgeons and NIH/NCI partially funded the work.
Why It Matters
Demonstrating LLMs’ ability to accurately extract structured data from radiology imaging reports could streamline large-scale research and surveillance tasks, reducing costs and improving patient care efficiency. This proof-of-concept signals the increasing feasibility and trustworthiness of leveraging advanced AI models in radiological workflows.
Source
EurekAlert
Related News
•EurekAlert
Advancements in CRC Screening: Imaging, AI, and Point-of-Care Diagnostics
Recent innovations in colorectal cancer screening include advanced imaging, AI tools, and novel diagnostics to improve early detection and outcomes.
•EurekAlert
AI Model Improves Prediction of Knee Osteoarthritis Progression Using MRI and Biomarkers
A new AI-assisted model that combines MRI, biochemical, and clinical data improves predictions of worsening knee osteoarthritis.
•EurekAlert
AI Trains on Pathologists’ Eye Movements to Improve Biopsy Analysis
Researchers developed a deep learning system using eye-tracking data to enhance AI-powered biopsy image interpretation.