Manual abstraction of pathology reports is one of the most time-consuming and error-prone tasks in clinical data workflows. With institutions employing dozens of specialists and spending hours per patient, the need for scalable automation is urgent.
This study presents a breakthrough framework using GPT-4o to extract structured data from unstructured thymoma pathology reports, achieving 100% accuracy across five key clinical fields, without manual annotation or rigid rule engines.
What’s Inside the Study
- 100% extraction accuracy across tumor size, diagnosis type, and staging systems
- Prompt-engineered GPT-4o model that adapts to diverse report formats and terminology
- Successfully parsed handwritten notes, narrative descriptions, and synoptic summaries
- Generalizable across six hospital systems with no customization required
- Outputs aligned with CDISC-SDTM standards for seamless integration into clinical systems
Explore the full methodology, results, and future directions for deploying LLMs in clinical data workflows. Dive into the findings: complete the form now to access your copy.
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