Integration
Hybrid design for clinical data and expert validation in oncology.
Oncology Integration
Hybrid design for clinical data integration and expert validation.
Data Curation
De-identified oncology cases gathering clinical and treatment outcomes.
Model Training
Fine-tuning GPT-4 on oncology-specific guidelines and literature.
Tool Development
Web-based assistant for clinician decision support and reporting.
Validation Process
Incorporating experts to ensure precision and reliability in tools.


How can large language models (LLMs) like GPT-4 be applied to support precision interventional oncology by integrating patient-specific clinical data, imaging reports, molecular profiles, and treatment protocols into personalized decision support systems?
Detailed Description:
Interventional oncology involves complex decisions requiring the synthesis of radiological findings, genetic markers, pathology reports, and evolving clinical guidelines. This project seeks to answer:
Can GPT-4 provide real-time interpretation of multi-modal diagnostic data (e.g., CT/MRI reports, genomics, biomarkers)?
Can LLMs generate individualized interventional treatment suggestions (e.g., ablation vs. embolization) based on tumor type, staging, and clinical history?
How effectively can GPT-4 assist in drafting procedure plans, patient education material, and documentation for regulatory approval?