Oncology Integration

Hybrid design for clinical data integration and expert validation.

Data Curation

De-identified data from over 500 oncology cases curated.

Model Training

Fine-tuning GPT-4 on oncology-specific guidelines and literature.

A smartphone displaying a rapid antigen test reporting platform application is placed on top of several red and white boxes labeled with 'Rapid SARS-CoV-2 Antigen Test.' The phone screen shows options for different languages and a start button for the application.
A smartphone displaying a rapid antigen test reporting platform application is placed on top of several red and white boxes labeled with 'Rapid SARS-CoV-2 Antigen Test.' The phone screen shows options for different languages and a start button for the application.
Tool Development

Web-based API assistant for clinical decision support.

Expert Validation

Incorporating expert feedback for improved decision-making accuracy.

gray computer monitor

This study will adopt a hybrid design of clinical data integration, fine-tuned LLM modeling, and expert-in-the-loop validation.

  1. Data Collection: De-identified data from over 500 interventional oncology cases will be curated, including structured EMR entries, radiology images (linked via captioning), pathology notes, and treatment outcomes.

  2. Model Training: GPT-4 will be fine-tuned on oncology-specific corpora, including NCCN/ESMO guidelines, procedure manuals, and academic literature.

  3. Tool Development: A web-based API-driven assistant will be developed. Clinicians can input patient summaries and receive decision suggestions, draft procedure reports, or translate technical plans into layman's language.

  4. Evaluation: Interventional radiologists and oncologists will review outputs for accuracy, clinical utility, and interpretability.

Integration

Combining clinical data for enhanced oncology insights.

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