Large Language Model to Support Clinicians in an Oncology Department

Title:

Large Language Model to Support Clinicians in an Oncology Department

Full Description:

This project aims to design, develop, and evaluate a Large Language Model (LLM)–based clinical decision support assistant tailored for use in an oncology department. Leveraging anonymized and ethically approved cancer patient records, the system will support clinicians by providing evidence-informed insights, summaries, and recommendations that enhance clinical workflows while preserving patient safety, privacy, and professional judgment.

The proposed AI assistant will be trained and/or fine-tuned on structured and unstructured oncology data, including clinical notes, diagnostic reports, treatment protocols, laboratory results, imaging summaries, and outcomes data. Its primary role will be to assist clinicians—not replace them—by offering contextualized information at the point of care. Key functionalities include summarizing patient histories, highlighting relevant clinical trends, suggesting guideline-aligned treatment options, flagging potential drug interactions or adverse effects, and supporting differential diagnosis and follow-up planning.

A strong emphasis will be placed on data governance, anonymization, and compliance with ethical and regulatory frameworks. The model will operate exclusively on de-identified patient data, with robust access controls, audit trails, and human-in-the-loop validation mechanisms to ensure accountability and trustworthiness. Explainability and transparency will be integral, enabling clinicians to understand the rationale behind AI-generated outputs and assess their relevance to individual patient contexts.

The project will also evaluate the system’s impact on clinical efficiency, decision quality, and clinician satisfaction through pilot deployments and structured feedback. Performance metrics will include accuracy against established oncology guidelines, usability, reduction in documentation burden, and time savings in clinical decision-making.

Ultimately, this initiative seeks to demonstrate how responsibly deployed LLMs can augment oncology care by improving information access, supporting evidence-based practice, and enabling clinicians to focus more on patient-centered care. The outcomes are expected to inform scalable AI adoption strategies in specialized clinical domains, particularly within resource-constrained healthcare settings.