OncoGPT: A Modular AI Assistant Orchestrating LLMs in Molecular Oncology

General-purpose large language models (LLMs) show promise for biomedical reasoning but remain ill-suited to regulated clinical workflows: they hallucinate, rely on opaque sources, and are difficult to audit—limitations incompatible with validated molecular reporting pipelines. A common response is to train or host domain-specific LLMs, yet this requires substantial data, infrastructure, and time. We present OncoGPT, a modular, provider-agnostic orchestration layer that enables the safe and auditable use of off-the-shelf LLMs in molecular oncology with minimal integration cost. A pluggable ModelSelector routes each query to on-premise or API models based on declarative capability and cost profiles, avoiding vendor lock-in and enabling model swaps by configuration rather than code. A hierarchical ContextBuilder assembles task-specific information so that outputs prioritize content from the injected context (e.g., report sections and linked references), with optional fallback to general biomedical knowledge when needed. Evaluated on 19 representative clinical prompts derived from real-world oncology reports, automatic model selection with context achieved expert acceptance across all prompts while reducing inference cost by an order of magnitude; by contrast, a fixed high-end model produced higher cost and lower expert-rated quality. These results demonstrate that a context-first, plug-and-play orchestration approach can operationalize general LLMs for traceable, cost-efficient support in precision oncology workflows—without training new domain-specific models.