The Large Cancer Assistant: Model-Agnostic Framework for Clinical Decision Support

ArXiv researchers published LCA, a model-agnostic orchestration framework for oncology decision support that decouples clinical logic from underlying LLM infrastructure. The system coordinates multiple models and data sources to generate treatment recommendations across cancer types.

For healthcare AI operators, this establishes a practical abstraction layer pattern: clinical workflows can function independently of which models power them, reducing lock-in and enabling rapid model substitution as performance or cost dynamics shift. This matters operationally because oncology centers face fragmented tooling—LCA's architecture signals that orchestration layers mitigate vendor dependency and allow staged deployment of specialized models rather than monolithic replacements.

Builders should expect infrastructure standardization around model-agnostic wrappers in clinical contexts. The pattern reduces friction for institutions evaluating competing models or migrating between providers. Model validation cycles compress when swap-ability is built in. This likely accelerates adoption cycles for healthcare AI by making switching costs explicit and manageable rather than implicit and organizational.