The Large Cancer Assistant (LCA): A Model-Agnostic Orchestration Framework for Scalable Clinical Decision Support in Oncology
arXiv:2607.06531v1 Announce Type: new Abstract: - Objective: Multimodal deep learning models in oncology are currently limited by monolithic designs that rigidly couple data ingestion, clinical routing, and artificial intelligence (AI) inference. To address this inflexibility, we propose the Large Cancer Assistant (LCA), a model-agnostic, post-hoc orchestration framework designed for scalable clinical decision support. - Methods: The LCA is mathematically formalized as a 7-tuple architecture grounded in the principle of Algorithmic Impermeability, ensuring the orchestration logic remains stric
The proliferation of multimodal deep learning models in healthcare necessitates an architectural solution for their flexible integration, especially as AI advances towards more complex, multi-agent systems.
This framework addresses key limitations in current AI deployment within critical sectors like oncology, promising more scalable, adaptable, and robust clinical decision support systems.
The shift from monolithic AI models to an orchestrated, model-agnostic framework in oncology inference changes how AI solutions are deployed, maintained, and adapted in clinical settings.
- · Oncology patients
- · Clinical AI developers
- · Healthcare providers
- · AI orchestration platform providers
- · Monolithic AI system vendors
- · Healthcare IT departments resistant to new architectures
Improved accuracy and efficiency in cancer diagnosis and treatment planning due to better AI integration.
Accelerated adoption of AI in other complex medical fields as this orchestration model proves effective in oncology.
Potential for a specialized AI 'app store' or ecosystem enabling rapid deployment and swapping of best-of-breed AI components in healthcare.
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Read at arXiv cs.AI