Agentic AI Framework for End-to-End Medical Data Inference

Abstract

Developing clinical ML systems is costly and labor-intensive due to fragmented preprocessing, privacy constraints, and model-data alignment challenges. We introduce a modular agentic AI framework that automates the end-to-end ML lifecycle, from ingestion and anonymization to preprocessing, model selection, and interpretable inference. Each agent performs a well-defined task, enabling scalable workflows across structured and unstructured data. We evaluate the framework on public datasets from geriatrics, palliative care, and colonoscopy imaging. Data are automatically classified, anonymized via DLP, semantically represented, and mapped to suitable models using embedding- or LLM-based strategies. Preprocessing and inference agents ensure compatibility and produce interpretable outputs (e.g., SHAP, attention maps). By consolidating manual tasks into coordinated autonomous agents, our approach reduces expert intervention, lowers operational costs, and supports scalable clinical ML deployment.

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