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

--Building and deploying machine learning solutions in healthcare remains expensive and labor-intensive due to fragmented preprocessing workflows, model compatibility issues, and stringent data privacy constraints. In this work, we introduce an Agentic AI framework that automates the entire clinical data pipeline, from ingestion to inference, through a system of modular, task-specific agents. These agents are capable of handling both structured and unstructured data, enabling automatic feature selection, model selection, and preprocessing recommendation without manual intervention. We evaluate the system on publicly available datasets from geriatrics, palliative care, and colonoscopy imaging. For example, in the case of structured data (anxiety data) and unstructured data (colonoscopy polyps data), the pipeline begins with file-type detection by the "Ingestion Identifier Agent", followed by the "Data Anonymizer Agent" ensuring privacy compliance, where we first identify what type of data it is and then anonymize it. The "Feature Extraction Agent" then identifies features using an embedding-based approach for tabular data, which gives us all the column names, and a multistage MedGemma-based approach for image data, which gives us the modality and disease name. The "Preprocessing Recommender Agent" and "Preprocessing Implementor Agent" then apply tailored pre-processing based on data type and model requirements. Finally, the "Model Inference Agent" runs the selected model on the user uploaded data and generates interpretable outputs using tools like SHAP, LIME, and DETR attention maps. By automating these high-friction stages of the ML lifecycle, the proposed framework reduces the need for repeated expert intervention, offering a scalable and cost-efficient pathway for operationalizing AI in clinical environments. The integration of Artificial Intelligence (AI) into clinical workflows holds transformative potential for healthcare, enabling timely, data-driven decision-making across diagnosis and treatment planning [1].