This paper presents an agent AI framework based on a modular task-specific agent system to address the challenges of building and deploying machine learning solutions in healthcare (fragmented preprocessing workflows, model compatibility issues, and strict data privacy constraints). The framework automates the entire clinical data pipeline from data collection to inference, automatically performing feature selection, model selection, and preprocessing recommendations without manual intervention. The system is evaluated using public datasets from gerontology, palliative care, and colonoscopy images, and examples are presented for structured data (anxiety data) and unstructured data (colonoscopy polyp data). It goes through steps such as data type detection, data anonymization, feature extraction, model-data feature matching, preprocessing recommendations and implementation, and model inference, and generates interpretable outputs using tools such as SHAP, LIME, and DETR attention maps. This reduces the need for repetitive expert intervention and provides a scalable and cost-effective path for operationalizing AI in clinical settings.
