Autonomous Medical Imaging Pipeline Synthesis with Validation-Gated Refinement

Large language models enable increasingly autonomous machine learning workflows, but reliable end-to-end pipeline construction remains difficult in specialised domains such as medical imaging. Pipeline synthesis requires coordinated decisions over dataset interpretation, preprocessing, model design, training, and integration, while an incorrect intermediate decision can invalidate subsequent stages. We introduce MedPilot, a validation-gated agentic framework for autonomous medical imaging pipeline synthesis. Rather than generating a pipeline in a single pass, MedPilot decomposes synthesis into specialised stages and validates intermediate artefacts before they are propagated downstream. Failed candidates receive structured feedback and are iteratively repaired, while validated solutions are accumulated in an adaptive knowledge base and reused as references for related tasks. We evaluate MedPilot on six medical imaging benchmarks spanning supervised and semi-supervised segmentation and 3D classification. MedPilot achieves zero human intervention across all evaluated runs, with Dice scores of up to 0.945 on segmentation and 98.6% accuracy on classification, while consistently outperforming the evaluated baselines. These results demonstrate the effectiveness of validation-gated refinement for reliable and autonomous medical imaging pipeline synthesis.