Currently In Development

A risk-aware clinical decision support pipeline that fuses medical images with radiology notes and structured signals to produce calibrated predictions, uncertainty flags, and evidence-grounded outputs for safer triage and reporting assistance.

A standardized evaluation suite to measure and improve model performance across hospitals, scanners, and patient subgroups—supporting domain-shift testing, fairness slices, and reproducible reporting for deployment-ready medical AI.

A safety layer that detects uncertain, out-of-distribution, or artifact-corrupted cases and defers them for human review. Includes coverage–risk analysis, abstention policies, and audit-friendly logs for high-stakes clinical deployment.

A clinician-oriented explainability toolkit that goes beyond heatmaps—providing concept-based explanations, counterfactual evidence, faithfulness checks, and concise explanation report cards to support transparent and auditable medical AI.

A safety-first LLM workflow that drafts structured clinical summaries using only verified evidence (model outputs, metadata, and approved templates). Includes confidence-aware refusal, traceable citations, and guardrails for responsible use.