Future Innovations

A privacy-preserving foundation model trained across institutions without centralizing patient data. Focuses on federated optimization, calibration under client shift, and robust performance across sites and scanners.

Risk forecasting from serial scans to predict progression and time-to-event outcomes (e.g., glaucoma progression). Produces calibrated risk curves, uncertainty, and clinician-friendly timelines for follow-up planning.

A quality-control layer that detects motion blur, low contrast, compression, and device artifacts before inference. Routes low-quality cases for re-capture or robust enhancement to reduce silent failures in practice.

A monitoring and evaluation dashboard that reports performance, calibration, and failure modes across age/sex/site/device subgroups. Includes bias discovery, shift alerts, and standardized reporting for responsible deployment.

A self-supervised learning pipeline (contrastive/masked modeling) for data-efficient medical imaging. Targets strong transfer across modalities with minimal labels and robust generalization under dataset shift.

Counterfactual and concept-based explanations designed for clinical reasoning—testing what minimal, plausible image changes would alter predictions while tracking faithfulness and safety constraints.

A lightweight, real-time ultrasound screening pipeline optimized for low-resource clinics. Focuses on efficient architectures, quantization, and a simple operator-facing interface with uncertainty-aware alerts.