Security-first medical AI infrastructure built around evidence-bounded deployment controls.
- MedSigLIP V0 integration: Medical image/text embedding architecture suitable for data-efficient classification, zero-shot classification, and semantic image retrieval; text generation is outside the intended model role.
- Maximum Efficiency & Security Protocol (Sep 2026): Ultra-PEFT design using rank-4 Q/V-only LoRA or 8-token prompt tuning, with frozen encoders and strict pre-embedding de-identification. Differential-privacy controls, attested-enclave inference, and encrypted vector storage are deployment-dependent or optional controls.
- SecureFreshDiskANN: Proposed optional persistent vector-storage layer for confidential embeddings, designed for encrypted storage and retrieval within an attested or air-gapped boundary.
- Offline / air-gapped deployment: Architecture supports deployments where external network access is unavailable or prohibited, subject to local availability of all required dependencies.
- Validation-gated operation: Performance, privacy-budget behavior, clinical utility, and deployment controls are treated as measurable validation requirements rather than assumptions.
The September 2026 protocol is an engineering baseline, not a claim of universal or independently certified security superiority.
The supplied assessment identifies several measurements that must remain validation-gated, including absolute VRAM usage, wall-clock training time, throughput, and clinical utility under rank-4 PEFT with the stated privacy budget.
Accordingly, this repository distinguishes:
- Architecture: controls and components defined by the design.
- Benchmark: measured performance under a named environment and configuration.
- Audit: independent assessment supported by an identifiable report and scope.
- Clinical validation: institution-specific evidence on the intended task and population.
- Regulatory readiness: documentation and controls mapped to the applicable pathway.