Research & Engineering Disclaimer: This article documents system design principles, human-computer interaction safeguards, and regulatory considerations for experimental prototype software.

The Cardinal Rule: AI Recommends, Human Decides

When designing user interfaces for diagnostic support—such as our prototypes for RetinaScan AI and IDRP—the most important design choice was not in the neural network weights; it was in the visual information architecture.

We explicitly outlawed binary "Diagnostic Decrees". The software must never state: "This patient has Severe Diabetic Retinopathy." Instead, the interface communicates probabilistic triage:

  • "Model triage indicates 84% probability of moderate non-proliferative changes. Key localized biomarker regions highlighted via Grad-CAM for physician verification."
  • Mandatory clinician confirmation checkbox before any report generation or export.
  • Complete transparency regarding image quality caveats (e.g., "Slight peripheral defocus detected").

Immutable Audit Logging for Clinical Reproducibility

In clinical engineering, you must be able to reproduce any inference made two years ago down to the exact floating-point output. This requires storing:

  1. Raw Input SHA-256: Cryptographic hash of the uncompressed source photograph.
  2. Model Release Hash: Exact git commit hash and model weight checksum (e.g., retinascan_v2.1_efficientnet_b4_sha256_e83a...).
  3. Preprocessing Parameters: Exact CLAHE tile grid size, normalization mean/std constants.
  4. Raw Logits: Uncalibrated floating point arrays prior to softmax transformation.