TRIAGE is a research-oriented framework tackling one of the thorniest problems in applied machine learning today: making AI safe, explainable, and trustworthy enough to use in clinical decision support. The repo's description hints at an ambitious architecture that fuses three complementary ideas into a single pipeline.

• Feature-based explainers — likely SHAP, LIME, or similar attribution methods that surface why a model produced a given prediction in terms a clinician can audit.
• Retrieval-augmented LLMs — grounding generated explanations in retrieved medical literature or guidelines, so the natural-language output isn't hallucinated from model weights alone.
• Abstention-awareness — perhaps the most interesting piece. Rather than always producing an answer, the system can decline to predict when confidence is low or evidence is thin. In medicine, "I don't know" is often the most valuable output a model can give.

The combination is timely. Regulators (EU AI Act, FDA SaMD guidance) increasingly demand traceability and human oversight for clinical AI, and the "explanation" most LLMs offer is post-hoc rationalisation rather than grounded reasoning. A modular framework that separates the predictor, the retrieval layer, the abstention policy, and the explanation surface is exactly the kind of scaffolding a clinical informatics team would need to integrate into a real workflow.

Who might benefit:

• Clinical informaticists and ML engineers building decision-support tools who need an architecture template rather than a black-box demo.
• Academic researchers in trustworthy ML looking for a concrete codebase to extend with new abstention criteria or explainers.
• Anyone studying RAG patterns in high-stakes domains — the same design generalises to legal, financial, and safety-critical engineering contexts.

With zero stars and a fresh push, it's likely still early-stage. Worth watching to see whether the modularity claim holds up in the code.