EDTC WEBINAR

From Capability to Trust: Building a Reliable Evidence Layer for Clinical AI

Tuesday, October 27, 2026 · 11:30am MT / 1:30pm ET

Virtual · Free

About the session

Clinical AI has demonstrated remarkable capability, yet reliability remains its most critical unsolved challenge. Without transparent, traceable reasoning, AI outputs are difficult to verify, dispute, or act upon with clinical confidence. The missing prerequisite is not better models. It is a trust layer that makes AI reasoning accountable to clinicians and grounded in validated evidence.

This presentation introduces a reasoning-based approach to building that trust layer, centered on making biomedical knowledge computable and contextual at scale. By encoding clinical evidence in a structured, machine-executable knowledge graph, AI systems can reason deterministically over patient-specific inputs using an inspectable, auditable evidence base, producing outputs that clinicians can trace, interrogate, and dispute.

We explore an approach to scaling trustworthy knowledge infrastructure that moves beyond static evidence repositories, enabling not just informed responses, but reliable reasoning and agentic orchestration grounded in verified biomedical intelligence.

The trust layer is not a brake on AI. It is the prerequisite that turns capability into clinical transformation.

Your presenter

Ravi Bajracharya

CTO & Co-founder, Datum.bio

Ravi is CTO and co-founder of Datum.bio, where he builds semantic health data solutions powered by biomedical knowledge graphs and ontologies. With over 15 years of software engineering experience, he currently focuses on developing guideline-adherent AI agents through a neuro-symbolic approach.

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