Evidence First, Adoption Next: Why AI in Health Will Only Scale on Proof

At the India AI Impact Summit 2026, one message stood out in discussions with colleagues from India's National Health Mission, IndiaAI, Wellcome Trust, and the Gates Foundation: AI in health will only scale on the strength of rigorous, high-quality evidence.

Froncort.AI's deployment of AI_NETRA is designed as a prospective, field-validated evaluation comparing AI-guided TB Active Case Finding (ACF) and Diagnostic Network Optimization (DNO) against routine program workflows under real operational conditions—with predefined outcomes and state-level oversight. This is implementation science in action and a prerequisite for precision public health at scale.

A fourfold architecture

The architecture is intentionally structured around four pillars:

• Expert-led, clinically grounded model design
• Geospatial, actionable case finding beyond disease mapping
• Network optimization within real system constraints
• Human-in-the-loop frontline execution

From today's TB programs to tomorrow's vaccines

These discussions reinforced a broader point: the same rigorously validated geospatial AI frameworks being tested today for ACF and DNO will be critical tomorrow for rational TB vaccine targeting and equitable allocation. No methodological rigor, no credibility. No credibility, no scale.

We are grateful to our partners at FIND and for the support from Google.org. Evidence first, adoption next.