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

At the India AI Impact Summit 2026, discussions with colleagues from India's National Health Mission, IndiaAI, Wellcome Trust, and the Gates Foundation returned to a single point. Artificial intelligence in health will scale only on the strength of careful, high-quality evidence.

Froncort.AI's deployment of AI-NETRA is designed as a field evaluation. It compares AI-guided tuberculosis screening and diagnostic network planning with routine programme work under real conditions, with outcomes defined in advance and with state-level oversight. That is implementation science, and it is a condition for precision public health at scale.

The work is built around four simple commitments. Models are designed with clinical experts. Mapping is meant to guide action, not only to display disease. Network planning respects real system limits. Frontline workers remain in the loop.

The same conversations pointed to a longer horizon. The mapping and planning methods being tested today for screening and laboratory networks will matter tomorrow for fair targeting of tuberculosis vaccines. Without methodological care there is no credibility. Without credibility there is no scale.

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