Perspectives on AI in regulatory documentation, clinical intelligence, and digital health, from Froncort and the wider life-sciences community.
How AI-DNO lets health network planners describe a goal in plain language, then compares several network strategies for cost, coverage, and turnaround.
What community health workers and programme leaders in Pune said about using AI-NETRA, from group discussions, surveys, and real field use.
A plain-language account of the AI-NETRA Pune Pilot Study: how it was designed, what the field results showed, and what the evidence does not yet prove.
A plain-language guide to AI-NETRA: how routine programme data becomes ranked screening plans, better sample routing, and a clearer view of progress.
Tuberculosis clusters in neighbourhoods, but most screening campaigns still cover whole blocks or wards. Why where teams go matters as much as how many people they screen.
Reflections from India AI Impact Summit 2026: artificial intelligence in health should scale only when field evidence supports it.
How AI-NETRA was presented at the India AI Impact Summit 2026 as a practical aid for screening plans, laboratory networks, and better use of scarce field resources.
Tuberculosis and other public health risks need smarter allocation of effort. Why programmes need tools that help them decide where to act, not only charts that describe the problem.