India AI Impact Summit 2026

scaling-ai-for-public-health-impact-public-private-partnership

6 min read

TRACK:Economic Growth and Social Good
SPEAKERS
  • Dr Sunil Kumar Barnwal, National Health Authority, GoI
  • Mr Anurag Yadav, Government of Uttar Pradesh
  • Mr Kiran Gopal Vaska, National Health Authority, GoI
  • Mr Sourabh Gaur, Department of Health and Family Welfare, Government of AP
  • Ms Geetha Manjunath, Niramai
  • Ms. Punya Salila Srivastava, IAS, Govt. of India
  • Rajan Khobragade, Health & Family Welfare Department, Government of Kerala
SESSION OVERVIEW

As artificial intelligence (AI) adoption in health accelerates, its public health impact depends on governance, institutional capacity, and effective public–private sector collaboration. This panel examines how AI is being moved from pilots to scale, highlighting implementation experiences, procurement and integration challenges, and sustainability beyond proof-of-concept. Drawing on public and private sector perspectives, the discussion focuses on enabling procurement, ecosystems and practical pathways for responsible, scalable AI deployment in public health systems.

VIDEO RECORDING

Scaling AI for Public Health Impact: Public Private Partnership

Abstract

The panel explored how artificial‑intelligence solutions can move from isolated pilots to sustainable, large‑scale deployments within India’s public‑health system. The discussion covered the national digital health infrastructure (ABDM), concrete AI use‑cases (maternal‑mortality prediction, TB screening, retinal disease detection), procurement and funding bottlenecks faced by start‑ups, the need for state governments to act as ecosystem curators, and the governance safeguards required for trustworthy, bias‑free AI. Panelists converged on the importance of standards, interoperable open‑source frameworks, and a public‑private partnership model that treats AI as a public‑health tool rather than a mere commercial product.

Detailed Summary

  • Digital‑health journey: Over the past decade India has progressed from digitising paper records to a national interoperable ecosystem – the Ayushman Bharat Digital Mission (ABDM).
  • Key metrics: 859 million citizens hold ABHA IDs; >878 million health records are linked.
  • Infrastructure rollout: 180 000 “Ayushman Arogya Mandirs/Kendra” (primary‑care hubs) equipped with e‑Sanjivni tele‑medicine, AI‑assisted clinical decision support, delivering ≈449 million consultations via 220 000 registered providers.
  • Policy alignment: ABDM “open‑standards, interoperable, responsible Gen‑AI” framework; emphasis on privacy‑by‑design and equitable access (PM Modi’s digital‑public‑infrastructure agenda).
  • AI expectations: Reduce workforce burden, support frontline workers, and enable early disease detection (e.g., diabetic‑retinopathy, TB).
  • Call for panel: Identify state‑level constraints, procurement pathways, and sustainability models for scaling AI.

Announcements / Data Highlights

IndicatorFigure
ABHA‑linked citizens859 M
Health records linked878 M
Primary‑care hubs (Ayushman Arogya)180 000
Tele‑medicine consultations449 M
AI‑assisted TB‑screening tool usage>1.6 L individuals screened
AI‑assisted retinal‑screening impact>7 000 patients across 38 facilities

2. Vision for a Mature AI‑Enabled Public‑Health Ecosystem – Dr Sunil Kumar Barnwal

  • From “projects” to “ecosystem”: AI must be embedded across the entire care workflow, not treated as a peripheral add‑on.
  • Stakeholder diversity: Government, private health providers, health‑tech firms, start‑ups, and innovators all must participate.
  • Two‑pronged AI impact:
    1. Clinical efficiency – e.g., diagnostics, robotics.
    2. Workflow optimisation – reducing non‑clinical tasks for doctors, ASHAs, and nurses.
  • Standardisation & Open Architecture: Leverage ABDM’s open standards to enable AI deployment even when data is decentralized.
  • Five‑year target: An integrated AI ecosystem where every health encounter is supported by AI‑driven decision aids, data analytics, and interoperable registries.

Key Insight

“If AI merely sits beside the workflow, its potential is lost; it must become the workflow.”

3. State‑Level Implementation Experience – Meghalaya Representative (IAS)

a. Maternal‑Mortality Prediction

  • Problem: High maternal mortality; data existed but was under‑utilised.
  • Approach: Combined HMIS data with socio‑economic determinants to build a predictive risk model for pregnant women.
  • Outcome: >50 % reduction in maternal deaths over five years; front‑line workers receive early alerts for high‑risk mothers.

b. AI‑Enabled TB Screening

  • Tool: Offline AI‑powered handheld X‑ray interpretation.
  • Scale: 90 000 screened; 110 + positive cases identified and linked to treatment.
  • Economic impact: ₹4 crore saved on patient transport costs.

c. Electronic Health Records (EHR) & Administrative Burden

  • Goal: Reduce clinicians’ paperwork through AI‑driven automation.
  • Method: “Problem‑Driven Iterative Adaptation (PDI‑8)” – test‑learn‑adapt cycles to refine solutions.

Overall Takeaway

  • Value‑driven pilots that directly address a state’s pressing problem (e.g., specialist shortage, admin load) gain political and executive support, facilitating scale‑up.

4. Private‑Sector Challenges – Dr Geeta Manjunath (Niramai)

  • Pathway to pilots:
    • Fragmented decision‑making across 28 states; each requires separate convincing.
    • Pilot funding: Start‑ups often must fund pilots themselves; few states allocate budget for proof‑of‑concept.
  • Scale‑up bottlenecks:
    • Lack of a clear, communicated roadmap from government to move from pilot → program → nationwide rollout.
    • Regulatory ambiguity for AI‑based medical devices (software‑as‑device).
  • Knowledge gap: Start‑ups are unaware of how to engage with ABDM’s 180 000 Ayushman Arogya Mandirs or the procurement portals.
  • Suggested remedy: A dedicated governance body that publishes criteria, timelines, and funding mechanisms for AI health solutions.

Data Point

  • Screening coverage for breast cancer in India: 1.3 % (mammography), 98.7 % detected by self‑examination → underscores need for low‑cost AI‑enabled early detection.

5. State as Ecosystem Curator – Dr Piyush Singh (Jammu & Kashmir)

  • ABDM as enabler: Provides interoperability standards that relieve states from building bespoke data pipelines.
  • Computational Health Unit: Collaboration with ICMR and IIT Jammu to set up a dedicated AI hub.
  • Stakeholder convening: Organises public notices inviting start‑ups, academia, and national health missions to co‑design solutions tailored to local disease burden.
  • Capacity‑building: High demand from clinicians for data‑analytics training (≈10 doctors expressed interest during a recent workshop).

Recommendation

  • Treat the state not only as implementer but as a “convenor” that frames problem statements, curates data, and validates AI prototypes before scaling.

6. Private‑Sector Role Beyond Technology – Dr Anand Sivraman (Remedio Innovative Solutions)

  • Product: Smartphone‑based retinal‑imaging device with edge‑AI for diabetic retinopathy, glaucoma, and macular degeneration detection.
  • Programmatic delivery:
    • Capacity building of health workers; continuous dashboard monitoring of device utilisation.
    • Incentive alignment: Shift from capital‑equipment sales to pay‑per‑screened‑person models.
  • Public‑health framing:
    • Integrated retinal image analysis to predict cardiovascular, CKD, and calcium‑score risks, expanding value from a single screening to multiple disease markers.
  • Regulatory journey: First Indian AI solution to obtain CDSCO (Central Drugs Standard Control Organisation) approval without a predicate device; now serves as a reference standard for subsequent solutions.
  • Economic impact: 85 % reduction in screening cost; 99 % of detected cases were previously undiagnosed.

Insight

“Treat AI as a public‑health program—design, implement, monitor, and sustain—rather than a one‑off commercial transaction.”

7. Guardrails for Scalable AI Deployments – Dr Sunil Kumar Barnwal (follow‑up)

  • Governance‑by‑design: Embed ethical, privacy, and security controls before deployment, not as afterthoughts.
  • Human‑in‑the‑loop: AI should augment clinicians, never replace them.
  • Bias & Equity: Proactively test for demographic bias; continuously monitor throughout the AI system’s lifecycle.
  • Lifecycle Management: Include de‑commissioning plans when models become outdated.
  • National Strategy: Ministry is drafting a stand‑alone AI‑in‑Health strategy that will codify these guardrails.

8. Way‑Forward & Concluding Remarks – Ms Punya Srivastava

  • Ecosystem‑wide guidance: Propose pathway documents that outline procurement, regulatory, and technical steps for states and innovators.
  • Regulatory capacity building: Strengthen a dedicated health‑technology assessment (HTA) body (ICMR/HTA) to evaluate cost‑effectiveness and implementability.
  • Solution Registry: Create a centralized catalogue of vetted AI tools, their evidence base, and deployment status, to be shared at NHM conferences and regional meetings.
  • Best‑practice sharing: Leverage NHM’s flagship programs to disseminate successful state‑level AI pilots across the country.
  • Closing gratitude: Acknowledgement of all panelists and the collaborative spirit required to achieve “Viksit Bharat 2047.”

Key Takeaways

  • National Digital Backbone: ABDM already provides an interoperable, open‑standards health data ecosystem (859 M ABHA IDs) that can host AI services at scale.
  • Ecosystem First: Successful AI scaling demands an ecosystem approach—government, private innovators, academia, and health workers must co‑design, not merely procure.
  • State‑Driven Pilots: Real‑world impact comes from problem‑driven pilots that address concrete state challenges (maternal mortality, TB detection, admin burden).
  • Private‑Sector Bottlenecks: Start‑ups face fragmented procurement, lack of pilot funding, and unclear pathways to national rollout; a single governance liaison could streamline this.
  • Human‑Centred AI: AI must augment health‑care workers, be privacy‑by‑design, and include continuous bias monitoring to avoid widening health inequities.
  • Capacity Building is Crucial: Both clinicians and administrators need training in data analytics and AI to adopt tools effectively.
  • Programmatic Business Models: Transition from hardware sales to outcome‑based payment (e.g., per screened person) improves sustainability and alignment with public‑health goals.
  • Regulatory & HRTA Frameworks: A dedicated health‑technology assessment and a national AI‑in‑Health strategy are essential for evaluating cost‑effectiveness, feasibility, and ethical compliance.
  • Solution Registry & Knowledge Sharing: A central registry of vetted AI solutions and regular NHM knowledge‑exchange sessions can accelerate cross‑state learning.
  • Long‑term Vision: Within five years, India aims for an integrated AI‑enabled public‑health ecosystem that reduces mortality, cuts costs, and democratizes quality care to the last mile.

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