India AI Impact Summit 2026

harnessing-ai-for-health-equity-building-inclusive-human-capital-and-strengthening-researchindustry-collaboration

6 min read

TRACK:Human Capital
SPEAKERS
  • Dr. Mona Duggal, ICMR
  • Mr Abhimanyu Saxena, UNDP India
  • Ms. Nande Putta, UNICEF
  • Ms. Vinita Sethi, Independent Director
  • Prof (Dr.) Balvir S Tomar, NIMS University Rajasthan, Jaipur
  • Prof. Dr Anurag Agrawal:, Ashoka University
  • Prof. Dr Sanjay Pattanshetty, NIMS University, Rajasthan, Jaipur
  • Prof. Ilona Kickbusch, Global Health Centre at the Graduate Institute of International and Development Studies, Geneva
SESSION OVERVIEW

This high-level panel will explore how AI can strengthen global health while keeping inclusion and social equity central. The conversation will focus on building human capital for AI-driven health innovation, catalyzing inclusive participation, and strengthening research–industry linkages so scientific insights translate into practical health solutions. The aim is to identify clear priorities, collaboration pathways, and measurable actions that support responsible AI adoption and a more equitable digital health future.

VIDEO RECORDING

Harnessing AI for Health Equity: Building Inclusive Human Capital and Strengthening Research–Industry Collaboration

Abstract

The panel explored how artificial intelligence can be harnessed to advance health equity worldwide, keeping inclusion and social justice at the core. Speakers described concrete initiatives—such as the U‑Win voice‑to‑text platform for frontline health workers, AI‑enabled outbreak prediction for measles‑rubella, and nationwide digital health IDs—to illustrate convergence of research, industry, and public‑sector actors. The discussion centered on building inclusive human capital, establishing robust data‑governance frameworks, and ensuring that AI systems are transparent, trustworthy, and co‑created with the communities they serve. Audience questions probed responsible AI, data privacy, and the need to embed younger generations in digital‑health strategy. The session closed with a formal MOU between the National Institute of Research in Digital Health & Data Science and NIMS University.

Detailed Summary

The moderator opened the session with a series of acknowledgments and a brief recap of the agenda, emphasizing the need for convergence, collaboration, and co‑creation in AI‑driven health solutions.

“We are talking about convergence, collaboration and most importantly co‑creation.”

The opening remarks set the tone for a conversation that would weave together technical innovation, policy frameworks, and human‑centered design.

2. Panelists Share Illustrative Initiatives

2.1 The U‑Win Platform – Reducing Data‑Entry Burden

  • Speaker (Panelist – likely Dr. Mona Duggal) described the U‑Win platform, launched after the COVID‑19 pandemic to support one million frontline health workers (ASHA workers, ANMs).
  • The platform originally required 26 parallel applications per worker, creating a massive data‑entry load.
  • In partnership with UNICC and Amazon Web Services, the team built a voice‑to‑text API that enables health workers to dictate patient details (child’s name, vaccination status, weight, parents’ names).
  • This AI‑enabled speech capture reduces 40–50 % of manual data‑entry effort, allowing workers to focus on care delivery.
  • The speaker stressed that reducing entry burden is not purely a technical issue but also a governance one, requiring co‑creation with end users to guarantee adoption.

2.2 Measles‑Rubella (MR) Elimination – AI‑Powered Outbreak Prediction

  • The panel highlighted India’s goal of measles‑rubella elimination and the associated data challenges: vaccination coverage data (MR1, MR2), outbreak reports, and case‑reporting streams all exist in separate silos.
  • An AI‑driven modeling framework was introduced that integrates GIS‑based disease mapping, vaccination coverage, and real‑time outbreak data to generate early‑warning signals for health workers.
  • This predictive capability supports targeted interventions, helping to leave no one behind by directing resources where they are most needed.

2.3 Key Themes Summarised by the Panel

A panelist (identity not explicit) distilled the discussion into four pillars:

  1. Innovation – Developing new AI tools
  2. Implementation – Deploying them at scale
  3. Integration – Linking AI outputs with existing health systems
  4. Inclusivity – Ensuring diversity of models and contexts

The speaker also urged independence in model development, emphasizing that local data diversity must inform AI design.

3. Audience Q & A

3.1 Question from Gaurav Jain (Pfizer, APEC) – Responsible AI & Frontline Burden

  • Question: How can data‑governance frameworks prevent over‑burdened frontline workers from blindly trusting AI outputs?

  • Panel Response (multiple panelists, attribution uncertain):

    • Data Quality First: AI must be built on high‑quality, validated data; otherwise, AI magnifies existing weaknesses.
    • Checks & Balances: Introduce routine monitoring of output indicators (e.g., data‑entry adherence rate, timeliness) and outcome indicators (e.g., vaccination coverage, outbreak detection).
    • Human‑in‑the‑Loop: Empower ASHA workers and other frontline staff with decision‑support tools that augment rather than replace their judgment.

  • Key Insight: Governance responsibility lies with public‑health stakeholders, not solely with AI developers.

3.2 Question from Dr. Deepti (Deloitte) – Technology Readiness & Equity

  • Question: How do we balance rapid AI roll‑outs with the need for governance, evaluation frameworks, and equitable access?

  • Panel Response (identity unclear):

    • Iterative Approach: Start with feasible, low‑complexity use cases, develop evaluation frameworks, then progressively tackle more complex problems.
    • Technology as an Enabler, Not a Force‑Fit: Avoid forcing tech solutions where they do not fit; let policy and equity considerations guide adoption.
    • COVID‑19 as a Catalyst: The pandemic accelerated digital infrastructure (internet, tele‑consultations), demonstrating that large‑scale interventions are possible when urgency aligns stakeholders.

3.3 Question on Data Privacy & Ownership (Unnamed Participant)

  • Question: In a nation‑scale digital health system, how can patients retain ownership of their data while ensuring privacy?

  • Panel Response (likely Prof. Ilona Kickbusch or a panelist focused on policy):

    • RCH Portal (Reproductive & Child Health): A government‑owned, UNICEF‑supported longitudinal health record now deployed across ~25 Indian states, aiming for nationwide coverage.
    • Interoperability Through ABHA IDs: The Aayushman Bharat Health Account (ABHA), together with Healthcare Facility Registration (HFR) and Healthcare Professional Registration (HPR), creates a single, unique health ID enabling secure data exchange.
    • Compliance with DPDP Act: Recent Data Protection and Data Privacy (DPDP) Act provides a legal framework that enhances patient confidence that their data is safe and their consent is respected.

3.4 Generational Equity – Voices of Youth

  • Comment by Prof. Ilona Kickbusch:

    • Stressed that digital‑health strategies must embed “generational equity”, ensuring young people (under 25) participate in decision‑making.
    • Cited a review of 87 digital‑health strategies globally; only 43 % mention youth.
    • Urged India—being a young nation—to systematically include youth (both male and female) in co‑creation processes.

  • Follow‑up Query (unidentified participant) on inter‑generational AI: Requested clarification on how platforms can bring younger generations into system design. The moderator indicated insufficient time for a full response and suggested a post‑session discussion.

4. Closing Remarks & Formal Announcements

  • MOU Signing: The session concluded with a Memorandum of Understanding between the National Institute of Research in Digital Health & Data Science and NIMS University. Dr. Mona Duggal (founder & Chancellor of NIMS University) formally marked the panel’s end.

  • Final Acknowledgments: The moderator thanked all panelists, the audience, and the organizing bodies, reiterating the collective commitment to equitable, AI‑enabled health solutions.

Key Takeaways

  • AI must be co‑created with frontline workers to meaningfully reduce data‑entry burdens; the U‑Win voice‑to‑text pilot saved up to 50 % of manual effort.
  • Data quality is a pre‑condition for responsible AI; poor data amplifies systemic weaknesses.
  • Predictive AI for disease outbreaks (e.g., measles‑rubella) can provide early warnings, enabling targeted interventions and improving equity.
  • Robust governance frameworks—including regular monitoring of output/outcome indicators—are essential to prevent over‑reliance on AI by over‑burdened health staff.
  • Digital health IDs (ABHA) and interoperability standards are foundational for scalable, privacy‑preserving health data ecosystems in India.
  • Generational equity remains under‑addressed; only 43 % of national digital‑health strategies mention youth, despite their majority demographic status.
  • Iterative, feasibility‑first rollout of AI solutions is preferred over “force‑fit” deployments; the COVID‑19 pandemic demonstrated the speed possible when urgency aligns stakeholders.
  • The MOU between NIMS University and the National Institute of Research in Digital Health & Data Science formalises a collaborative pathway for research‑industry translation of AI health innovations.

Prepared from the verbatim transcript of the panel discussion held at the AI for Health Equity conference in Delhi.

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