India AI Impact Summit 2026

designing-health-ai-for-bharat-a-billion-clinical-realities

7 min read

TRACK:Inclusion for Social Empowerment
SPEAKERS
  • Karthik Adapa, WHO
  • Kiran Gopal Vaska, NHA
  • Ms. Aswathy S., IAS, Department of Health & Family Welfare
  • Prof. Rajendra Pratap Gupta, Parliament Health
  • Prof. Rajendra Pratap Gupta, Parliament Health
  • Shalini Kapoor, Ek Step Foundation
SESSION OVERVIEW

As AI is integrated more closely into indian health care, this discussion brings together pioneers across technology, public systems, and deep last-mile knowledge, public systems and last mile delivery. The session will study ground realities in the Health-AI space. Drawing on institutional memory and frontier innovation, the session explores how Artificial Intelligence must evolve with people, infrastructure and ecosystems to deliver resilient and inclusive health outcomes at population scale.

VIDEO RECORDING

Designing Health AI for Bharat - A Billion Clinical Realities

Detailed Summary

Sub‑sectionContent
Context & MotivationAbhay thanked the audience, described travelling from Mumbai, and framed Max Health’s success as stemming from patient trust. Occupancy rates are 10‑15 % higher than competitors, which he attributes to early digital adoption.
Early Digital FoundationsFive‑six years ago Max built a common‑sized data lake aggregating 15 years of patient records, refreshed in real‑time. The aim was a “closed‑loop” system akin to Google’s search, but language localisation (regional Indian languages) was a major obstacle, leading to many search‑engine failures.
AI’s Current Role – Task‑Level WinsAI is still task‑oriented, improving efficiency rather than reshaping entire institutions. Examples:
  • Predictive analytics for bed‑availability.
  • Safety‑alert dashboards.
  • Automated clinical data capture via mobile forms, reducing clinician time spent on history‑taking.
Failures as LearningThe team experienced numerous setbacks:
  • Failed attempts to map ICD‑11 codes (expensive off‑the‑shelf tools, limited success).
  • Repeated “left‑right‑center” failures in longitudinal patient data pipelines.
Abhay framed these as necessary steps toward eventual success, likening the process to Edison’s iterative experiments.
Governance, Safety & SupervisionAI in health demands near‑zero tolerance for error. Unlike education, mis‑information can be fatal. Hence, extensive human supervision remains essential, especially for prescribing decisions.
Strategic Priorities for HospitalsTwo primary drivers for AI adoption:
  • Changing consumer behavior – patients now search for providers online, compare ESG scores, and expect digital interaction.
  • Efficiency incentives – predictive bed‑allocation, discharge planning, reduced clinician documentation time.
Institutional ReadinessEmphasis on a culture of circumspection; large workforce (≈43 k health workers) means AI must integrate with many processes. The “first‑mover advantage” is less valuable than getting it right.
Trust as the Core CurrencyDrawing from his earlier travel‑startup (Eka), Abhay stressed that trust in doctors is the biggest barrier for AI uptake. Even if a tool is technically sound, patients will still defer to the clinician they trust.
Future Outlook (3‑5 years)• Demographic dividend – India’s population will age, creating a massive demand‑supply gap in doctors.
• Predictive health – shift from reactive to proactive care (identifying illness before it manifests).
• Home‑care & skill‑set replication – scaling clinicians’ expertise via AI‑driven tools.
• Leapfrogging – India can bypass intermediate tech stages by adopting AI at scale now.

Key Announcements

  • No formal product launch was announced, but Abhay highlighted that Max’s data‑lake and EMR platform are “ahead of the curve” and serve as the backbone for forthcoming AI integration.

2. Transition to Panel Discussion

  • Moderator (Deepak Chopra) thanked Abhay, announced a short Q&A, and introduced the panel.
  • Housekeeping: Time constraints, audience participation limited to a few questions.

3. Panel – Part 1: Policy & Public‑Sector Vision (Dr Rajendra Pratap Gupta)

Sub‑sectionContent
ABDM OriginsThe Ayushman Bharat Digital Mission (ABDM) began as a 2014 BJP manifesto promise, later codified in the National Health Policy 2016 and fully realised when Gupta served as an advisor to the Health Minister.
Scale & Unprecedented ScopeABDM aims to create digital health records for a billion people, a task without precedent.
Current Achievements• Digital infrastructure (unique health IDs, HMI solutions) is in place.
• Interoperability: moving away from fragmented schemes toward a single, unified patient record.
Remaining Gaps• Rural doctor shortage persists; infrastructure for doctors to stay in villages is still lacking.
• Need to empower end‑users (clinicians, patients) to actually use the digital backbone.
Vision of “Golden Hour → Platinum Minutes”Leveraging real‑time data to shrink decision‑making times for primary care, especially in emergencies.
Optimism about TimelineGupta argues that months, not years, are the realistic horizon for measurable impact, citing the rapid rollout of COVID‑19 vaccination through the same digital IDs.

Key Insight – Policy is no longer a bottleneck; the implementation engine (ABDM) is ready, and the focus now shifts to adoption at the front‑line.

4. Panel – Part 2: National Health Authority & Data Architecture (Nikhil Dongri)

Sub‑sectionContent
Federated Architecture of ABDMABDM provides a federated data layer that allows Indian‑built AI models to train on context‑rich, domestic data without exporting it.
Limitations of Foreign ModelsImported models often mis‑represent Indian demographics, especially rural and low‑resource populations, leading to bias.
Domestic “nano‑models”Small, lightweight models (SLMs) can be trained locally, leveraging the federated platform, and are more suitable for Indian clinical workflows.
Public‑Sector as Training GroundLarge government hospitals generate massive, diverse datasets that can feed Indian startups, provided the data is privacy‑preserving (DPDP Act compliance).
Future Private‑Sector IntegrationThe same federated approach can be extended to private hospitals, allowing interoperable AI services across sectors.
Call for Context‑Rich DataEmphasised that clinical context (language, environment, comorbidities) must be encoded for AI to be trustworthy and effective.

Recommendation – Encourage open, federated data ecosystems that let Indian startups iterate quickly, while maintaining patient privacy and regulatory compliance.

5. Panel – Part 3: Trust, Cloud vs Edge & Multilingual Voice AI (Jigar – NVIDIA/Cloud Specialist)

Sub‑sectionContent
Multilayered TrustTrust varies by stakeholder: physicians demand clinical accuracy; patients (e.g., a mother with a newborn) demand zero‑risk outcomes.
Data‑Centric TrustTrust also hinges on how data is sourced, stored, and governed. The rise of cloud‑native AI raises concerns around sovereignty, cost, and latency.
Edge vs Cloud Decision‑Tree
  • Edge is suitable for offline, low‑connectivity use‑cases (e.g., remote PHC voice capture).
  • Cloud/Hybrid required for high‑throughput inference, multilingual translation, and model updates.
Personalization & LanguageExample: a Tamil‑speaking doctor’s voice should be converted to Hindi for a Delhi patient—illustrates the need for language‑agnostic, voice‑first AI.
Feedback LoopsPatients often seek second opinions, revisiting recorded consultations. Incorporating this feedback into model training improves trust and model relevance.
Policy & GovernanceSuggests robust audit trails and transparent model explainability to satisfy regulators and clinicians.

Key Takeaways

6. Panel – Part 4: Private‑Sector & Builder Perspective (Tanvi Lal)

Sub‑sectionContent
AI as a Personalisation EngineAI must be voice‑first, multilingual, and context‑aware to bridge inequity gaps.
Transformation vs. TechnologyBuilding a tech stack is only part of the journey; success depends on education, adoption, and workflow integration.
Pilot‑to‑Scale ChallengeMany startups deliver short‑term demos (3‑6 months) that fail to embed into everyday workflows, especially in public health centres (PHCs).
Differentiated Strategies• Private hospitals – higher digital literacy, more resources; can adopt sophisticated AI stacks quickly.
• PHCs – need simpler, low‑resource solutions, heavy emphasis on trust‑building and capacity‑building.
Holistic Transformation StackIncludes tech, training, policy alignment, feedback mechanisms, and evaluation metrics that differ between private and public settings.
Vision for Population‑ScaleEmphasises designing for scalability from day‑one, not just “pilot‑first”.

Insight – The human element (training, trust, cultural fit) outweighs pure technology in determining whether AI takes root across India’s heterogeneous health ecosystem.

7. Panel – Part 5: Regional Equity & Normative Guidance (Padmini Vishwanath)

Sub‑sectionContent
WHO’s RoleDevelop normative guidance to ensure AI is equitable, safe, and context‑appropriate across South‑East Asia.
Readiness Frameworks for Low‑Resource SettingsInstead of building tools for top‑tier hospitals and later adapting them, WHO pilots frameworks that start with the most remote settings, defining device availability, connectivity, and human‑resource constraints.
Benefits Observed• Higher provider trust when tools are designed for the ground reality.
• Faster adoption and equity gains.
RecommendationModernise legacy systems before layering new AI solutions; avoid “technology‑first” thinking.
Policy ImplicationsCalls for country‑specific standards that recognize linguistic diversity, health‑system fragmentation, and varied digital maturity.

Key Observation – Bottom‑up design—starting from the most constrained environments—produces more equitable AI deployment than a top‑down “best‑in‑class” approach.

8. Audience Q&A & Closing Remarks

QuestionHighlights of Answers
Edge vs Cloud for multilingual voice AI (22 languages)Jigar reiterated that use‑case dictates architecture: remote, low‑bandwidth scenarios → edge; anything requiring real‑time large‑model inference → cloud (preferably India‑hosted for data sovereignty).
Reliance on Indian vs Global datasetsA Mayo‑Clinic representative stressed the absence of a data‑culture in India; despite 860 M health IDs, the actual clinical records are sparse, limiting Indian‑centric model training.
Ethics, prescribing practices, and regulationModerator (and panelists) highlighted the need for ethical enforcement (e.g., flagging over‑prescription), pointing to China’s real‑time prescription audit as a model; emphasized that behavioral change among clinicians is the bigger hurdle than technology.
Future milestones (next 3‑6 months)Consensus: AI‑ready data systems, policy‑driven supervision, and scalable pilots that feed back into the federated ABDM platform.
Model training on Indian dataGupta reiterated that most current models still rely on foreign data; the push is to generate Indian‑specific datasets through public‑private collaborations.

Closing – Deepak thanked the panel, highlighted the collective optimism and cross‑sector commitment to leverage AI for Bharat’s health challenges, and concluded the session.

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