MedTech and AI Innovations in Public Health Systems

Detailed Summary

• The moderator welcomed the audience and outlined three core anchors for AI in public health:

  1. Cost‑effectiveness – reducing both government spending and out‑of‑pocket costs for patients.
  2. Care coordination – building longitudinal health records that clinicians can use for smarter decision‑making.
  3. Operational efficiency – shortening patient‑waiting times while maintaining quality.

• The moderator invited Mr. Saurabh Gaur (Andhra Pradesh) to describe the government’s perspective on population‑scale AI deployment.

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2. Government‑Level AI Strategy (Mr. Saurabh Gaur)

• Introduced SAHI – Strategy for Artificial Intelligence in Public Health – a national policy framework launched by the Ministry of Health.

• Highlighted current AI‑enabled services:

  • Radiology & screening – AI‑based X‑ray interpretation and diabetic retinopathy detection to overcome specialist shortages in rural areas.
  • eSanjivani tele‑consultation platform – connects primary‑care doctors with tertiary experts in real time.
  • Out‑of‑pocket (OOP) reduction – AI‑driven diagnostics lower patient expenses and shift utilisation from private to public facilities.
  • Digital health records & workflow optimisation – digitisation of patient data supports supply‑chain management and hospital operations.

• Stressed that AI must be trustworthy, safe and transparent to encourage public confidence and broaden uptake.

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3. From Innovation to Institutionalisation (Mr. Shiv Kumar, Swasti)

• Observed a common pattern: “solutions looking for problems.” Successful institutionalisation starts with problem‑first agenda‑setting by the state.

• Described a four‑step framework:

  1. Problem definition – e.g., Andhra Pradesh’s Center for Applied Technology issuing a call for solutions targeting frontline‑worker pain points.
  2. Evidence generation – rigorous field testing rather than relying only on vendor hype.
  3. Use‑case library – cataloguing validated pilots (including performance in tribal or low‑resource settings) to guide scaling.
  4. Policy & guardrails – establishing AI‑specific data‑sharing, privacy and monetisation policies, ensuring community benefit.

• Emphasised that the state must act as the ultimate validator, not merely a passive recipient of vendor claims.

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4. Private‑Sector AI for Primary‑Care (Ms. Saraswathi Padmanabhan, Tata MD)

4.1 Clinical Decision Support

• AI provides structured longitudinal summaries to medical officers at PHCs, turning episodic visits into continuous care pathways (e.g., tracking HbA1c trajectories for diabetes).
• Real‑time prompts alert clinicians to missed investigations (e.g., foot exam for diabetics) and surface evidence‑based treatment guidelines.

4.2 Front‑line Worker Assistance

• AI‑driven prioritisation bots help ASHA workers rank pregnant women or high‑risk mothers among a large caseload, improving outreach efficiency.

4.3 System‑Level Analytics

• Development of a “wellness score” that merges patient data, environmental indicators and community‑level metrics to flag high‑risk geographies for proactive interventions.

• Acknowledged deeper challenges: data capture across diverse geographies, integration with existing government platforms, and ensuring the AI output is actionable for field staff.

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5. AI for Programme Monitoring – Tobacco‑Control (Mr. Sanjay Seth)

• Illustrated how AI can move dashboards from “what we haven’t done” to “what we must do.”

  • Predictive alerts identify districts where implementation of tobacco‑control activities is likely to fail.
  • Image‑recognition validates whether school‑level activities have been performed correctly (≈ 98 % accuracy).
  • Personalised messaging – 40 000 teachers receive AI‑curated reminders in their preferred language, dramatically improving compliance.

• Stated that AI must be embedded within the delivery workflow, not layered on top as an after‑thought.

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6. Clinical‑Care AI Innovations from AIG Hospitals (Dr. Rakesh Kalapala)

• Cost‑effective diagnostics – a 500‑rupee AI model that detects fatty liver, compared with a 1.2‑crore MRI machine charging 5 000 rupee per scan.

• AI‑enabled discharge summaries – reduces turnaround from 8–10 hours to ≤ 30 minutes, freeing beds faster.

• Bed‑management & EMR optimisation – AI streamlines patient flow, cutting administrative bottlenecks in both private and public hospitals.

• Highlighted the need for early‑adopter private pilots that can later be handed over to the public sector for scale‑up.

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7. Public‑Private Collaboration Platform (Mr. Suhel Bidani, Gates Foundation)

• The Gates Foundation, together with Triple‑IT, ISB, IIT‑Delhi and the AIM Foundation, created a neutral innovation platform where startups pitch ideas, receive clinical validation and are subsequently hand‑held into state‑level pilots.

• Example: “Jeevan Mitra” – an AI‑driven risk‑stratification tool for pregnant women in Andhra Pradesh, now deployed in government clinics.

• The model demonstrates how early‑stage private innovation, rigorous validation and public‑sector adoption can be chained together for rapid diffusion.

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8. Barriers to Scaling AI in Public Health

Barrier	Key Points Raised
Bounded rationality & policy caution	Decision‑makers must balance speed with safety, efficacy and cost; evidence is mandatory before rollout.
Data quality	AI performance hinges on high‑quality, exhaustive data; many ANM‑level entries are manual and error‑prone.
Workflow integration	Technology must be seamlessly embedded into existing clinical routines to gain adoption.
Change‑management & incentives	Early adopters act as champions; resistance can be reduced through training, incentives, and demonstrable benefits.
Infrastructure (connectivity, power)	Generally less problematic in Andhra Pradesh, but remains a hurdle in many other states.

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9. National‑Level Vision (Mr. Saurabh Jain, Ministry of Health)

• Representativeness of data – AI models need training data from every region to capture diverse disease patterns.
• ABHA (Aadhaar‑based Health ID) – universal health‑record ID enables cross‑state data flow and supports disease‑surveillance, imaging analytics and automated reporting.
• Administrative workload reduction – AI can auto‑populate multiple portals from a single data entry, freeing frontline workers for clinical duties.
• Supply‑chain optimisation – predictive analytics for drug‑stock and equipment distribution.
• Collaborative model – central ministry works with state governments and private partners (e.g., Tata MD) to co‑design AI solutions.

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10. Audience Q & A Highlights

Topic	Summary of Discussion
Mental‑health AI	Audience asked about AI for suicide‑prevention and depression detection. Speakers noted the lack of uniform EMR data for mental‑health, but mentioned ongoing pilots using voice‑analysis and QPR (Question‑Persuade‑Refer) methodology with the Suicide Prevention Foundation of India.
Startup replication at the centre	A question about a central platform for scaling validated startups. ICMR’s sandbox was cited as a forthcoming national test‑bed that can certify models before broader rollout.
Future “most‑impactful” innovation	Panelists converged on three themes: (1) Holistic public‑private collaboration, (2) Preventive health (behaviour‑change programmes), and (3) Data‑ownership models (data cooperatives) that return value to citizens.
Closing remarks	Moderator thanked the panel and audience, highlighted the need for continued partnership and invited further collaboration on mental‑health AI and startup sandbox initiatives.

Key Takeaways

• SAHI (Strategy for AI in Public Health) provides a national policy backbone, but implementation hinges on state‑level problem definition and evidence generation.
• Problem‑first innovation: AI solutions must address a clearly articulated health‑system need; otherwise they remain “solution‑seeking problems.”
• Longitudinal, structured health records powered by AI enable primary‑care clinicians to deliver continuous, data‑driven care rather than episodic visits.
• Predictive monitoring and real‑time feedback (e.g., in tobacco‑control) can shift program dashboards from retrospective reporting to proactive action.
• Cost‑effective AI diagnostics (e.g., low‑cost fatty‑liver detection) dramatically lower OOP expenses and expand access in resource‑constrained settings.
• Data quality and integration are the most critical technical constraints; without reliable entry points, AI models cannot deliver trustworthy outputs.
• Change‑management, incentives and workflow embedding are decisive for adoption; early adopters act as champions for broader staff uptake.
• National digital identity (ABHA) coupled with interoperable health records creates the data foundation required for scalable AI across states.
• Public‑private innovation platforms (e.g., Gates‑Foundation‑Triple‑IT consortium) can fast‑track pilot validation and hand‑over to governments.
• Preventive health (behaviour‑change, adolescent health, tobacco cessation) offers the highest ROI and should be the focal point of AI‑driven public‑health programmes.

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Prepared from the verbatim transcript of the “MedTech and AI Innovations in Public Health Systems” session at the AI Conference, Delhi (2026).

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