Governance
AI in Healthcare
- 13 Jun 2025
- 13 min read
For Prelims: Garbhini-GA2, Telemedicine, World Health Organization, SARAH, eSanjeevani, Digital Personal Data Protection Act, 2023, National Health Policy (2017), Ayushman Bharat Digital Mission (ABDM), National Data and Analytics Platform (NDAP), BharatNet, National Health Authority, CDSCO (Central Drugs Standard Control Organization),
For Mains: Use of AI in healthcare, Major challenges of AI in healthcare in India and suggested measures to deal with them.
Why in News?
Indian researchers have developed Garbhini-GA2, an Artificial Intelligence (AI) model that predicts fetal age from ultrasound images with an error margin of just half a day, outperforming current methods with an error of up to 7 days. This development highlights the vast potential of AI to drive advancements in healthcare in India.
What are the Applications of AI in Healthcare?
- Early Disease Detection and Diagnosis: AI tools assist doctors in analyzing medical images like X-rays, CT scans, and ultrasounds quickly and accurately—vital for countries with limited specialists.
- AIIMS Delhi has launched an AI platform - iOncology.ai. - designed for the early detection of breast and ovarian cancer.
- Also, Mumbai-based Qure.ai detects TB, pneumonia, and lung cancer from chest X-rays, while Bengaluru startup NIRAMAI uses AI-powered thermal imaging to identify early-stage breast cancer without radiation.
- AI in Telemedicine and Remote Consultations: AI-driven telemedicine is bridging gaps in rural healthcare by improving access and efficiency.
- Tools like Practo’s AI chatbot and Apollo’s "Ask Apollo" assistant offer symptom-based guidance, instant medical advice, and appointment scheduling, reducing unnecessary hospital visits.
- AI for Drug Discovery: Indian startups and research labs are using AI to create affordable, patient-specific treatments.
- E.g., Bengaluru-based InnAccel developed SAANS, an intelligent, infrastructure-free, multi-therapy system that delivers non-invasive breathing support for neonatal and pediatric patients, helping reduce infant mortality in rural clinics.
- AI in Wearables: AI-powered wearables and apps are enabling Indians to manage chronic diseases like diabetes and hypertension more effectively.
- E.g., Delhi-based BeatO offers an AI-enabled glucometer that tracks blood sugar levels and gives real-time diet and medication recommendations.
- AI for Hospital Efficiency: Hospitals are using AI to reduce administrative workload and improve operational efficiency.
- E.g., Microsoft’s AI Network for Healthcare has partnered with eye hospitals in India to predict the progression of diabetic retinopathy, helping prevent blindness in high-risk patients.
- Enhancing Medical Education and Training: AI is transforming medical education and training through personalized learning and simulation of complex clinical scenarios.
- Platforms like FundamentalVR use AI-powered VR and haptic systems for realistic surgical practice, while adaptive learning tools customize curricula, enhancing training efficiency and competency.
What are the Key Initiatives Enabling the Adoption of AI in India’s Healthcare System?
- Ayushman Bharat Digital Mission (ABDM): ABDM provides a unique digital health ID for each citizen.
- HealthLocker/Personal Health Records (PHR): It is a digital national health database backed by a cloud-based storage system, serving as a single source of health data for the nation.
- National Health Stack (NHS): It includes platforms like the National Health Analytics Platform, supporting data-driven healthcare solutions.
Note: The World Health Organization has launched S.A.R.A.H. (Smart AI Resource Assistant for Health), a generative AI prototype that uses advanced language models to deliver reliable information on key health topics like mental health, healthy habits, and non-communicable diseases (e.g., cancer, heart disease, lung disease, diabetes).
What are the Major Challenges of AI in Healthcare in India?
- Lack of High-Quality, Standardized Medical Data: AI models require large, diverse, well-labeled datasets, but face limitations in India due to fragmented data—as most hospitals still rely on handwritten prescriptions and non-digital records.
- Additionally, AI trained on Western data often performs poorly in India because of differences in lifestyle and disease patterns.
- Limited AI Infrastructure in Rural Areas: Advanced AI tools need high-speed internet, cloud computing, and digital healthcare systems, which are often lacking in rural India.
- Platforms like eSanjeevani and tools like Qure.ai’s TB detection face challenges in remote areas and PHCs due to poor connectivity and lack of digital infrastructure (e.g., digital X-ray machines).
- Regulatory and Ethical Concerns: India lacks a clear AI governance framework, leading to concerns over patient privacy, bias, and accountability.
- While the Digital Personal Data Protection Act, 2023 sets strict rules on health data use, weak enforcement and cases of AI bias hinder safe AI deployment.
- Also, the Digital Information Security in Healthcare Act (DISHA), proposed by the Ministry of Health & Family Welfare in 2017 to regulate digital health data, remains unenacted.
- Language and Localization Issue: India’s linguistic diversity, with 22 official languages and numerous dialects, poses a major challenge for AI implementation in healthcare.
- This language barrier can cause misdiagnosis, miscommunication, and reduce the effectiveness of AI tools.
- Resistance from Healthcare Professionals: Doctors and nurses often show distrust towards AI, fearing job loss or potential misdiagnosis.
- Many remain reluctant to use AI for critical decisions, favoring traditional clinical methods instead.
ICMR Guidelines for AI Use in the Health Sector
In March 2023, the Indian Council of Medical Research (ICMR) released the "Ethical Guidelines for Application of AI in Biomedical Research and Healthcare," outlining 10 key patient-centric ethical principles for the use of AI in healthcare.
- 10 Guiding Principles:
- Accountability and Liability: Regular audits to ensure optimal AI performance, with findings made public.
- Autonomy: Mandatory human oversight and informed patient consent, including risk disclosure.
- Data Privacy: Protection of privacy and personal data at every stage of AI use.
- Collaboration: Encourages interdisciplinary and international partnerships for responsible AI development.
- Safety and Risk Minimization: Focus on misuse prevention, data security, and ethical review by committees.
- Accessibility, Equity, and Inclusiveness: Ensure AI infrastructure is accessible to all, bridging the digital divide.
- Data Optimization: Minimize biases and errors from poor data quality or lack of representation.
- Non-Discrimination and Fairness: Promote universal access to bias-free AI technologies.
- Trustworthiness: Ensure AI is valid, reliable, ethical, and lawful to build user confidence.
- Transparency: Provide clinicians with clear methods to test AI's validity and reliability.
Frameworks: India's frameworks supporting AI in healthcare include the Digital Health Authority under the National Health Policy (2017), DISHA 2018, and Medical Device Rules, 2017.
How Can India Effectively Integrate AI into Healthcare?
- Build High-Quality, Localized Healthcare Datasets: India should expand the Ayushman Bharat Digital Mission (ABDM) to standardize electronic health records (EHRs) across hospitals and leverage platforms like the National Data and Analytics Platform (NDAP) for anonymized AI training data.
- Leading hospitals like AIIMS, Apollo, and Tata Memorial can share de-identified data with AI startups (e.g., Qure.ai, SigTuple), ensuring datasets represent rural populations, women, and ethnic minorities to reduce bias.
- Strengthen AI Infrastructure in Rural Healthcare: eSanjeevani can integrate offline-capable AI symptom checkers for use in low-connectivity areas.
- ASHA workers can be equipped with AI tools (e.g., portable ultrasound devices like Butterfly Network’s) while BharatNet and Jio’s 5G can support cloud-based AI radiology in district hospitals.
- Establish Clear AI Regulations & Ethical Guidelines: CDSCO (Central Drugs Standard Control Organization) should establish clear approval pathways for AI-based diagnostics, similar to the US’s AI/ML Action Plan, while NITI Aayog’s Responsible AI guidelines must be enforced in healthcare.
- Mandatory algorithm audits (for caste/gender bias) and a strengthened Digital Personal Data Protection Act, 2023 are essential to protect patient data and ensure ethical AI use.
- Train Doctors & Build AI Awareness: Include AI and Digital Health modules in MBBS and nursing curricula, and let the National Health Authority train doctors in using AI tools like predictive analytics.
- AI developers must offer clear explanations for algorithmic decisions to ensure clinical trust and transparency.
- Launch Public Awareness Campaigns: To build patient trust and acceptance, India should launch public awareness campaigns explaining the benefits and limitations of AI in healthcare in simple, relatable terms.
- Using media channels like social media, TV, and community outreach, and following models like the Pulse Polio campaign, can boost AI awareness and adoption.
Conclusion
AI in Indian healthcare offers transformative potential—enhancing diagnostics, telemedicine, and drug discovery—but faces challenges like data bias, infrastructure gaps, and regulatory hurdles. Effective integration requires robust datasets, rural AI adoption, clear regulations, and clinician training. Ethical frameworks like ICMR’s guidelines ensure responsible AI use, balancing innovation with patient safety and equity.
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Drishti Mains Question: Q. Discuss the role of Artificial Intelligence in transforming India’s healthcare system. What challenges hinder its effective implementation? |
UPSC Civil Services Examination, Previous Year Question (PYQ)
Prelims:
Q. With the present state of development, Artificial Intelligence can effectively do which of the following? (2020)
- Bring down electricity consumption in industrial units
- Create meaningful short stories and songs
- Disease diagnosis
- Text-to-Speech Conversion
- Wireless transmission of electrical energy
Select the correct answer using the code given below:
(a) 1, 2, 3 and 5 only
(b) 1, 3 and 4 only
(c) 2, 4 and 5 only
(d) 1, 2, 3, 4 and 5
Ans: (b)
Mains:
Q.1 What are the areas of prohibitive labour that can be sustainably managed by robots? Discuss the initiatives that can propel the research in premier research institutes for substantive and gainful innovation. (2015)
