Description

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Picture Courtesy: LIVEMINT

Context

The rapid integration of Artificial Intelligence (AI) and smart devices is reshaping the healthcare landscape, promising a future of personalized and proactive wellbeing.

Read all about: AI IN HEALTHCARE l INDIA AND WHO LAUNCH GLOBAL CALL FOR AI IN HEALTH l INDIA JOINS HEALTH AI GLOBAL NETWORK l AI IN MENTAL HEALTH: HELPFUL OR HARMFUL? l WHO ISSUES ETHICAL GUIDELINES FOR HEALTHCARE AI

AI in Indian Healthcare

AI in healthcare focuses on enhancing diagnostics (radiology, pathology), improving telemedicine (e-Sanjeevani), enabling personalized medicine, and boosting public health surveillance, while addressing rural access gaps.

Key Applications & Initiatives

Diagnostics: AI assists in detecting diseases from X-rays (like diabetic retinopathy), pathology slides, and medical images faster and more accurately, helping overcome specialist shortages.

Example: Bengaluru-based Niramai uses AI for non-invasive breast cancer screening, while Qure.ai helps detect tuberculosis and strokes from scans.

Telemedicine: Integrated into the e-Sanjeevani platform, AI provides Clinical Decision Support (CDSS) for differential diagnoses, improving consultation quality.

Public Health: Tools like 'Media Disease Surveillance' (MDS) track digital news for early outbreak detection, and AI aids in personalized care and predicting health risks.

Personalized Medicine: AI analyzes patient data (genetics, lifestyle) to tailor treatment plans, seen in initiatives like Apollo Hospitals' AI-Precision Oncology Center.

Drug Discovery: Machine learning accelerates identifying potential drug candidates, cutting costs and time.

Mental Health: Apps like Wysa offer AI-driven mental health support, bridging gaps in access.

Role & Impact

Force Multiplier: AI empowers nurses, technicians, and community health workers to perform early screenings, extending specialist reach.

Efficiency: Automates tasks, optimizes hospital operations (staffing, inventory), and reduces diagnostic errors.

Accessibility: Democratizes healthcare by connecting rural communities with urban specialists via AI-powered telemedicine.

Key Challenges to AI integration in Healthcare

Vulnerability to Cyberattacks

According to India Cyber Threat Report 2025, the healthcare sector accounted for 21.82% of all cyberattacks, making it the most targeted industry in India.

Case Study: AIIMS, Delhi Attack

In November 2022, ransomware attack on the All India Institute of Medical Sciences (AIIMS) compromised the data of millions of patients.

It paralyzed hospital services for over two weeks, highlighting the vulnerability of India's critical health infrastructure.

Algorithmic Bias

AI models using non-representative data can worsen health inequities. For example, an algorithm trained on one demographic's data may poorly diagnose diseases in others, causing health disparities.

Data Privacy & Consent

The complexity of "black-box" algorithms makes ensuring genuinely informed consent challenging, as patients and doctors struggle to understand AI's decision-making, raising ethical questions about data usage.

Accountability & Liability

Determining who is responsible for an AI-driven diagnostic error is a complex legal and ethical problem.

India's Legal and Policy Framework for Health Data

Foundational Legal Pillars

Puttaswamy Judgement (2017)

The Supreme Court affirmed the Right to Privacy as a fundamental right under Article 21 of the Constitution, establishing the constitutional basis for data protection laws, covering informational privacy and bodily integrity.

Digital Personal Data Protection Act (DPDPA), 2023: India's first comprehensive data protection law. For healthcare, it mandates:

Explicit Consent: Healthcare providers (Data Fiduciaries) must obtain clear and specific consent from individuals before processing their personal data.
Security Safeguards: Fiduciaries are legally obligated to implement strong security measures to prevent data breaches.
Breach Notification: Any data breach must be promptly reported to the Data Protection Board of India and the affected individuals.

Ayushman Bharat Digital Mission (ABDM)

Launched in 2021, ABDM aims to create a national digital health ecosystem based on the principle of "privacy by design."

Ayushman Bharat Health Account (ABHA): A unique 14-digit health ID allowing citizens to link and share their health records digitally.
Registries: Verified national databases of healthcare professionals (HPR) and health facilities (HFR).
Consent Manager: Individuals maintain control over their health record sharing, consenting to who receives which records and for how long.

Global Best Practices and Lessons for India

Framework	Region	Key Features & Lessons for India
GDPR (General Data Protection Regulation)	European Union	Considered the global gold standard for data protection. Enforces strict principles like data minimisation (collecting only necessary data) and the 'right to be forgotten'. Lesson: India can adopt a similar model of imposing strong penalties for non-compliance to ensure accountability.
HIPAA (Health Insurance Portability and Accountability Act)	USA	A sector-specific law designed exclusively to protect patient health information (PHI). Sets national standards for the security and confidentiality of electronic health records. Lesson: While DPDPA is a general law, India could benefit from developing sector-specific codes of practice for healthcare, providing clearer guidance on handling sensitive data, similar to HIPAA's focused approach.

Way Forward

India must adopt a comprehensive strategy to harness AI's potential in healthcare while safeguarding citizens' rights. This strategy depends on:

Robust Regulatory Implementation: Clearly and effectively enforce the DPDPA, 2023, specifically tailoring its rules for the health sector.
Ethical Oversight: Develop an enforceable ethical AI framework to mitigate and prevent algorithmic bias.
Cybersecurity Enhancement: Strengthen the cybersecurity infrastructure of all healthcare institutions.
Privacy Innovation: Promote Privacy-Enhancing Technologies (PETs), such as federated learning.
Citizen Empowerment: Launch nationwide digital literacy campaigns to better inform and empower the public.

Conclusion

Balancing technological innovation with public trust is vital for establishing a resilient, equitable, and patient-centric digital health ecosystem that benefits every Indian.

Source: LIVEMINT

Attempt Daily Quiz

PRACTICE QUESTION

Q. Analyze the dual-use nature of AI in healthcare, where data collected for 'wellness' can potentially lead to 'biometric surveillance' by private corporations. (250 words)

Frequently Asked Questions (FAQs)

The DPDPA, 2023 aims to protect citizens' personal data by making it mandatory for healthcare providers (Data Fiduciaries) to obtain explicit consent before collecting or processing data, implement strong security measures to prevent breaches, and promptly notify both the authorities and affected individuals if a breach occurs.

ABDM ensures privacy through its "privacy by design" approach. Its core feature, the 'Consent Manager', empowers individuals by giving them direct and granular control over their health data. Patients can decide which records to share, with which doctor, and for how long, ensuring that all data exchange is based on their explicit and informed consent.

Algorithmic bias occurs when an AI system produces prejudiced results because it was trained on biased or non-representative data. In healthcare, this is a major concern as an AI trained on data from one demographic group might misdiagnose or provide incorrect treatment recommendations for other groups, thus worsening existing health inequities.