[Answered] Evaluate the integration of AI in healthcare through the lens of patients’ rights and health equity. Analyze how India can balance technological efficiency with the necessity of retaining human-centric care as the backbone of its public health architecture.

Introduction

India spends nearly 2.1% of GDP on health, while out-of-pocket expenditure remains above 45% (National Health Accounts). As AI expands in diagnostics and surveillance, rights-based integration becomes imperative.

AI in Healthcare: Promise and Practical Limits

1. Artificial Intelligence (AI) has demonstrated value in radiology, pathology, predictive analytics, and workflow optimisation.
2. AI-enabled Clinical Decision Support Systems (CDSS) improve tuberculosis screening and diabetic retinopathy detection in pilot projects.
3. Platforms like eSanjeevani have expanded teleconsultations to rural populations, showcasing AI-assisted scalability.
4. However, systematic reviews in global medical journals show that algorithms performing well in controlled trials often underperform in heterogeneous real-world contexts due to data variability and contextual complexity.

Patients’ Rights in the Algorithmic Era

A rights-based framework requires reinterpreting healthcare obligations under Article 21 (Right to Life and Health).

1. Algorithmic Transparency and Explainability: Deep learning systems often operate as black boxes. Patients must know when AI informs diagnosis or triage decisions. The principle of Explainable AI (XAI) becomes central to informed consent and medical accountability.
2. Data Sovereignty and Privacy Protection: The Digital Personal Data Protection Act 2023 categorises health data as sensitive personal data. Data fiduciaries must ensure anonymisation, purpose limitation, and revocable consent—preventing digital extractivism where private platforms monetise patient data without proportional public benefit.
3. Right to Human Review and Non-Exclusion: No patient should be denied care for opting out of AI-mediated pathways. International bioethics standards emphasise human-in-the-loop safeguards for life-critical decisions.

AI and Health Equity: Bridging or Deepening Divides

1. Addressing Rural-Urban Disparities: AI-enabled screening tools can empower ASHA workers and primary health centres in underserved districts, mitigating specialist shortages (doctor-population ratio ~1:834 as per WHO benchmark alignment).
2. Mitigating Algorithmic Bias: If models are trained predominantly on urban, digitised datasets, they risk reinforcing caste, gender, and regional inequities—violating Article 14 (Equality). Mandatory bias audits and representative datasets are essential to avoid discriminatory outputs.
3. Language and Accessibility Inclusion: Multilingual AI interfaces can democratise access, ensuring comprehension among diverse populations rather than privileging English-speaking urban elites. Yet, equity concerns arise if AI deployment favours tertiary private hospitals over strengthening public primary healthcare under Ayushman Bharat and Health and Wellness Centres.

Political Economy and the Risk of Techno-Solutionism

1. AI integration occurs within broader structural constraints: underinvestment in public health, workforce shortages, and regulatory gaps. Overreliance on commercial platforms risks corporatisation and elite capture of care.
2. If publicly funded datasets and digital infrastructure generate proprietary algorithms for private gain, distributive justice concerns emerge. AI must be treated as a Digital Public Good, not merely a profit-maximising platform.

Human-Centric Care as the Backbone

Healthcare transcends pattern recognition; it involves empathy, ethical judgement, and contextual understanding.

1. Augmentation, Not Substitution: AI can reduce administrative burdens—voice-to-text documentation, automated triage, epidemiological forecasting—freeing physicians for relational care.
2. Labour Impact Safeguards: Approval of AI tools should include workforce impact assessments, ensuring no arbitrary displacement or algorithmic surveillance of frontline workers such as ASHAs.
3. Institutionalising Accountability: Centres of Excellence in AI-health research (e.g., AIIMS initiatives) must embed ethical review boards, audit trails, and grievance redress mechanisms.

Balancing Efficiency with Ethical Governance

India can adopt a Public Health Systems Approach by:

1. Mandating explainability and bias audits.
2. Ensuring AI-supported services remain free at point of use within public systems.
3. Strengthening primary healthcare infrastructure before scaling high-end AI.
4. Embedding community participation in digital health governance.
5. Such measures reconcile technological efficiency with constitutional morality and social justice.

Conclusion

As Mahatma Gandhi reminded, ‘The best way to find yourself is to lose yourself in the service of others.’ AI in healthcare must remain a servant of human dignity, not its substitute.