- Flat No-1502, TOWER-D, Emaar Palm Heights, Sector 77, Gurugram, Haryana 122004
- info@varalegal.com
- +91-9821127838
India’s healthcare sector is rapidly adopting artificial intelligence for diagnostics, insurance assessments, telemedicine, and hospital management. However, concerns are growing that healthcare algorithms may unintentionally discriminate against vulnerable populations.
A major concern in India is data imbalance. Many AI healthcare systems are trained using urban hospital data, while rural populations, lower-income groups, and regional communities remain underrepresented. This creates a risk that algorithms may perform accurately for privileged populations but poorly for others.
During the COVID-19 pandemic in India, concerns were raised about digital exclusion in healthcare systems such as telemedicine platforms and the CoWIN vaccine registration portal. Many citizens without smartphones, reliable internet access, or digital literacy faced difficulties accessing healthcare services and vaccine appointments. The Supreme Court of India itself acknowledged the country’s “digital divide” while questioning mandatory online registration policies for vaccination. Legal scholars and policy experts argued that excessive dependence on digital systems could disproportionately disadvantage.
India’s legal framework is also evolving around health data and algorithmic accountability. The Digital Personal Data Protection Act has increased attention on consent, data usage, and automated processing of sensitive personal data, including health information.
In addition, the NITI Aayog has issued discussions and strategy papers emphasizing responsible AI and the need to prevent bias in healthcare technologies.
The challenge for India is unique: if healthcare algorithms are trained on unequal social and economic realities, technology may unintentionally deepen existing healthcare inequalities instead of reducing them.
As AI adoption expands, Indian hospitals, insurers, and health-tech startups may increasingly face legal scrutiny over transparency, fairness, and accountability in automated healthcare decisions.
