As AI enters healthcare, doctors grapple with safety and oversight

  • February 23, 2026
  • Science and Tech

Indian Express

As AI enters healthcare, doctors grapple with safety and oversight

 

I. Core Context

The article explores the growing integration of Artificial Intelligence (AI) in healthcare, particularly in diagnostics and clinical decision-making, while highlighting concerns related to safety, bias, regulatory oversight, and accountability.

It frames AI as both:

  1. A transformative medical tool
  2. A source of ethical and governance dilemmas

II. Key Arguments Presented

1. AI’s Diagnostic Promise

The article underscores that AI can:

  1. Detect diseases early (e.g., radiology, pathology, ophthalmology)
  2. Improve efficiency in clinical workflows
  3. Reduce costs in the long run
  4. Support physicians in decision-making

AI is presented as augmentative rather than fully autonomous.

2. Data Bias and Representation Risks

A central concern raised is:

  1. AI models trained on limited or skewed datasets
  2. Underrepresentation of certain populations
  3. Risk of misdiagnosis due to demographic bias

Healthcare inequity may be exacerbated if datasets lack diversity.

3. Transparency and Explainability

Doctors express unease over:

  1. “Black-box” algorithms
  2. Lack of interpretability
  3. Difficulty in tracing decision logic

Clinical responsibility becomes complex when AI recommendations are opaque.

4. Regulatory and Oversight Gaps

The article highlights:

  1. Lack of clear regulatory frameworks
  2. Need for clinical validation
  3. Ethical review mechanisms

Approval processes must evolve alongside AI development.

III. Author’s Stance

The tone is balanced but cautious.

The author does not reject AI in healthcare but stresses:

  1. Need for rigorous oversight
  2. Ethical safeguards
  3. Clinical validation
  4. Human supervision

It reflects a reformist rather than alarmist perspective.

IV. Possible Biases and Limitations

1. Emphasis on Risks

While concerns are valid, the article may understate:

  1. Documented improvements in diagnostic accuracy
  2. AI’s role in rural and resource-poor settings
  3. Long-term cost efficiencies

2. Limited Discussion on Implementation Ecosystems

The article does not fully explore:

  1. Hospital readiness
  2. Integration with electronic health records
  3. Infrastructure constraints in developing countries

3. Global Regulatory Comparison Missing

The piece could have compared:

  1. US FDA frameworks
  2. EU AI Act provisions
  3. India’s emerging digital health policies

V. Pros and Cons of AI in Healthcare

Pros

• Early disease detection
• Reduced diagnostic error
• Enhanced workflow efficiency
• Potential cost reduction
• Expanded access in underserved regions

Cons

• Algorithmic bias
• Lack of explainability
• Accountability ambiguity
• Data privacy concerns
• Overdependence on technology

VI. Policy Implications

1. Regulatory Architecture

India must develop:

  1. Risk-based AI classification
  2. Mandatory clinical trials for AI tools
  3. Transparent audit frameworks

Regulation should ensure safety without stifling innovation.

2. Data Governance

Key priorities include:

  1. Diverse and representative datasets
  2. Patient consent protocols
  3. Data protection compliance

Bias mitigation must be institutionalised.

3. Capacity Building

Doctors require:

  1. AI literacy
  2. Interdisciplinary training
  3. Clear medico-legal guidelines

Human oversight remains indispensable.

4. Ethical Oversight

Institutional ethics committees should:

  1. Review AI deployment
  2. Monitor real-world outcomes
  3. Enforce accountability mechanisms

VII. Real-World Impact

Short-term:

  • Improved diagnostic support
  • Professional scepticism among clinicians

Medium-term:

  • Gradual integration in radiology, oncology, cardiology
  • Legal disputes over liability

Long-term:

  • Transformation of healthcare delivery models
  • AI-human collaborative medicine

VIII. UPSC Relevance

GS Paper II

• Health governance
• Data protection and privacy
• Regulatory institutions

GS Paper III

• Science and technology in healthcare
• Artificial Intelligence applications
• Ethical dimensions of technology

GS Paper IV

• Accountability in automated decision-making
• Ethical responsibility in public service delivery
• Human vs machine decision dilemmas

Essay Themes

• Technology and ethics
• AI and human judgment
• Innovation with responsibility

IX. Balanced Conclusion and Future Perspective

AI in healthcare is not a question of if, but how.

Its potential to revolutionise diagnostics and expand access is undeniable. Yet healthcare decisions involve life, dignity, and trust—domains that demand transparency and accountability.

The future lies in:

  1. Human-in-the-loop systems
  2. Evidence-based validation
  3. Robust regulatory frameworks
  4. Ethical data governance

AI should not replace doctors; it should enhance clinical judgment under clearly defined oversight.

Technological advancement without governance risks undermining trust. With careful design and regulation, however, AI can become a cornerstone of equitable and efficient healthcare systems.