When Bias Wears a White Coat: Generative AI in Healthcare Raises Ethical Alarms

🧭 Thematic Focus

Category: Science & Tech | Ethics | Health
GS Paper: GS Paper III – Science and Technology
Tagline: When empathy is coded, vigilance must be constant.

🧪 Key Highlights

💡 What is Generative AI in Healthcare?

• AI models (LLMs) generate diagnoses, triage steps, and treatment plans
• Used in emergency rooms, virtual care, and treatment suggestion systems
• Based on patterns from large health datasets

🧬 Study Findings

• 1.7 million emergency case simulations evaluated
• AI models gave unequal recommendations based on race, income, gender
• High-income patients were more likely to get advanced testing
• Marginalised groups (e.g., Black transgender patients) often flagged for mental health, regardless of actual symptoms

📉 Impact and Inequity

• Perpetuates systemic bias in healthcare delivery
• May deepen health disparities across socioeconomic lines
• Undermines the objectivity and promise of AI-driven care

🛠️ Why Does Bias Occur?

• Training data contains real-world prejudices
• Underrepresentation of minority populations in datasets
• Lack of contextual understanding or cultural sensitivity in models

🩺 What Can Be Done?

✅ Recommendations

• Bias audits of AI models must be mandatory
• Ensure diverse and inclusive datasets
• Promote transparency in AI training and decision logic
• Establish oversight bodies for ethical AI use in health

🤝 Human-AI Collaboration

• Doctors must review AI outputs, not follow blindly
• Especially important for vulnerable or marginalised patients
• AI should support, not replace, clinical judgement

🧠 GS Paper Mapping

• GS Paper II: Ethics in Governance, Health
• GS Paper III: Technology & Its Impact on Society, AI Governance
• GS Paper IV: Case Study Potential – Bias, Fairness, Accountability

💭 A Thought Spark — by IAS Monk

“Bias in steel robes is still bias.
When algorithms judge, let wisdom whisper louder.”