Context

The prospect of providing a “free AI-powered primary-care physician for every Indian, available 24/7” within the next five years is certainly ambitious. It prompts important questions about the feasibility, sustainability, and preparedness of India to address such a significant challenge.

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• Primary health care (PHC) aims to provide the highest level of health by integrating services within communities, addressing health needs, and empowering individuals. Relying on AI could undermine this by being impersonal and turning people into passive recipients of care rather than active participants.
• While AI is effective at processing and automating repetitive tasks, it falls short in areas critical to medicine, such as understanding the physical world, retrieving complex information, maintaining persistent memory, and engaging in reasoning and planning. These human traits are essential for grasping the nuances of a patient's condition, which extends beyond mere pattern recognition.
• Healthcare requires empathy and cultural understanding, which are rooted in human consciousness and ethical reasoning—traits AI lacks. Unlike other fields, healthcare data is often scattered, incomplete, and inaccessible, complicating AI training and limiting its effectiveness.

"Data, Models, and Challenges in Healthcare AI"

• Naegele’s rule: used for over 200 years in obstetrics to estimate a child’s birth date, highlights current challenges in healthcare. Based on 18th-century European reproductive habits, this method relies only on the length of the last menstrual cycle and has only 4% accuracy. It neglects important factors like maternal age, parity, nutrition, height, race, and uterus type, which are crucial for accurate predictions.
• Creating a more precise model: more precise than Naegele’s rule requires extensive personal data, which raises privacy and ethical issues. This illustrates a paradox in AI development. The need for comprehensive data to enhance accuracy conflicts with concerns about privacy. Additionally, the substantial costs of establishing and maintaining infrastructure for data collection and AI training are compounded by the need for ongoing adjustments as reproductive health and fertility rates evolve. The complexity and personal nature of healthcare data further complicate efforts to standardize it across different populations.
• India’s diversity: adds another layer of complexity. To develop effective AI models, data needs to be both extensive and highly contextualized. However, generating this data requires access to personal and behavioural information.

AI’s utility in health care

• Highly effective in specific area: AI can be highly effective in specific, well-defined healthcare tasks through narrow intelligence, diffusion models, and transformer.
  • Narrow intelligence excels in specialized tasks like predicting hospital supply needs or managing biomedical waste.
  • Diffusion models, which analyze complex datasets, can aid in tasks such as screening histopathology slides or analyzing medical images.
  • Large Language Models (LLMs) and Large Multimodal Models (LMMs) are proving to be valuable in medical education and research. They offer quick access to medical knowledge, simulate patient interactions, and aid in training healthcare professionals. By providing personalized learning experiences and simulating complex clinical scenarios, these models enhance traditional medical education.
• Black box: a major challenge with AI in healthcare is the "black box" problem, where the decision-making processes of AI algorithms are opaque and difficult to interpret. This lack of transparency is problematic in healthcare, where understanding the rationale behind diagnoses and treatment plans is essential.
• Surety of AI: Healthcare providers are often unsure how AI arrives at its conclusions, which can erode trust and pose risks if the AI makes incorrect or inappropriate recommendations. While Google Deep Mind’s AI achievements in defeating world-class GO players are impressive in the realm of games, such feats highlight concerns when applied to real-life healthcare. The stakes are much higher in medicine, where mistakes can have potentially life-threatening consequences.

AI Governance in India: Challenges and Considerations

• A recent petition : by  a content moderators in Kenya against OpenAI’s ChatGPT has highlighted the ethical issues in AI development, including the exploitation of underpaid workers in training AI models. This raises concerns about the potential exploitation of vulnerable populations in AI training, emphasizing the need to protect the interests of Indian patients, as the data used for training belongs to them.
• AI tools must adhere to core medical ethic: Although population-level data from health systems can be valuable, it is susceptible to ecological fallacy. India currently lacks comprehensive AI regulations, unlike the European Union's Artificial Intelligence Act, making robust governance even more crucial. AI tools in healthcare must adhere to the core medical ethic of "Do No Harm."
• AI has the potential to enhance efficiency and reduce errors: In Indian health care However, advanced AI technologies require substantial investments in research, data infrastructure, and ongoing updates, imposing significant costs. India cannot transition to AI-driven healthcare without addressing foundational issues in its health system. The complexities of patient care, the need for high-quality data, and the ethical implications of AI necessitate a cautious and well-considered approach.

Conclusion

While AI holds the potential to transform the Indian health system, its successful integration requires a careful, balanced approach. This includes addressing foundational issues in data quality and privacy, establishing clear regulatory frameworks, and ensuring that AI systems complement rather than replace the human elements crucial to healthcare. By taking these steps, India can harness the benefits of AI while maintaining the integrity and effectiveness of its healthcare system.