4. Conclusion: Considerations for AI and Healthcare in India
AI can undoubtedly bring new efficiencies and quality to healthcare outcomes in India. However, gaps and challenges in the healthcare sector reflect deep-rooted issues around inadequate funding, weak regulation, insufficient healthcare infrastructure, and deeply embedded socio-cultural practices. These cannot be addressed by AI solutions alone.
Moreover, technological possibility cannot be equated to adoption. In India, poor digital infrastructure, a large, diverse and unregulated private sector, and variable capacity among states and medical professionals alike, mean that the adoption of AI is likely to be slow and deeply heterogeneous. The same factors also make it quite likely that well-established private hospitals will be the main adopters. This in turn would imply that much of the dominant narrative or rationale for the development of AI in healthcare, in terms of improving equity and quality, is unlikely to be addressed through market forces alone: these solutions are more likely to serve populations who already have access to high-quality care, typically in cities with well-developed digital infrastructure. In many small hospitals and single-provider practices in India, administrative systems have barely moved beyond rudimentary ICT solutions such as invoicing and billing platforms.
The effectiveness of these systems will depend on accurate identification of problems and their matching to appropriate solutions. Currently, there is a risk that solutions are technology-led rather than problem-led, and they are as a result often blind to specific contextual needs or constraints. For example, it might not be the best approach to design real-time or synchronous solutions for digital products meant to be used in remote areas where basic internet infrastructure is lacking. Designing the right digital interventions is often challenging because of the digital divide between the user and the technology developers, who are typically more adept at using technology than the user is (Deo and Tyagi, 2019). Finally, issues around privacy, misuse and accountability are only slowly being understood, and require much more far-reaching consideration before AI can deliver safe and fair healthcare solutions.