In a recent report titled ‘Unlocking AI’s Potential in India’, Boston Consulting Group (BCG) underscores the transformative potential of AI in revolutionising India’s healthcare landscape. The report reveals that AI-driven diagnostic support can accelerate radiology reporting by 46 per cent, significantly reduce mammography costs by up to 66 per cent, and lower the cost of tuberculosis diagnosis by substantial margins. Moreover, the integration of telemedicine and AI-assisted screenings is enabling access to quality healthcare for millions in rural and underserved areas, thereby helping bridge longstanding gaps in the country’s healthcare system.
How would you define the current state of AI adoption in India’s healthcare sector? Are we keeping pace with global trends or falling behind? Additionally, which areas do you believe will see the most significant impact from AI adoption in the Indian healthcare sector?
AI adoption in India’s healthcare sector is evolving on two fronts. Among larger private players, we’re seeing real momentum—many are moving beyond Proof-of-Concept to scaled deployment of AI-based solutions. On the public health side, the potential is immense, especially in addressing what we call the “Bermuda Triangle” of healthcare: Accessibility / Reach, Quality, and Cost. AI can help break this tricky balance and trade-off between the three by improving reach and quality without proportionally increasing cost.
Early-stage pilots are encouraging, and several startups are making real progress. However, scaling remains a challenge—particularly at the last mile. Some solutions may require viability gap funding to overcome affordability barriers for a period, before they can establish longer-term viability. Companies like Apollo, NIRAMAI, Qure.ai, and Forus Health are already demonstrating AI’s value in early cancer detection, both in terms of cost efficiency and clinical impact.
Speaking of cost, reports indicate that AI-driven solutions have the potential to reduce healthcare costs by up to 66 per cent. Could you elaborate on which areas are seeing the most significant cost benefits? Additionally, do you foresee these AI-enabled efficiencies translating into lower healthcare expenses for patients, or will they primarily benefit healthcare providers and insurers?
Diagnostics and early detection are seeing the most visible cost benefits. For instance, AI-based breast cancer screening and TB diagnostics using radiology tools have shown significant reductions in cost—up to 66 per cent in some cases.
Beyond diagnostics, AI is streamlining workflows—for example, AI-powered chatbots for patient intake and triage are reducing administrative burden. While providers and insurers do benefit from these efficiencies, the ultimate goal is to pass these benefits on to patients—making early screening and treatment more affordable, accessible, and timely. The real impact will be felt when these solutions are deployed at scale and market forces drive providers to pass on the value efficiently to the end users.
The report also highlights AI’s ability to reduce diagnosis times from three weeks to just two hours, with improved detection rates by 29 per cent. Aside from breast cancer, which diseases or conditions have seen the most notable improvements due to AI-powered diagnostics?
While oncology has been a strong starting point, AI’s role in diagnostics is rapidly expanding across specialties. One good example is the work on Diabetic retinopathy – the AI-powered screening tool is helping detect early signs of vision loss among diabetic patients, enabling timely intervention and preventing irreversible blindness. It’s already being deployed in clinical settings and has proven especially useful in low-resource environments where specialist access is limited.
Another promising innovation is Swaasa’s HEaR model, which leverages AI and bioacoustic foundation models to detect tuberculosis through cough sound analysis. This kind of non-invasive, scalable screening tool has huge potential in India, especially for large-scale public health programs.
AI is also being explored in mental health, where voice and speech pattern analysis is showing early promise in identifying conditions like depression and anxiety.
It’s important to emphasise: AI does not replace doctors or healthcare staff — it augments them. Much like an autopilot assists a pilot, AI enhances clinical decision-making by increasing speed, precision, and reach — especially in underserved areas.
AI-powered telemedicine and screenings are transforming healthcare in rural India. What role do you see the government playing in supporting the widespread deployment of AI-powered healthcare solutions in these regions? Are there gaps that still need to be addressed?
Government support is crucial in the initial days of any new technology or innovation but success will come from smart collaborations within the private sector. We don’t need massive infrastructure investments to make AI work in rural areas. Instead, we must focus on cost-effective, frugal innovation using smartphones, edge devices, and cloud-based AI tools that can integrate with existing public health infrastructure like PHCs.
The government can support this through integration into public schemes as a buyer of these services, initial viability funding support for accessibility in the under-served regions, and by enabling public-private partnerships that drive reach and scale.
While AI brings efficiency and scalability, it also raises concerns about data privacy, security, and workforce adaptation. What are your thoughts on addressing these challenges?
These are fundamental concerns that must be addressed proactively. First, data privacy and cybersecurity must be built into every solution—especially with the rise of cloud-based systems. Responsible AI frameworks are essential to ensure data is protected and ethically used.
Second, AI must be inclusive and non-discriminatory. Models should be rigorously tested to avoid bias, especially around gender, geography, or socioeconomic status unless clinically justified.
Lastly, workforce adaptation is key. While there has been initial scepticism, there is growing recognition that AI is not a threat—it’s a tool or an enabler. Continued training and integration support will be critical to build confidence and ensure AI is used effectively by healthcare professionals.
