The Indian healthcare industry is growing significantly due to increased government and corporate investments. However, many challenges remain in terms of affordability, accessibility, and quality of care. These challenges are most noticeable in underserved and rural areas, where a lack of developed healthcare infrastructure exists.  

As an effective tool for addressing these challenges, artificial intelligence (AI) can enable personalized treatment while enhancing diagnostic accuracy, optimizing resource allocation, and reducing costs. AI has even more potential, providing innovative solutions that help accelerate India’s advancement toward its long-term healthcare goals.   

In this context, Nasscom and Kantar have released a report titled Advancing Healthcare in India: Navigating the Transformative Impact of AI. The report covers the AI focus, adoption, and maturity trends across various stakeholder categories and offers a comprehensive playbook for stakeholders, aiming to drive AI adoption and enhance the overall efficiency and effectiveness of the Indian healthcare ecosystem.  

The Report Unveils AI’s Potential in Indian Healthcare  

The report illuminates the need for AI adoption in Indian healthcare and explores how AI can offer solutions to fundamental problems like healthcare quality, accessibility, and affordability. Additionally, it covers AI adoption and maturity trends across all stakeholder categories in the Indian healthcare ecosystem, including healthcare providers, medical device/equipment manufacturers, life sciences and pharmaceutical companies, while highlighting major use cases and application areas.  

This report also aims to provide a comprehensive guide for the strategic adoption of AI in the Indian healthcare sector, enabling India to achieve its long-term goals around universal healthcare coverage, public health, and advanced pandemic prediction and mitigation. The playbook addresses the critical components required to effectively adopt AI, including infrastructure, skills, planning and implementation, scalability, and regulatory frameworks.  

AI to Bridge Gaps in Accessibility and Quality  

The Indian healthcare sector experienced a 22.5% CAGR between 2016 and 2022, with an expected value of USD 650 Bn by 2025. This growth is attributed to increased public health spending, medical infrastructure investments, and home healthcare and telemedicine growth.  

Despite public health spending doubling from 1.3% of GDP in 2016 to ~2.6% in 2023, out-of-pocket expenditure still constitutes 54.8% of total health spending. The mass-scale adoption of AI-enabled solutions can address the critical gaps that our country faces in terms of accessibility, affordability, and quality, along with the hospital bed-population ratio and doctor-population ratio, which are now lower than those of global peers. This is expected to help India break the classic Iron Triangle of healthcare.  

As per the report, the AI in healthcare market is expected to reach USD 1.6 Bn by 2025, growing at a 40.6% CAGR.  

AI Adoption Rates and Roadblocks  

The report found that in the pharma and life sciences sector, AI can reduce drug discovery costs, achieve faster time to market, and improve clinical success, with 82% adopting AI at a small scale. However, only 12% have integrated AI into their functional processes.  

In MedTech, AI can improve medical device quality and accuracy, but adoption is in its infancy. Healthcare providers have a high adoption rate of 68%, but 92% are in the PoC phase. Gen AI adoption in India is nascent, with 34% piloting projects and 16% moving cases to production.  

Five Foundational Elements for AI Success in Indian Healthcare  

AI can enhance operational efficiency, accuracy, and speed in the short term while integrating with electronic health records for preventive care and providing personalized treatments, which can improve long-term healthcare coverage. To fully utilize AI in the Indian healthcare sector, five foundational elements must be in place: infrastructure, skills, planning, implementation, scalability, and regulatory frameworks.  

• AI in healthcare requires robust infrastructure for advanced diagnostics, patient care, and data-driven decision-making. High-performance computing systems, broadband enhancement, cloud-based platforms, and standardized EHR systems are essential for seamless data integration.  
• Equipping healthcare professionals and policymakers with AI skills is essential to harness AI’s full potential for improved healthcare outcomes and operational efficiency. The focus should be on implementing AI training for healthcare professionals and policymakers, fostering interdisciplinary collaboration, and establishing robust AI talent management and upskilling programs.   
• Successful AI integration in healthcare requires strategic planning and implementation, including business case development, AI budget allocation, identification of high-impact use cases, readiness assessments, and governance frameworks.   
• A robust regulatory framework is needed to promote safe and ethical AI adoption in healthcare and ensure compliance with patient privacy. Streamlined approval processes, robust data privacy and security regulations, and responsible AI practices are needed to build this. 
• Scalability is crucial for AI adoption in healthcare. It enables adaptable solutions across various environments and patient needs. Companies should develop modular AI solutions, conduct pilot projects, and develop prototypes for successful scaling. 

Source: Full report