Artificial Intelligence (AI) is changing the way healthcare works around the world, making it faster, more accurate, and more efficient. India is known for its progress in technology and medicine and AI has the potential to solve big challenges in healthcare and improve access to quality care for everyone. From enhancing diagnostic precision to optimising clinical workflows, AI adoption can make a big difference. However, despite its immense potential, the adoption of AI in Indian healthcare remains staggeringly low, with only 12 per cent of clinicians currently integrating AI-driven tools into their practice.
This slow adoption is due to several reasons, such as a lack of proper technology in many healthcare centres, unclear rules about using AI, challenges to working with big data and not enough training for doctors to use these tools confidently. But the future looks bright. Experts predict that AI use in Indian healthcare will skyrocket, with nearly 79 per cent of clinicians expected to adopt it in the next few years.
Government support, better digital infrastructure, and teamwork among healthcare providers, tech companies, and policymakers are expected to drive this growth.
In an interview with IndiaMedToday, three industry experts - Vineet Mehta, Co-Founder and CTO, Alyve Health; Jasdeep Singh, Group CEO, CARE Hospitals; and Jatin Mahajan, Secretary, Association of Diagnostics Manufacturers of India (ADMI) - talk about how AI is transforming healthcare in India. They share their thoughts on the challenges of adopting AI, the key growth drivers, and the areas where AI can have the biggest impact, like improving patient care and making healthcare more accessible.
Despite India's reputation as a leader in digital advancements, there is a lag in AI adoption in healthcare. Why is it so? Identifying the reasons behind this gap - such as infrastructure, regulatory support, and education, could illuminate pathways to accelerating adoption and bridging the digital divide in healthcare.
Vineet Mehta: Despite India’s technological advancements, the healthcare industry faces notable challenges in adopting AI -
1. Infrastructure:
● AI requires access to quality data, but many healthcare providers struggle to digitise and maintain patient records systematically, leading to fragmented and unreliable data.
● Smaller healthcare facilities, like clinics and rural hospitals, often lack advanced technology and equipment, limiting AI integration.
● The development of a centralised health records repository is still in its early stages, preventing AI from providing contextual and personalised care recommendations.
2. Regulatory Support:
● Regulatory frameworks for AI in healthcare are still evolving, and aligning with India’s Digital Personal Data Protection Act is a work in progress.
● Emerging data privacy laws are expected to clarify data usage, helping address health data sensitivity and enabling effective AI deployment.
● Innovation is often hindered by regulatory ambiguities, slowing down AI integration across healthcare systems.
3. Education and Awareness:
● Many healthcare providers remain unaware of AI’s potential benefits and are reluctant to alter existing workflows without clear incentives.
● The limited availability of healthcare professionals trained in AI is a significant barrier to developing and implementing AI-driven solutions.
● Patient hesitation towards AI-based care must be addressed through transparent, -based campaigns by healthcare providers and government initiatives.
Jasdeep Singh: India is a global leader in digital advancements, but our healthcare sector still lags in AI adoption, due to infrastructure gaps, regulatory challenges, and the need for specialised training. To bridge this, we need targeted investments in digital infrastructure, supportive regulatory frameworks, and focused skill-building for healthcare professionals. It’s not just about deploying technology, it’s about creating an environment that encourages digital transformation across the sector.
Jatin Mahajan: While India is considered a leader in digital advancements, artificial intelligence (AI) adoption in the healthcare sector has lagged far behind in comparison to other sectors. Numerous factors contribute as inhibitors:
- Basic facilities in rural and semi-urban healthcare setups do not support effective AI systems. According to a recent study, 55 of rural healthcare centres do not even have reliable electricity backup.
- India’s mix of public and private healthcare systems, non-harmonious data collection, and limited electronic health records pose significant challenges for comprehensive datasets crucial for training AI models.
- Digital Divide – With only 55 per cent of India having internet access, AI healthcare solutions will primarily benefit only the urban populations and result in the widening of the existing healthcare gap
- Regulation – DISHA (Digital Information Security in Healthcare Act) is yet to be implemented, leading to a regulatory vacuum that inhibits innovation and adoption of AI
- Many healthcare workers, technicians, and clinicians lack the technical knowledge to integrate AI tools into their work processes.
While the current AI adoption among Indian clinicians is relatively low at around 12 per cent, projections indicate this could rise to 79 per cent within the next two to three years. What factors are expected to drive this rapid growth, and how can AI be positioned as an indispensable tool in clinical practice?
Mehta
1. Government and Policy Support:
● Increased funding for digital health projects and AI initiatives, along with government-backed incentives, can foster innovation.
● Clear guidelines on data privacy, security, and AI ethics will reduce legal uncertainties and encourage adoption.
● Government initiatives like the National Digital Health Mission (NDHM) will promote data standards, interoperability, and digital infrastructure to facilitate AI implementation.
2. Improved Data and Accessibility Infrastructure:
● The government’s push for Electronic Health Records (EHR) will enable large-scale data collection, essential for AI algorithms to function effectively.
● Expanding internet access in rural and urban areas will allow more healthcare facilities to deploy AI tools requiring real-time data processing.
3. Affordable and Accessible AI Solutions:
● An ecosystem encouraging tech companies to develop affordable AI tools—like low-cost diagnostics or language-agnostic interfaces—will ensure AI solutions cater to India’s diverse healthcare landscape.
4. Awareness and Skill Development:
● Collaboration between healthcare providers and tech companies will provide hands-on AI experience, building competence among clinicians.
● Training programmes focused on AI's practical applications will encourage healthcare professionals to explore new technologies confidently.
5. Proven Value and Clinical Outcomes:
● Highlighting successful AI use cases, such as in diagnostics, will build credibility and trust among clinicians and patients.
● Using AI for routine administrative tasks like patient records management can demonstrate practical outcomes and foster acceptance.
Singh: Though current AI adoption among Indian clinicians is low, at around 12 per cent, it is projected to rise to nearly 79 per cent within two to three years. This rapid growth will be driven by affordable AI solutions, supportive policies, and increased awareness of AI’s potential to improve care. By positioning AI as an enabler rather than a replacement in clinical practice, we can help clinicians see its value in enhancing diagnostic accuracy, reducing administrative burdens, and strengthening patient engagement.
Mahajan: Some established best practices that will catalyse rapid AI adoption include –
- Government initiatives like the NDHM and Ayushman Bharat will improve digital infrastructure and standardise health data collection.
- Awareness about AI solutions' availability and potential benefits will drive adoption.
- AI implementation will increase with the reduction in cost of implementation.
- Increased collaborations between the various stakeholders – industry, academic institutions, and tech companies will drive AI adoption.
India has quickly adapted to changing global best practices, and it is just a matter of time before we shun our tardiness and move forward faster than most developed countries in the world for AI adoption and implementation.
With AI adoption projected to grow substantially, what areas in healthcare will experience the most transformative impact, and why?
Mehta: AI integration will revolutionise several critical areas, enhancing both efficiency and patient outcomes:
● Medical Imaging and Diagnostics: AI-powered systems can accurately detect anomalies, improving early disease detection and minimising human error.
● Remote Monitoring: AI-enabled tools can continuously monitor patient health, promoting early interventions and reducing the need for hospital visits.
● Predictive Analytics and Risk Assessment: AI can forecast disease outbreaks and predict hospital readmissions, enabling preventive care and reducing healthcare costs.
● Administrative Workflow Optimization: Automating tasks like scheduling and billing with AI will allow healthcare professionals to focus more on patient care.
● Mental Health and Remote Care: AI tools can expand access to mental health services, especially in underserved regions, by providing remote care and support.
Singh: AI’s most transformative impacts will likely be in diagnostic imaging, early disease detection, and predictive analytics for better patient outcomes. Additionally, AI-powered telemedicine can extend quality healthcare to underserved areas, helping to bridge gaps in access and care.
What are the primary challenges facing AI adoption among clinicians in India, and what strategies could be effective in addressing these challenges to ensure safe and beneficial integration?
Mehta: AI adoption among clinicians in India faces several challenges, but targeted strategies can help overcome these hurdles:
1. Lack of Training and Expertise:
● Many clinicians lack the skills to interpret AI recommendations, which leads to hesitation in adopting AI solutions.
● Introducing AI-focussed education in medical and nursing schools and offering tailored training programmes will build competence and confidence.
2. Concerns Over Diagnostic Accuracy and Reliability:
● Clinicians are cautious about relying on AI due to concerns about errors and the potential risks of over-dependence on technology.
● Demonstrating AI’s proven effectiveness through real-world case studies will help alleviate scepticism. Emphasising that AI complements clinical judgment rather than replacing it will further build trust.
● Regular third-party validation of AI tools and transparent performance reporting will enhance credibility.
3. Certification and Standards:
● Establishing a certification process for healthcare AI solutions, similar to medical device approvals, will ensure their safety and accuracy. Certified tools will foster trust among clinicians.
Singh: The challenges to widespread AI adoption, however, remain significant, especially in terms of clinician familiarity with AI, data privacy concerns, and regulatory clarity. Effective strategies include targeted training programmes for clinicians, robust data privacy policies to ensure patient trust, and collaborative efforts among healthcare providers, policymakers, and technology firms.
Role of AI in improving patient outcomes and optimising clinical workflows
Mehta: AI offers transformative possibilities in both patient care and administrative operations:
● Proactive Care: AI algorithms can analyse patient data to predict health risks, enabling early interventions that improve outcomes.
● Personalised Treatment Plans: By evaluating genetics, lifestyle, and treatment history, AI can create tailored treatment strategies for more effective care.
● Real-Time Monitoring: AI-powered wearable devices can track patients’ vitals remotely, reducing the need for hospital readmissions and enabling timely interventions.
● Administrative Optimisation: Natural language processing (NLP) tools can transcribe clinical notes and summarize lab results, minimizing administrative workloads for clinicians.
● Resource Management: AI tools can predict patient no-shows and help optimise staff schedules, bed availability, and inventory, ensuring smooth operations.
● Enhanced Patient Engagement: AI chatbots can handle patient queries, send appointment reminders, and provide pre- and post-operative guidance, improving patient satisfaction.
Singh: Ultimately, AI’s potential to improve patient outcomes and streamline clinical workflows is profound. By aiding decision-making and automating routine tasks, AI enables clinicians to dedicate more time to patient care, reinforcing its role as an essential tool in advancing healthcare efficiency and quality.
Conclusion: The integration of AI into clinical practice in India is not merely a technological evolution but a transformative journey toward improving healthcare accessibility, efficiency, and outcomes. By addressing challenges such as infrastructure gaps, regulatory clarity, and skill development, India is poised to leverage AI's full potential. Strengthening collaboration among policymakers, healthcare providers and tech innovators will play a crucial role in this journey. With strategic investments and a commitment to innovation, AI could soon become the backbone of India's healthcare system, ensuring better care for all.