Artificial Intelligence (AI) is moving deeper into Indian healthcare, reshaping how doctors diagnose, hospitals operate and patients receive care. A new KPMG–FICCI report says this shift marks a turning point, with AI pushing the sector away from reactive treatment and towards proactive, data-driven and preventive care.
The report, titled ‘AI in Healthcare: Reimagine Care with AI-driven Transformation’, was released at FICCI HEAL 2025. It highlights that while interest in AI is high, adoption is still at an early stage and requires careful planning, strong governance, and a clear purpose.
The report also states that AI could be a major driver of India’s economic growth, with NITI Aayog projecting it may triple the country’s GDP by 2035.
Key considerations for AI in healthcare
1. AI in self-care, health management
Self-care is emerging as a core element of improved health outcomes through fitness planning, screenings, disease management and rehabilitation. Access to credible guidance remains uneven between urban and rural regions.
AI tools help close this gap with personalised insights through wearables and apps that track vitals, sleep and activity. Chatbots offer mental health guidance, nutrition apps customise meal plans, and predictive analytics alert users to risks before symptoms arise. Automated reminders for medicines and lifestyle changes further support preventive, patient-led care.
2. AI in patient onboarding and engagement
Onboarding through phone, portals or chatbots often faces delays, language barriers and fragmented systems. Many patients remain dissatisfied with poorly integrated digital platforms.
AI streamlines this experience by automating appointments, triage and communication. Multilingual interfaces and predictive analytics enable more personalised and responsive interactions, reducing administrative loads and improving satisfaction.
3. AI in clinical screening and diagnosis
Fragmented data and limited specialist access often delay diagnosis. AI analyses clinical records, lab results, scans and histories using image recognition, predictive models and natural language processing.
This leads to faster, more precise diagnostics, reduced cognitive load for clinicians and better integration with electronic health records. Learning from real-world data improves accuracy and supports proactive care.
4. AI in hospital operations
AI enhances hospital efficiency through predictive analytics for resource planning, onboarding and diagnostics. It automates documentation, supports decision-making, and assists in discharge planning, robotic surgeries, inventory, claims management and even marketing optimisation.
5. AI in public health
AI strengthens public health systems via predictive epidemiology, real-time monitoring and climate-sensitive disease forecasting. These tools help healthcare systems make data-driven decisions and improve preparedness and operational efficiency.
6. Technology landscape
While AI’s potential in healthcare is enormous, adoption in India remains early-stage. Current applications include generative AI, speech recognition, agentic AI, machine learning and robotic process automation (RPA) integrated to streamline processes and documentation.
Lalit Mistry, partner and co-head, Healthcare, KPMG in India, said: “This paper explores the transformative role of AI in connecting the dots between vast untapped data, disconnected systems into a unified, intelligent network that delivers personalised and effective care. Providers across the public and private sector can unlock huge value and efficiency by adopting AI-driven transformation to deliver better care and outcomes.”
Key considerations for AI in healthcare
The report states that AI adoption must begin with a clear purpose, rather than following trends. Healthcare organisations are advised to start with specific use cases before expanding to enterprise-wide strategies.
Stakeholder alignment, across clinicians, administrators, patients and technology partners, is essential. Other critical factors include:
• Assessing solution maturity and clinical accuracy
• Obtaining regulatory approvals
• Ensuring ethical use, data quality, workflow integration and cybersecurity
• Monitoring ongoing performance and safeguarding privacy
• Evaluating vendors carefully and establishing pilots as “proof-of-value”
The way forward
The report suggests that India’s next phase of AI adoption should focus on integrated, system-wide transformation rather than isolated pilots. Healthcare leaders must:
• Develop enterprise AI strategies
• Strengthen data privacy and ethics
• Invest in workforce readiness
• Build interoperable systems
• Prioritise initiatives with measurable clinical and operational impact
