India’s healthcare, infrastructure, is undergoing a remarkable digital transformation, driven by emerging technologies like artificialintelligence (AI), machinelearning, and bigdata. These advancements are not only enhancing accessibility and efficiency but also setting the foundation for a more equitable health system. According to the WorldEconomicForum (WEF), AI is poised to account for 30% of new drug discoveries by 2025 — a major leap for the pharmaceutical industry.
As outlined in the Global Outlook and Forecast 2025–2030, the market for AI in drugdiscovery is projected to grow from $1.72 billion in 2024 to $8.53 billion by 2030, clocking a CAGR of 30.59%. Major tech players like IBMWatson, NVIDIA, and GoogleDeepMind are partnering with pharmaceutical firms to fast-track AI-led breakthroughs.
Beyond R&D, AI is transforming clinical workflows by digitising patientrecords and decentralising models to improve diagnostic precision while protecting privacy.
During an interview with AnalyticsIndiaMagazine (AIM), Rajan Kashyap, Assistant Professor at the National Institute of Mental Health and Neuro Sciences (NIMHANS), shared insights into the government’s push toward innovation: “Increasing the number of seats in medical and paramedical courses, implementing mandatory rural health services, and developing Indigenous low-cost MRI machines are contributing significantly to hardware development in the AI innovation cycle.”
Tech-Driven Healthcare Innovation
Kashyap pointed to major initiatives like the GenomeIndia project, cVEDA, and the AyushmanBharatDigitalMission as critical steps toward advancing India’s clinical research capabilities. He added that initiatives in genomics, AI, and ML are already improving clinicaloutcomes and streamlining operations.
He also spotlighted BrainSightAI, a Bengaluru-based startup that raised $5 million in a Pre-Series A round to scale its diagnostic tools for neurological conditions. The company aims to expand across Tier 1 and 2 cities and pursue FDA certification to access global healthcaremarkets.
Another innovator, Niramai Health Analytics, offers an AI-based breast cancer screening solution. Their product, Thermalytix, is a portable, radiationfree, and cost-effective screening device that is compatible with all age groups and breast densities.
Meanwhile, biopharma giant Biocon is leveraging AI in biosimilar development. Their work in predictivemodelling is reducing formulation failures and expediting regulatory approvals. One of their standout contributions is Semglee, the world’s first interchangeablebiosimilar insulin, now made accessible through their tie-up with ErisLifesciences.
Rising R&D costs have pushed pharma companies to adopt AI solutions for innovation and costefficiency.
Data Security Still a Grey Zone
While innovation is flourishing, there are pressing concerns around dataprivacy. A report by Netskope Threat Labs highlighted that doctors are increasingly uploading sensitive patient information to unregulated platforms like ChatGPT and Gemini.
Kashyap expressed serious concerns about lax data practices:
“During my professional experience at AI labs abroad, I observed that organisations enforced strict data protection regulations and mandatory training programs…The use of public AI tools like ChatGPT or Gemini was strictly prohibited, with no exceptions or shortcuts allowed.”
He added that anonymised data is still vulnerable to hacking or reidentification. Studies show that even brainscans like MRIs could potentially reveal personal or financial information.
“I strongly advocate for strict adherence to protected data-sharing protocols when handling clinical information. In today’s landscape of data warfare, where numerous companies face legal action for breaching data privacy norms, protecting health data is no less critical than protecting national security,” he warned.
Policy Direction and Regulatory Needs
The Netskope report recommends implementing approved GenAI tools in healthcare to reduce “shadow AI” usage and enhance security. It also urges deploying datalossprevention (DLP) policies to regulate what kind of data can be shared on generative AI platforms.
Although the usage of personal GenAI tools has declined — from 87% to 71% in one year — risks remain.
Kashyap commented on the pace of India’s regulatory approach:
“India is still in the process of formulating a comprehensive data protection framework. While the pace may seem slow, India’s approach has traditionally been organic, carefully evolving with consideration for its unique context.”
He also pushed for developing interdisciplinary medtech programs that integrate AIeducation into medicaltraining.
“Misinformation and fake news pose a significant threat to progress. In a recent R&D project I was involved in, public participation was disrupted due to the spread of misleading information. It’s crucial that legal mechanisms are in place to counteract such disruptions, ensuring that innovation is not undermined by false narratives,” he concluded.
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