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From remote monitoring and early disease detection to tools that help frontline health workers make better decisions, startups are developing solutions to address gaps in healthcare delivery. The bigger challenge, however, lies in integrating these technologies into India’s public health system and making them accessible. At TechSparks 2026, Dr Geetha Manjunath, Founder and CEO, Niramai Health Analytix; Debraj Banerjee, Global technology investor and General Partner, Fundamentum Frontier Advisors; and Shekar Sivasubramanian, Head, Wadhwani AI; discussed on healthcare innovation and AI, and why promising innovations often struggle to achieve widespread adoption. According to the panel, startups face three interconnected challenges: rigorous clinical validation, integration with existing public health infrastructure, and the patience capital required to scale solutions across India’s diverse healthcare ecosystem. “There are no gaps in the system,” said Shekhar Sivasubramanian, Head of Wadhwani AI, arguing that innovators must learn to design solutions around real-world constraints rather than treat them as obstacles. Internet connectivity may be inconsistent, data may be incomplete, and solutions may need to work across languages and cultural contexts. These are realities that developers must account for, he said. “If you cannot solve a problem with these realities, then you shouldn’t be in this business,” he added. Clinical validation remains a critical hurdle Geetha Manjunath said one of the biggest barriers to adoption is the tendency to prioritise rapid product development over clinical validation. AI tools can now be built in days using readily available datasets and development platforms. But a model that performs well in a laboratory does not necessarily deliver reliable results when deployed in real-world healthcare settings. “The question is not whether you build your model. The real problem is to see if your algorithm actually works on real people,” she said. Retrospective studies, which use existing hospital data with known outcomes, can provide an initial assessment. Prospective studies go further by testing a technology in a live clinical setting without allowing its predictions to influence treatment decisions. The results are subsequently compared with established clinical findings. However, she added that even these stages may not be enough for public health deployment. A technology tested in a hospital must also be evaluated in the settings where it is intended to be used, including rural areas where diagnostic infrastructure may be limited. The cost of implementation, feasibility of deployment and clinical performance must all be considered before a technology can be recommended for wider use. “Healthcare requires more patience,” she said, adding that founders need resilience and a long-term commitment. Integrating with public health systems Beyond clinical validation, integrating new technologies into existing government systems remains a significant challenge. Sivasubramanian of Wadhwani AI said companies looking to work in public health must understand the systems used by both central and state governments. His organisation has teams embedded in state health offices across 17 states, with teams of around five to seven people working within these offices. The approach is necessary because implementing a technology requires more than developing a functional product. Teams must train users, understand local requirements and adapt their solutions to existing workflows. He added that each application requires integration with frontline healthcare infrastructure. “India has around 150,000 primary health centres, and innovators must be prepared to work at that scale. Without patience, perseverance and sufficient financial resources, a company may reach a halfway point, become disappointed and walk away. Deep commitment is essential.” He added that government officials implement these solutions, while innovators act as facilitators. “Technology companies must work with them rather than assume they can independently deliver change.” Making AI affordable and accessible For healthcare technologies to reach underserved populations, affordability and accessibility must be built into their design. Geetha Manjunath cited Niramai’s breast cancer screening technology as an example. The company uses thermal imaging and AI to assess breast cancer risk through a non-invasive screening process. The company aims to bring the cost of screening below Rs 50 at scale. The technology is also designed to address barriers that discourage women from seeking screening, including discomfort with procedures involving physical contact or breast compression. Manjunath identified affordability, accessibility, acceptability, and accuracy among the key requirements for successful AI adoption in public health. Timely results and clear accountability are also important. Scaling still remains difficult For investors, the challenge extends beyond determining whether a technology works. They must assess whether the business can navigate regulatory approvals, financing requirements and the complexities of reaching customers. Banerjee, a general partner at Fundamental Frontier Advisors, said deep-tech companies need an ecosystem that supports the journey from product development to commercialisation. “There is no single formula for scaling healthcare innovation.” He added, “When evaluating AI products, we look at the underlying principles and whether several of them apply to the product. It should be appropriately segmented, nested within the relevant system and hyperlocalised. Those are some of the factors that can help us assess whether an investment is likely to work.” “Venture capital represents only one part of the entire ecosystem,” he said, emphasising that the wider network of healthcare providers, regulators, investors and commercial partners must also develop. The broader challenge, he said, is not simply creating better technology but building the systems that allow it to reach users. From India to global markets For Niramai, the opportunity extends beyond India as the company has screened around seven lakh women and is working towards expanding access to its technology. “Our goal is to reach every woman,” Geetha Manjunath said, acknowledging that achieving this would take sustained effort over many years. The company has also expanded into international markets. The same technology can also be relevant in developed markets, where healthcare systems are seeking ways to manage costs. Niramai has six centres in the US using its solution and that the company received US FDA approval for use alongside existing screening methods. However, expansion requires a different approach in each market, she added. Regulatory requirements, reimbursement structures and pricing models vary, and a solution cannot necessarily be deployed in the same way everywhere. For Indian healthcare innovators, she said, building for local needs should not prevent them from pursuing international opportunities.
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