For AI to reach its full potential in health systems, countries need to “future-proof” their infrastructure, build enabling regulatory environments, and foster open partnerships with both public and private actors.
Throughout your career leading diverse teams across global health initiatives. Which core leadership principles have remained your guiding compass?
My leadership approach has always been anchored in openness, accessibility, and empathy. I believe in walking alongside my team rather than directing from a distance. An “open-door” policy, where conversations are welcome and hierarchies don’t get in the way—has been central to building trust.
When mistakes happen, my instinct is not to reprimand but to use them as opportunities for growth. I stand by my teams in challenging moments, helping them learn, adapt, and find better solutions. This collaborative, participatory style has not only strengthened morale but also fostered a culture of shared ownership and accountability.
An “open-door” policy, where conversations are welcome and hierarchies don’t get in the way, has been central to building trust.
From your work with WHO, PATH, and MSF, what lessons have you learned about aligning technology-driven health projects with global health policies?
Over the years, I have had the privilege of working at the intersection of technology and health policy, starting from the early DOS-based Nikshay systems in the 2000s to today’s real-time, case-based platforms. Being part of the development, piloting, and scaling of technology in TB control has offered valuable lessons.
First, there is often a disconnect between technology developers and on-the-ground realities. Many tech teams are unaware of infrastructure limitations, human resource readiness, and adoption barriers at the field level. This results in solutions that may be technologically sound but misaligned with the operational context, leading to low adoption.
Second, behavioural transition is often underestimated. Moving from familiar processes to new, technology-enabled workflows requires sustained engagement, training, and trust-building. In some cases, half-baked solutions are handed over to program teams without adequate support, leading to resistance and eventual rejection.
Third, the policy change cycle can be long and complex. When we started working on AI for chest X-ray interpretation in TB detection, it took a decade, 2010 to 2020, before WHO recommendations were updated to reflect this innovation. Policy-shaping expert groups are often removed from the technology development process, which can lead to scepticism and high evidence thresholds. One key learning is that we must shorten the journey from proof-of-concept to policy uptake without compromising scientific rigor.
Lastly, investing in adoption strategies is as important as investing in product development.
Without structured plans for change management, even the best technologies fail to achieve meaningful scale.
What are the key challenges in integrating AI-enabled solutions into existing healthcare delivery systems?
Integration happens at multiple levels, and each has its own challenges:
- Awareness and understanding: Many decision-makers and frontline providers are unfamiliar with AI concepts, leading to skepticism or unrealistic expectations.
- Regulatory frameworks: In many countries, there is no established regulatory pathway for validating or approving AI tools for healthcare. This creates uncertainty for implementers and slows adoption.
- Infrastructure gaps: Computing capacity, stable internet connectivity, secure in-country data storage, and integration with existing health information systems are often lacking.
- Policy-practice mismatch: Regulatory ecosystems can be rigid and out of step with current infrastructure realities, causing projects to remain in pilot mode rather than scaling nationally.
For AI to reach its full potential in health systems, countries need to “future-proof” their infrastructure, build enabling regulatory environments, and foster open partnerships with both public and private actors.
What strategies do you employ to ensure that innovative health interventions achieve both scalability and long-term sustainability in resource-constrained settings?
The foundation for scalability and sustainability is national ownership. Countries need digital and AI roadmaps that are integrated into their broader Digital Public Infrastructure (DPI) strategies. AI enablement should not be an isolated project, it must be a part of the health system’s long-term investment plan.
Key strategies include:
- Embedding in national strategies: Position AI-enabled solutions within country health and technology roadmaps from the outset.
- Public-private partnerships: Leverage the agility of the private sector with the reach and legitimacy of the public sector.
- Research collaborations: Engage local and global academic partners for continuous evidence generation.
- Co-creation: Develop solutions with local institutions and communities to ensure cultural and operational fit.
- Capacity building: Strengthen local capabilities through targeted training, certification programs, and experiential learning, ensuring that skills remain even after project completion.
What role does private sector engagement play in achieving large-scale public health goals?
The private sector brings risk appetite, innovation speed, and operational efficiency, all of which are critical for scaling health solutions. Partnerships between governments and private innovators, such as India’s AI Mission demonstrate how joint efforts can accelerate digital transformation in healthcare.
Private sector actors can also mobilize resources faster, respond more flexibly to challenges, and leverage global networks for technology transfer. When aligned with public health priorities, such collaborations can bridge the gap between innovation and impact.
With AI’s rapid advancement, what emerging technologies do you believe will most significantly shape the future of global health?
Several technologies stand out:
- Large Language Models (LLMs): These can power decision-support tools, automate documentation, and improve patient communication—especially in multilingual, resource-limited contexts.
- Multi-modal AI: Integrating data from imaging, clinical records, and biosensors for more accurate diagnostics and risk stratification.
- Edge AI: Enabling real-time AI processing on portable devices without reliance on cloud connectivity—critical for remote settings.
- AI-enabled Point-of-Care Ultrasound (POCUS): Expanding access to imaging diagnostics at the primary care level, particularly for TB, pneumonia, and maternal health.
The future of global health will depend not just on the technologies themselves, but on our ability to integrate them into health systems responsibly, equitably, and sustainably.
About Dr. Shibu Vijayan:
Dr. Shibu Vijayan is the Chief Medical Officer – Global Health at Qure.ai, where he leads the strategic integration of AI-driven diagnostics into health systems worldwide. With over two decades of experience spanning WHO, PATH, and Médecins Sans Frontières, he has worked across more than 30 countries, shaping policy, advancing TB and lung health programs, and scaling innovative health technologies.
A medical doctor, TB survivor, and author of 30+ scientific publications, Dr. Vijayan specializes in aligning cutting-edge innovations with public health priorities, ensuring they are scalable, sustainable, and impactful, particularly in resource-constrained settings. His leadership has been instrumental in advancing AI adoption for TB screening, integrated lung health, and point-of-care imaging, influencing both global policy and country-level implementation.
