Technology alone won't solve AI health care's biggest challenges

AI is quietly transforming the way we think about medicine. Chatbots now help triage patients online, wearable devices monitor vital signs in real time and machine learning tools assist radiologists in spotting early signs of disease. These are not futuristic concepts – they are already being tested and used in hospitals around the world. Insights10 – a healthcare-focused market research firm – has projected the UAE’s AI healthcare market to grow from $40 million in 2022 to $720 million by 2030 – an annual growth rate of more than 46 per cent.

But as AI becomes more powerful and pervasive, one truth remains clear: its real-world impact depends not just on the quality of the technology, but on how well people work together to develop and use it.

That is a lesson I’ve learnt first-hand, both as an AI researcher and through my years of involvement with the NYU Abu Dhabi International Hackathon for Social Good. I joined the hackathon as a student, part of the winning team that built a mobile app to help prevent heat stroke among outdoor workers. Since then, I’ve returned as a mentor and judge, and seen how transformative collaborative spaces can be for young technologists. These events are about more than building demos – they simulate the messy, exciting process of turning ideas into solutions that matter.

In my research, I focus on how AI can support clinical decision-making – from predicting stroke risk to generating diagnostic reports from chest X-rays, and improving IVF outcomes using medical image analysis. These projects may sound technical, but they are deeply human. They require working closely with doctors and patients to ensure that the tools we build address real clinical needs and can be trusted in practice. An algorithm is only as useful as its ability to integrate into workflows, support professionals and ultimately improve care.

This is where collaboration comes in. Too often, AI in health care is treated as a purely technical challenge. However, designing tools that clinicians will actually use means involving a range of voices – from healthcare workers and policymakers to ethicists and patients. Engineers need to understand the realities of hospital life, and data scientists must engage with concerns around bias, consent and fairness. These conversations are not optional – they are essential to making sure technology works for everyone.

Spaces like the NYUAD Hackathon help simulate this kind of interdisciplinary teamwork. Students from across the Arab world join forces with mentors from academia, industry and government to tackle complex social issues. The best projects do not emerge from technical genius alone but from diverse teams that combine coding skills with empathy, curiosity and a strong sense of purpose. Over the years, I have seen students prototype tools for early childhood health monitoring, pandemic preparedness and mental health support for displaced communities. These ideas succeed not because they are flashy but because they are grounded in context and built with care.

But even with strong teams and good ideas, another challenge looms large: data. AI systems need large, diverse and well-curated datasets – especially in health care, where accuracy is critical. Yet many countries, including those in the Arab world, are still developing the infrastructure and policies needed to support responsible data sharing while protecting privacy and ensuring equitable access. This is an area where regional collaboration and investment can make a major difference.

Looking ahead, emerging technologies such as quantum computing may help tackle some of health care AI’s biggest challenges. Quantum methods could enable faster, more powerful models and new ways of understanding complex datasets. However, technology alone will not solve anything. Whether it is AI or quantum, we need to prepare students to ask better questions, think across disciplines and stay focused on real-world impact.

Such preparation should not begin in university – it should start much earlier. That is why I also dedicate time to teaching AI to K-12 students. It is crucial to introduce these ideas from a young age, and to do so through a holistic lens. Students should learn not just how AI works, but why it matters, who it affects and how to use it responsibly.

This early exposure often feeds into programmes like the hackathon, where former students return as confident contributors and mentors. Eventually, it crosses into the real world – influencing how future doctors, engineers and researchers tackle some of the biggest challenges in health and medicine.

As medicine and technology become ever more entwined, collaboration will only grow in importance. AI has the potential to improve care, reduce costs and save lives – not just by treating illness, but by promoting health. Expanding our focus from lifespan to healthspan will depend on personalised treatment, continuous monitoring and proactive care – all driven by data.

Making that future a reality will require more than breakthroughs. It will require us to build – together – with intention, humility and deep respect for the people at the heart of health care.