Quantum Health AI: The Revolution of Medicine, Public Health, and Global Health is the first comprehensive book defining the transformation of the global health ecosystem by the fusion of our most powerful technologies—quantum computing and artificial intelligence—while defending an actionable human-centred approach to doing so responsibly, equitably, and sustainably.
We can continue to watch wars, diseases, poverty, polarization, cyber-crime, and climate change only worsen. Our strongest technologies can remain centralized in a small number of companies and countries for their profit and power. Or we can cooperatively put quantum AI to work for the health of all of us, by managing the technology's overarching competition between democracies and autocracies, along with the public and private sectors (balancing human security with national security, economic growth with household livelihoods, and individual rights with the common good). This book draws on the decade plus of original research and first-hand perspective of the world’s first triple doctorate-trained physician-data scientist and AI ethicist.
It unpacks the history, science, values, and political economics framing and driving quantum AI (including its physics, metaphysics, ethics, governance, computing, sensing, communication, materials, and security), the global health ecosystem (healthcare systems, public health agencies, biotechnology companies, and development institutions), and their growing integration, wins, and challenges. This one-stop book provides a global, inclusive, and practical guide for understanding and shaping these societal and technological trends. It thus empowers health, technology, and policy students, practitioners, professionals, researchers, and leaders in organizations, universities, companies, and governments—ultimately to make and maintain the human-centred quantum AI that can safeguard and advance humanity's common health, home, and future.
Quantum-powered AI will either be the greatest technological boost—or threat—to medicine and public health, as they apply our most disruptive hardware and software to humanity’s most critical strategic asset: our health. Health workers, policymakers, and leaders are therefore indispensable for advocating for our patients by helping steer the responsible design and deployment of these technologies. Healthcare is far too expensive and inaccessible for most people globally, while fielding too few health workers. But quantum AI may allow us to scale efficient and effective innovations to reverse these trends.
We cannot ensure they will be (since a small group of countries and companies are advancing these technologies faster than any regulation or legislation). But what we can do is be the force multipliers for equitable value-based care, by working with policymakers and institutions crafting and enforcing global AI governance for health, and so the guardrails for this global tech race maximizing mutual benefit and minimizing societal harms. We can also co-design the local governance frameworks synced with our existing clinical workflows and research portfolios that already are showing promise and widescale adoption (i.e., the TRAIN consortium and the World Health Organization’s responsible health AI).
Instead of just another list of principles we put up on walls, this approach is about rediscovering the essential principle: each patient loved as a unique person in community. When we recover our shared understanding across cultures, belief systems, and health systems that we belong to each other, and that the individual good and common good cannot be achieved without each other, then we can re-earn trust in each other through productive cooperation toward the full flourishing—aka integral wellbeing or health—of all. Like in human-centered economics, household living standards, not just GDP, are needed. So too in quantum health AI, human security and data security are both critical.
Political economics are the meta-determinants of health, shaping and constraining to a large extent the biological, social, commercial, and digital determinants of health. And the leading powers globally have both defined quantum and AI as critical national security assets that will determine our future. Health workers and leaders with our technology partners can therefore reinforce positive pressure on them to collectively manage this strategic competition so patients worldwide benefit from the resultant innovation (i.e. ChatGPT and DeepSeek making each other better), while minimizing its weaponization and conflict. Cooperate where we can, compete where we must.
Quantum simplifies and complicates the ethical challenges AI poses for healthcare. Quantum simplifies them by limiting the number of powerful actors driving this computing revolution forward (currently among a handful primarily of American and Chinese companies), so it facilitates the traditional institutional checks on their competition. But it also complicates the challenges by enabling decentralized breakthroughs that can fundamentally and unpredictably redefine the global digital ecosystem on which health innovations depend (such as by quantum increasingly threatening standard digital encryption on which our health data exchanges depend).
Human security ethically and pragmatically reinforces guardrails in the strategic competition for national security, in which there is an intensifying global technology race to dominate quantum AI. Consider how a healthcare system may want to expand its reach and profit margin through greater organizational efficiencies (but this also invites the financial risks of over-expansion and increased regulatory scrutiny). Similarly, it is in the interests of all governments to foster collaborative innovation according to a globally shared minimum set of fair rules that benefits all of humanity (while minimizing risks from rogue or irresponsible actors).
Quantum AI for global health is an inescapably inter-disciplinary enterprise. And the winners are those who leverage more comprehensive insights to drive faster innovation, not just technically with more efficient performance, but also institutionally with greater public trust and so use. Science, economics, and ethics must work together. More free, open, and collaborative ecosystems outcompete their more controlled, closed, and divisive counterparts in the quantum health AI R&D, governance, and thus market share.
The demographic decline (and even collapse) in many high-income countries means the producers and consumers of quantum AI must increasingly come from—or at least partner with—low- and middle-income countries. China and the United States are currently competing not just technically for quantum AI but also politically for preferential partnerships with these emergent powers. Whoever’s companies deliver greater value (higher quality for lower cost), defined as more transparently and equitably safe, efficient, and effective, ultimately wins.
Like washing hands as one of our most powerful anti-infection tools in hospitals, many of the most promising quantum AI advances are simple but fill key gaps. Leading use cases include digital platforms (more quickly and affordably expanding health systems’ AI and computing capacities especially in lower resource communities), federated data architectures (accelerating precision medicine and precision public health), swarm intelligence, AI agents, cloud and edge computing, quantum safe cryptography, digital twins, in-silico (digital) trials, translational meta-multi-omics, micro-nanorobots, and clean or green AI.
Fair and transparent ‘co-design’ is gaining traction in responsible AI creation and deployment, especially through public-private partnerships such as with large technology companies working with trusted non-government organizations and local health systems to ensure patients and providers can inform every step of the quantum AI cycle. Then health wins beget new wins and can strengthen trust, deepen collaboration, and scale applications through more of the health ecosystem.
Many health organizations worldwide are making the right changes (because they have no other choice if they want to stay open). The World Health Organization and others are doing important work on top-down governance. Meanwhile, bottom-up governance is happening much more rapidly and probably more influentially as health systems are having to show greater transparency, responsiveness, and efficiency delivering on local health demands (especially as more disruptive technology companies such as through smartphone apps can leapfrog brick-and-motor traditional health players if they fall too far behind delivering patient value).
Federated data architectures like with Mayo Clinic’s Platform and TriNetX in America and the Ayushman Bharat Digital Mission in India provide powerful examples of advancing innovation without jeopardizing security. Swarm learning and globally scalable quantum safe cryptography, such as with IBM’s z16 quantum-safe mainframe with on-chip AI, in addition to Nvidia’s health platforms, also show what ‘Data-Behind-Glass’ models can do in this area trying to keep data as secure and local as possible, while sharing quantum AI-generated insights across network partners.
The need for physicians has only increased as medicine becomes more technologically advanced: better science means more data in which patients can get more easily lost. Healthcare systems upskilling physicians with responsible AI will replace systems and physicians without such AI. Our role as healers stays constant, but our toolbox expands with this technology to better serve patients (especially with the accelerating trend of quantum AI democratization as with digital platforms and AI embedded in clinical workflows).
Go hold the hand of your closest patient and listen to their story. They will help us prioritize which quantum AI applications, capacities, and infrastructures are the greatest value-add for them, while keeping us rooted in why we are doing all this in the first place. And weekly meet one new person outside your discipline and background. They help us translate old insights into new breakthroughs in our niche, while improving the scale, scope, speed, and sustainability of a shared actionable strategic plan toward a healthier, fairer, more human future.
Dominique J. Monlezun, MD, PhD, PhD, MPH, is the world’s first known triple-doctorate-trained physician-data scientist and AI ethicist. He serves as a physician at the Mayo Clinic, a Professor of Cardiology at two American academic medical institutions, and a Professor of Bioethics at two United Nations-affiliated universities. He is also the Principal Investigator, Senior Data Scientist, and Biostatistician for over 100 research studies in collaboration with Harvard University, the National Institutes of Health, and institutions across dozens of low- and middle-income countries.
