Why AI in Health Demands a Different Kind of Design
Stay updated with us
Sign up for our newsletter
Every innovation in digital health begins with the same question: can we trust it with human life?
And as the world races to leverage the power of artificial intelligence, that question defines the entire design challenge. It asks engineers, data scientists, and healthcare leaders to hold technology to the same evidentiary standards that medicine demands of every tool in its system, where precision, validation, and explainability are ethical requirements.
Catch more IT Insights: Why Quantum-Resistant Identity Security Must Be a CISO Priority in 2025
We talk often about AI’s potential to predict risk, guide behavior, and transform access. Those ambitions are achievable, but only through the thoughtful use of high-quality data and the inclusion of deep domain expertise at every stage of development. Health requires a slower and more deliberate pace than technology typically allows, because lives are affected by every design decision. We have already seen some of the risks of unfettered use AI in the health sphere turning into negative headlines in our news feeds.
Across our work in sleep science and digital health, I have seen the difference between experimentation and adoption. The algorithms that truly change care are built on years of disciplined data collection, clinical validation, and real-world testing. Our database of more than 850 million hours of sleep, studied in 250+ scientific studies and over 90 scientific papers & journals, shows what can happen when rigor comes first. The data are meaningful because they have been verified, benchmarked, and refined in the context of patient or consumer outcomes, and not simply model performance.
This approach has begun to influence how payers and regulators view AI. European insurers now reimburse personalized smartphone-based sleep screening and coaching, a milestone that shows adoption is possible when evidence is strong enough to withstand scrutiny. It also demonstrates that high-quality data can scale when it is made accessible through the devices people already use.
The smartphone may turn out to be the most powerful tool in modern healthcare. It brings reach, continuity, and immediacy to populations that traditional medical systems struggle to serve. With careful model design and validation, a smartphone can deliver insights that scale in incredible ways whereas they were once confined to hospital equipment or specialized clinics. It is an opportunity to extend preventive care without increasing cost or complexity.
Catch more IT Insights: Human-AI Collaboration in Decision Intelligence: What’s Next for C-Suite Leaders
To realize this potential responsibly, the industry must commit to transparency, traceability, and explainability in every model deployed. Deterministic systems that consistently show why they reach a conclusion are essential for trust. They allow clinicians to interpret results confidently, users to understand the implications of their data, and regulators to verify safety and efficacy.
AI in health has now become a question of design philosophy. Technology already exists to model disease risk, personalize interventions, and improve outcomes. The challenge lies in ensuring these systems are built to preserve accuracy and privacy and prevent data bias.
When data are validated, methods are explainable, and access is universal, AI becomes a reliable extension of the health ecosystem rather than an experiment running alongside it. I feel that when innovation operates with the discipline of science and the humility that health demands, we will unlock the most valuable transformation.
That combination of ambition and accountability will define the next decade of digital medicine. The companies and researchers who embrace it will move farthest and create technologies that earn trust and improve care for millions.
