AI health coaches aren’t just for athletes anymore. They’re starting to handle the day-to-day interpretation, advice, and treatment adjustments that once required doctors. From glucose monitoring to hypertension management, technologies like Dexcom, Teladoc, and Omada are quietly taking over the work of routine clinical decision-making. This new generation of digital health coaches marks the next step in a long trend — technology shrinking healthcare by making us need it less.

Lawmakers are moving to ban AI-only insurance denials, requiring human sign-off for every case. It sounds compassionate, but it locks us into the same slow, opaque, costly system. The smarter move is AI with guardrails — transparency, audits, and contestable rationales — for faster, clearer, more accountable decisions.

The naturalist John Muir saw how everything in nature is connected — and today AI is showing us the same truth inside the body. From Apple Watch studies on atrial fibrillation to new hypertension alerts, hidden links in long-collected data are transforming how we understand health.

From telemedicine to wearables, fewer clinic visits mean fewer emissions. And as new products like low-carbon inhalers and self-administered HIV therapies move toward over-the-counter access, the environmental benefits of consumerization become even clearer.

The overwhelming majority of health-relevant data —movement, behavior, speech, sleep — is now generated outside the clinical setting. As a result, health innovation is increasingly shifting toward consumer devices and tech platforms that actually hold the data — not the EHR or the healthcare system.

Hospitals worry about retail clinics and other healthcare competitors. But real disruption may come from outside healthcare entirely: cars that don’t crash. As autonomous driving becomes safer and more widespread, the revenue ripple effects on emergency departments, orthopedics, and imaging will be profound—and sooner than most systems expect.

The public conversation about AI in healthcare swings between extremes—some predict it will replace doctors, others that it will usher in a golden age for medicine. So which is it? In my recent American Family Physician editorial, I explore how AI is less a disruptor of doctors than a powerful accelerator of consumer-driven health.

Innovations like GLP-1 drugs, self-driving cars and AI therapy chatbots are driving down illness, injury and the demand for traditional care. Rather than just improving delivery, these shifts reduce the need for doctors altogether. Snack food CEOs are planning for an Ozempic world. Why aren’t healthcare execs?

As AI and smartphones put more diagnostic power into consumers’ hands, healthcare faces disruption not just within the clinic—but beyond it. From OTC drugs to pneumonia-detecting apps, selfcare is rising fast. This isn’t the future. It’s already here—and it’s shrinking the doctor’s role.

One year after analyzing FDA’s AI medical device approvals, a new dataset confirms: growth continues, but acceleration is absent. While more young companies are joining the field, older firms like GE still dominate approvals—classic sustaining innovation. And Big Tech? Still barely on the board.

Smartphone apps that can diagnose pneumonia? FDA-approved machines that can diagnose conditions without a doctor? Robot psychotherapy? It’s not coming, it’s here now.

Most doctors, nurses, PAs, techs, and others in healthcare aren’t familiar with the term “disruption” and are unaware of how technological trends have already begun disrupting their current business models. This post is the first of three that will provide a basic understanding of the term, and the phenomenon.

FDA approvals of AI-enabled medical devices are accelerating—but not in the way you might expect. While new startups are entering the space, the real winners remain legacy giants like GE and Siemens. An analysis of the latest FDA data reveals a classic case of sustaining innovation, not disruption, as established players integrate AI to reinforce their dominance.

Every map you’ve seen of the coronavirus epidemic obscures the progression of the epidemic, rather than informing. Not just one map, but all of them. Let me explain how.

If you’ve been following the outbreak, you have probably noticed the predominance of mapping in data visualization about the virus. Maps are great, of course, but I suspect that the huge explosion in GPS/GIS/mapping capabilities over the last decade has given us all a “hammer in search of a nail”, applying maps even when better dataviz tools might apply.