Healthcare AI Is Already in the Exam Room. The Patients Weren't Asked.

A Columbia Business School professor named the problem better than anyone in a lab coat has managed to: "AI tourism," Carri Chan called it — the pattern of hype that circles clinical settings without actually landing in them. The phrase traveled because it gave a name to something patients and practitioners had both been feeling without being able to articulate. Healthcare AI is everywhere in press releases and nowhere in outcomes data, and the gap between those two realities is where the current conversation actually lives.

That gap has gotten personal. A Bluesky post from a parent describing two separate medical appointments in a single week — both recorded by AI without any real opportunity to object — accumulated responses that revealed the same experience happening quietly across the country. This is what the institutional discourse keeps missing: AI hasn't arrived in healthcare as a policy question. It arrived as an awkward moment in an exam room, where the power dynamics made saying "no" feel impossible. Google's Fitbit expansion, which now lets users upload medical records for AI health coaching, landed in this same mood. The framing was patient empowerment; the reception was wariness about what a platform whose business model runs on attention will actually do with your lab results.

The labor conversation is running on a different track, and it's producing its own friction. A Washington Post breakdown of healthcare roles by AI exposure — circulating widely on Bluesky — put medical secretaries and administrative assistants at highest risk, with physicians and nurses considerably lower. That ordering cuts against everything the public has been told about AI threatening knowledge workers first. In healthcare communities, the subtext landed hard: the roles most exposed to automation are already the most stretched and the least compensated, the people who manage the logistics that keep the system from collapsing. The high-status clinical roles, for now, remain largely insulated. Whether that's a feature or a bug depends on who you ask, and the answers are not converging.

The research pipeline keeps producing genuinely impressive announcements — Roche's NVIDIA Blackwell GPU deployment for drug discovery, Microsoft's GigaTIME protein mapping tool, MIT's neural circuit work on brain cancer. These are real, and the science press covers them as such. But they are not driving the emotional temperature of anything. The gap between "AI will transform how we find cancer drugs" and "my doctor's AI phone line couldn't tell me whether my prescription was covered" is not narrowing, and the research announcements aren't designed to narrow it. They're speaking to a different audience entirely.

ChatGPT Health is functioning as the sharpest test of this split. A widely shared MedCityNews piece argued that the product should be understood as a cultural event rather than a clinical intervention — that millions of people consulting AI about their bodies changes the relationship between patients and medical authority whether or not any clinical outcome improves. That framing is gaining purchase because it stops trying to answer the unanswerable question of whether the AI is "good enough" and starts asking what it means that we're already past the point of asking. The conversation is moving toward consent infrastructure and regulatory frameworks not because the technology is failing, but because it's succeeding fast enough that the social agreements required to make it legitimate are visibly absent. The exam room already changed. The paperwork hasn't caught up.