Is Bias in Clinical AI Good or Bad? It’s More Complicated Than That
We do not need models that pretend every patient has equal or equitable access to care. We need models capable of recognizing disparities and responding to them.
We do not need models that pretend every patient has equal or equitable access to care. We need models capable of recognizing disparities and responding to them.
In the push toward a more seamless, digitally enabled future, it’s important we understand exactly how this rush to interoperability may actually harm patients, instead of helping, and what we can do to make sure they keep pace.
While there are growing efforts to link dental health with overall health, the urgency should be felt strongly not only by pregnant women but, more importantly, by their care providers and health insurance companies. For instance, pregnant women with gum disease are 3-4 times more likely to develop pre-eclampsia, an emergency condition.
AI genuinely could reduce barriers, by making health information more conversational, more personalized, and easier to act on. But that only happens if the people building these tools decide, from day one, that accessibility isn’t optional.
If we focus only on placement, we will continue to see cycles of progress and regression. If we focus on stability — on what happens after the keys are handed over — we have an opportunity to change those trajectories more permanently.
How much of precision interventional psychiatry can actually happen remotely, and where does the model hit a wall? The answer is potentially more nuanced than the conventional framing suggests.
The longer someone waits to seek help, the more their condition progresses, the harder it becomes to intervene, and the easier it becomes to keep waiting. Delay compounds. That's what makes it so dangerous, and so worth targeting directly.
The truth is there are ways to understand our cancer risk more precisely than we do today, and there are tools to manage it. What’s missing is awareness, access, and a system built to help us use these tools before something goes wrong.
Today’s technology has the potential to empower independent pharmacies to offer adherence support at a greater scale — and healthcare engagements that overlook this touchpoint miss out.
Resource-sharing across large networks of hospitals allows people to receive a higher quality of care across the country, regardless of where they live or how well-resourced their local health system may be.
An algorithm can flag an irregular heartbeat. But it cannot notice that Tom hasn't mentioned his late wife in two weeks, or that he's wearing the same shirt for the fourth day running. Those observations require a person in the room, not a sensor on the wall.
Sixty million rural Americans aren’t asking for another strategic framework. They’re asking for care — accessible, affordable, and delivered with dignity.
Health systems can turn insights into action, ensuring that preventive care actually happens by combining accurate risk prediction with human outreach and careful planning.
Our biggest healthcare problems aren’t only cost or access, but the breakdown in how diagnoses are made, especially for women. Digital health’s new era of AI and real-time data is the most promising solution I’ve seen to fix this, but for these tools to work for women, they must be built on data that actually includes them.