Johns Hopkins established the model for American medical education more than a century ago. Today @HopkinsMedicine and OpenEvidence announced a collaboration to define what that education should look like in the AI era, and to ground clinical care in the latest evidence. As AI learns to synthesize the world's medical knowledge in seconds, the clinician's role moves toward clinical judgment, first-principles reasoning, and the human work of caring for patients. The education model Johns Hopkins and OpenEvidence will design builds on those foundations, with a deeper grounding in pathophysiology and evidence-based medicine. Knowing why an ACE inhibitor works is understanding a physician carries through an entire career. Together we'll also build human-first AI that puts the best available evidence in the context of each patient's medical history and Johns Hopkins' best practices, so clinicians can reason through that information faster and make the best decision for their patient. Sanjay Desai, Vice Dean for Education at Johns Hopkins Medicine, says AI "invites us back to the bedside." For the educators here: which skills do you want every trainee to keep sharp?

Rough back of the envelope math: if 40% of US physicians are OpenEvidence DAUs (60%+ are registered users) and there's ~22 workdays a month, 42m queries in August implies ~5 queries per workday day per physician (Google is ~4.2 searches per day and ChatGPT is ~2.5 queries per day). This kind of pulse on real-time clinical uncertainty is remarkable. Imagine you could now categorize those queries to create a map of the frontier of knowledge / know-how in medicine, and what's missing. By doing so, OpenEvidence is creating the closest thing to a universal ledger of medical problems and solutions. OpenEvidence can see for which flavors of clinical uncertainty existing knowledge / know-how / solutions are most scant (or don't exist at all). They can then find and plug in that knowledge / know-how, create it by funding studies / initiatives, and eventually even discern the best heuristics / simulation environments to train the judgement of the next generation of human capital in the life sciences. You start to see how OpenEvidence's flywheel will start to spin a lot faster than the labs in terms of medical capabilities. The general lesson here is that it's all about your product sensing and enumerating demand in some valuable subset of economic reality faster than competitors. In this case, OpenEvidence enumerates physician demand for medical knowledge / know-how that can help solve patient problems. They do this faster and with higher fidelity than anyone else. This is incredibly valuable.