How healthcare AI is actually governed: the regulations that already apply, the governance framework a hospital needs, what responsible AI means in testable terms, and where compliance accountability sits.
A dated tracker of state laws governing AI in healthcare: what is enforceable today, what takes effect between October 2026 and July 2027, and the four obligations that repeat across states.
Read moreThere is no single 'AI in healthcare' law. What actually governs a healthcare AI tool today is a patchwork: FDA oversight if it qualifies as a medical device, HIPAA if it touches protected health information, ONC's HTI-1 transparency rule if it's in certified health IT, and voluntary frameworks like NIST's AI RMF filling the rest of the gap.
Read moreAn AI governance framework for a hospital is not a policy document. It is a standing decision process that says who approves an AI tool, what evidence they require, who owns it after go-live, and what triggers turning it off. Most of what gets published as a framework skips the last two.
Read moreResponsible AI in healthcare comes down to four testable commitments: the model was validated on people like your patients, a clinician can see why it said that, someone is accountable when it is wrong, and performance is still being measured. Everything else in the average responsible AI statement is decoration.
Read moreThere is no AI compliance regime in United States healthcare. There are existing obligations that AI tools fall inside, and the compliance work is figuring out which ones apply to which tool. That reframing removes most of the confusion and all of the waiting.
Read morePatients can now connect their medical records to ChatGPT, and roughly seven in ten health conversations happen outside clinic hours. What that means for provider organizations, and what to do about it.
Read moreSix companies dominate ambient clinical documentation and the marketing language is nearly identical across all of them. The questions that actually separate them are about correction time, retention, specialty fit, and coding behavior.
Read morePredictive analytics in healthcare covers two very different activities with very different risk profiles. The documented failures are not failures of accuracy, they are failures of validation population, outcome definition, and what happens after go-live.
Read moreMost healthcare predictive models are not bought as products. They arrive inside the EHR or get built internally, which is why the usual software evaluation process misses them entirely.
Read moreA buyer-oriented map of 27 healthcare AI companies organized by the job they actually do, with category-level evidence, regulatory, workflow, and data questions.
Read moreA hub for the healthcare AI infrastructure strategy layer: data locality, local inference, IBM Power 11, IBM i modernization, clinical governance, and hospital AI readiness.
Read moreHospital AI strategy cannot stop at software selection. Clinical AI, claims automation, EHR matching, imaging workflows, and AI governance all depend on infrastructure that can keep data close, systems available, and operational control visible.
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