AI-assisted diagnostics, imaging, risk scoring, decision support, and clinical workflow augmentation.
AI in radiology has moved beyond pilot programs. FDA-cleared AI tools are now used in clinical settings for chest X-ray interpretation, mammography screening, stroke detection, and pulmonary embolism triage. Here is the current state of the field.
Read moreAmbient AI medical scribes use passive voice recognition and AI processing to generate clinical documentation during patient visits, reducing the time physicians spend on administrative tasks after the encounter.
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 moreThe healthcare AI vendor list changes constantly, funding rounds, acquisitions, renamed products. What doesn't change nearly as fast is the category map: the handful of jobs healthcare AI companies are actually built to do, and what to check before trusting any vendor's claim about doing one of them well.
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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