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AI diagnostics, drug discovery, clinical decision support, medical imaging, mental health chatbots, and the promise and peril of applying AI to human health — where the stakes of getting it wrong are measured in lives.
Bayesian Health's 510(k) clearance is real — the question of who owns the continuous monitoring data it generates is not yet answered.
Patients are refusing AI in the exam room before clinical deployment debates have resolved — putting adoption timelines under real pressure.
r/medicine's removal of two AI tool pitches reveals that healthcare professionals are treating access itself as the contested terrain, not the technology's merits.
Pathology AI trained on biased datasets reproduces those disparities at scale, making the tools most trusted by clinicians the ones most likely to harm underrepresented patients.
The tools physicians are adopting fastest — scribes, not robots — are being degraded by the same AI infrastructure they run alongside.