AI Healthcare
Asan/Samsung AI quantifies lung fibrosis to predict transplant or death risk
By Arjun
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3 Aug 11:49 AM
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Via Seoul Economic Daily
Korean teams at Asan and Samsung Medical Centers used AI CT analysis to score idiopathic pulmonary fibrosis; a one-year fibrosis-score rise of about 4.05% or more linked to roughly 2.8× higher transplant or death risk in validation, and improved prognostic models beyond demographics and lung function. Score changes also tracked FVC and DLCO declines. It matters as quantitative imaging moves from research demos toward prognosis tools. Caveat: thresholds need multi-center confirmation before routine transplant counseling use.
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Israel’s Sheba Medical Center, through its ARC innovation arm, is deploying OpenAI’s ChatGPT for Healthcare / OpenAI for Healthcare across clinical and operational workflows — the company’s first large international hospital partnership. Clinicians get cited answers drawn from literature and Sheba’s own protocols under role-based access, while patient data stays isolated and final decisions remain with staff. Sheba already runs imaging and ER documentation AI; this adds a generative layer plus early research-model access. It matters as a blueprint for AI-powered hospitals abroad. Caveat: governance, liability and local validation will decide whether the template travels.
A NEJM AI study across RWJBarnabas Health found Epic’s Deterioration Index, paired with automatic rapid-response alerts, cut high-risk inpatient mortality from 23.1% to 18.6% — about an 18% drop in risk-adjusted odds — while raising response-team activations. Leaders stress earlier critical-care eyes, not autonomous treatment. Follow-on work targets patients whose risk scores are rising fast. It matters because an EHR-native tool already in wide use may translate beyond one health system. Caveat: single-network results still need broader replication before national standard-of-care claims.
OpenAI for Healthcare and ChatGPT Health — encrypted clinical assistants launched earlier for a small set of U.S. hospitals — are now entering Sheba’s network as the first major non-U.S. deployment. The pitch is evidence-grounded decision support and lighter admin load, not autonomous diagnosis. Sheba will feed approved pathways into the workspace and return real-world clinical feedback to OpenAI. It matters because specialized medical model lines are becoming a competitive front in regulated care. Caveat: early hospital partnerships are not the same as proven outcome gains across health systems.
Operational detail from the RWJBarnabas deployment shows the Epic Deterioration Index continuously reading vitals, labs and nursing assessments, recalculating risk every fifteen minutes and paging rapid-response teams at top tiers. Nearly half of intervention patients generated alerts; not every alert triggered a full team activation. It matters because workflow integration — timing, paging, staffing — explains outcome changes as much as the model itself. Caveat: hospitals without comparable response capacity may not see the same mortality shift from software alone.