Icahn School of Medicine at Mount Sinai Archives - HIT Leaders and News

AI Drug Discovery Finds Hidden Cancer Protein Pocket

Jason Free — Tue, 09 Jun 2026 11:57:00 +0000

Researchers at the Icahn School of Medicine at Mount Sinai have identified a hidden drug-binding pocket in PKMYT1, a cancer-related kinase involved in cell growth and division. The finding matters because it points toward a more selective route for drug design, while also exposing a central limitation in current AI drug discovery: models can predict known protein structures with striking power, but still miss biologically important states that only appear through experimental work.

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Healthcare AI Policy Index Exposes Governance Fragmentation

Jason Free — Mon, 08 Jun 2026 10:50:16 +0000

The new Health & AI Policy Index from researchers at the Icahn School of Medicine at Mount Sinai offers a timely warning for healthcare leaders: artificial intelligence policy is growing rapidly, but not coherently enough to give hospitals a simple governance path.

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AI Gene Mapping Models Could Reshape Precision Medicine

Jason Free — Tue, 26 May 2026 11:51:12 +0000

The development of an AI model that maps how genes work together inside human cells should not be viewed as another narrow bioinformatics advance. It points to a larger shift in how healthcare may eventually interpret disease, discover biomarkers, evaluate therapies, and translate molecular data into clinical decisions.

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The Architecture Healthcare AI Can No Longer Ignore

Jason Free — Mon, 23 Mar 2026 11:05:58 +0000

Healthcare has spent the last two years talking about artificial intelligence as though the central question were model intelligence. The more consequential question is turning out to be system design. A new study from the Icahn School of Medicine at Mount Sinai published in npj Health Systems found that an orchestrated multi-agent setup sustained markedly stronger performance than a single general-purpose agent under simulated clinical workloads, with accuracy holding up far better as concurrent tasks increased and compute use dropping by as much as 65-fold.

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Mount Sinai: AI That Asks Its Own Questions Could Transform Clinical Diagnostics

Jason Free — Mon, 20 Oct 2025 10:11:41 +0000

Artificial intelligence in health care is often discussed in terms of automation and pattern recognition, but a new system developed at the Icahn School of Medicine at Mount Sinai signals a more profound shift: AI that can tailor its diagnostic reasoning to individual patients and recognize when it lacks enough information to proceed. The system, called InfEHR, challenges traditional models of clinical support by operating not just as a predictor, but as a dynamic inference engine.

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Mount Sinai: Spatial Omics Enters the Usability Era

Jason Free — Wed, 08 Oct 2025 20:16:02 +0000

As spatial omics technologies mature from laboratory breakthroughs to research mainstays, the bottleneck has shifted decisively from data generation to data interpretation. In this next phase, the value of spatial profiling will depend less on molecular resolution and more on analytical accessibility. Tools that can absorb multi-modal inputs, support varied research workflows, and deliver interpretable outputs at scale are now prerequisites for meaningful scientific use. Without them, spatial omics risks becoming an underutilized luxury rather than a transformative standard.

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Mount Sinai: AI Coding Accuracy Improves When Models Are Taught to Look First

Jason Free — Mon, 29 Sep 2025 11:50:57 +0000

A new study from the Icahn School of Medicine at Mount Sinai offers a deceptively simple insight: large language models assign medical diagnosis codes more accurately when prompted to consult similar past cases before selecting a code.

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Mount Sinai: New AI Tool Addresses Accuracy and Fairness in Data to Improve Health Algorithm

Jason Free — Tue, 09 Sep 2025 12:30:33 +0000

Artificial intelligence in healthcare has long carried a promise: faster diagnoses, predictive risk scores, and precision interventions, all at scale. But recent years have exposed a caveat baked into that optimism: if the training data isn’t representative, the output can’t be trusted. The result is a clinical inequity.

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Icahn School of Medicine at Mount Sinai: AI-Driven Penetrance Modeling Pushes Genetic Risk Into Operational Territory

Jason Free — Wed, 03 Sep 2025 11:28:19 +0000

The announcement from the Icahn School of Medicine at Mount Sinai that its researchers have developed an AI-powered approach to determine the penetrance of rare genetic variants marks a strategic breakthrough, not just in genomics, but in how healthcare organizations operationalize genetic information. By using routine lab tests and machine learning to estimate the real-world disease risk associated with rare variants, Mount Sinai’s team is reframing what genomic data means in clinical care.

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Icahn School of Medicine at Mount Sinai: AI Image Analysis Tools Like MARQO Will Force a Reckoning in Cancer Diagnostics

Jason Free — Mon, 01 Sep 2025 15:27:08 +0000

The debut of MARQO, a next-generation image analysis tool developed by researchers at the Icahn School of Medicine at Mount Sinai, marks more than a technical advancement in oncology research. It signals a strategic inflection point in the evolution of diagnostic pathology, one that challenges longstanding assumptions about speed, scale, and the role of human interpretation in cancer care.

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