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Academic Research

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

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.

October 20, 2025

The future of clinical care: human insight amplified by AI in a balanced, symbiotic partnership.

Emotional Intelligence Training Reveals Medical Student Unease with AI Integration

As artificial intelligence expands across clinical operations, most healthcare narratives continue to center on gains in speed, documentation efficiency, and decision support. Yet new data suggest a different conversation is unfolding among future physicians, one shaped less by technological capability and more by professional identity.

October 13, 2025

Photo 210434732 | Health Information © Noipornpan | Dreamstime.com

Mount Sinai: Spatial Omics Enters the Usability Era

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.

October 8, 2025

Mount Sinai in New York City, New York

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

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.

Stanford’s CRISPR-GPT Signals a Paradigm Shift in Genomic Experimentation

The launch of CRISPR-GPT, a generative AI platform developed by Stanford Medicine, signals a consequential step toward making gene-editing not only faster but also more accessible. In a field historically bound by complexity, trial-and-error, and steep learning curves, this tool reframes CRISPR as a collaborative endeavor, augmented by artificial intelligence and accessible to non-specialists.

Mount Sinai’s New AI Lab Reflects the Next Phase of Cardiac Precision Care

The launch of the Samuel Fineman Cardiac Catheterization Artificial Intelligence Research Lab at Mount Sinai Fuster Heart Hospital marks a significant inflection point in how advanced cardiac care may be delivered, optimized, and scaled. More than a ceremonial nod to emerging technologies, the new lab reflects a deliberate integration of AI into one of the most complex, high-stakes environments in medicine: interventional cardiology.

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

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.

UVA Health: Testing if MRI Can Reveal Undetected Brain Injuries in Soldiers

Hidden injuries have always been one of the most insidious threats in military medicine. From early battlefield concussion to long-term neurological impairment, brain trauma often escapes early detection, particularly in soldiers exposed to repeated low-level blasts.

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

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.

Chulalongkorn University: Virtual Reality Is No Longer a Novelty in Emergency Care Training

The expansion of virtual reality (VR) training in emergency medicine, as demonstrated by Chulalongkorn University’s nationwide rollout of its ER-VIPE platform, signals a larger shift in how global health systems are redefining preparedness, team performance, and workforce resiliency. The program, designed to improve real-time collaboration among emergency clinicians through gamified, VR-based simulations, offers more than an instructional upgrade. It highlights an emerging operational strategy: using immersive, low-risk environments to hardwire team-based competencies at scale.

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

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.

UVAHealth: Will AI-Driven Mental Health Support Close or Widen the Care Gap in Oncology?

The promise of artificial intelligence in mental health care is rapidly transitioning from experimental to operational. For breast cancer patients in particular, AI-based interventions could radically reshape how psychological distress is detected, triaged, and managed across settings. But the core question remains unresolved: Will these systems meaningfully extend care, or create a new layer of digital inequity?

UVA Health: The Automation Imperative in Diabetes Technology

Efforts to fully automate insulin delivery for type 1 diabetes are entering a critical inflection point. For nearly a decade, the artificial pancreas has promised to transform care for millions of people managing the condition. But even with notable advancements in hybrid closed-loop systems, a fundamental barrier remains: user burden.

Predictive AI in the ED Must Focus on Flow, Not Just Forecasting

Emergency department (ED) overcrowding is not a new problem, but artificial intelligence may finally be offering new tools to address it. A large-scale, multi-hospital study from the Mount Sinai Health System shows that AI can accurately predict which ED patients will require hospital admission, hours earlier than traditional workflows allow.

Digital CBT Tools Offer a Scalable Solution to Geriatric Insomnia

As the U.S. healthcare system contends with rising demand from an aging population, digital behavioral health interventions are stepping into gaps that traditional delivery systems cannot easily fill. A recent clinical trial of SHUTi OASIS, a fully automated, online cognitive behavioral therapy (CBT) program for insomnia tailored to adults over age 55, demonstrates the promise of digital therapeutics for chronic sleep disorders in older adults.