AI/Machine Learning Archives - HIT Leaders and News

Skip to main content

AI / Machine Learning

Fujitsu and Nvidia Work Together to Harness AI to Transform Healthcare

Artificial Intelligence

Artificial intelligence no longer needs to prove its potential in healthcare. It must now prove its composability. As automation efforts mature from pilot tools to enterprise-wide platforms, a new strategic question emerges: Can health systems orchestrate AI across diverse functions without introducing fragmentation, risk, or workflow redundancy?

Exploring how artificial intelligence fosters more personalized and empathetic healthcare experiences.

Cedars-Sinai Turns AI From Experiments Into Operating Model

Artificial Intelligence

Healthcare systems have spent the past two years proving that artificial intelligence can work in pockets. The harder work in 2025 has been institutional: turning scattered pilots into a foundation that can survive scale, scrutiny, and the realities of clinical labor. The most telling signal from Cedars-Sinai is not that dozens of AI projects exist. The signal is that AI is being treated as an operating model that spans nursing documentation, virtual care, remote monitoring, research, and workforce development.

Photo 19261384 © Gerry Boughan | Dreamstime.com

AI Trial Screening Shifts the Bottlenecks

Artificial Intelligence

The announcement that Mass General Brigham has spun out AIwithCare to commercialize a retrieval-augmented screening platform signals something more consequential than another hospital-built analytics tool. Clinical trial enrollment remains one of the most stubborn constraints on biomedical progress, and it has stayed stubborn despite decades of site networks, CRO scale, and patient-facing marketing. When a health system treats prescreening as software infrastructure instead of coordinator labor, the bottleneck does not disappear. It relocates into governance, data quality, and accountability.

ID 119285809 | Ai Health Care © Leowolfert | Dreamstime.com

AI Liability Is Forcing a New Era of Hospital Risk Management

Artificial intelligence is rapidly embedding itself in core hospital functions—from diagnostics and decision support to patient documentation and claims processing. But as this technology shifts from pilot tools to operational infrastructure, healthcare leaders are entering a legal gray zone that few are structurally prepared to navigate.

Oversight meets innovation: a visual exploration of governance at the intersection of medicine and artificial intelligence.

Mount Sinai Health System Will Roll Out Microsoft Dragon Copilot

Mount Sinai Health System’s announcement that it will deploy Microsoft Dragon Copilot marks yet another high-profile endorsement of AI-powered documentation tools. The integration of ambient voice capture and generative capabilities directly into clinical workflows signals a clear intent: free up clinicians’ time, reduce administrative load, and enhance patient-provider interaction.

Mount Sinai in New York City, New York

AI Adoption in Healthcare Is Surging, but Clinical Readiness Is Still Lagging

As generative AI tools accelerate their reach across healthcare, a revealing paradox is emerging: physicians are leading the charge in experimentation, but the systems meant to support them remain unprepared.

ID 119285809 | Ai Health Care © Leowolfert | Dreamstime.com

Microsoft’s Ambient AI Moves Into Nursing Workflows but Raises Broader Questions for Enterprise Integration

Microsoft’s expansion of its Dragon Copilot AI assistant into nursing workflows marks a notable shift in the trajectory of ambient clinical technology. What began as a physician-facing tool designed to streamline documentation is evolving into a platform with enterprise-wide ambitions, encompassing revenue cycle operations, third-party AI integration, and now the day-to-day responsibilities of frontline nurses.

Microsoft Dragon Copilot

Unclear Liability Poses a Growing Risk in AI-Driven Healthcare

As artificial intelligence continues its rapid integration into clinical workflows, a looming liability vacuum threatens to destabilize trust, governance, and accountability in patient care. While AI tools promise greater efficiency, diagnostic power, and operational agility, legal experts are raising alarms about the absence of clearly defined fault lines when outcomes go wrong.

JAMA: AI Tools in Health Care Are Spreading Faster Than We Can Govern Them

Artificial intelligence is already shaping how care is delivered, how health systems operate, and how patients access services. But the rapid pace of AI adoption is exposing a foundational gap: health care lacks the infrastructure, incentives, and oversight mechanisms to evaluate whether these tools are actually improving health outcomes.

The Doximity–OpenEvidence Lawsuit Could Redefine AI IP Boundaries in Healthcare

A legal feud between Doximity and OpenEvidence has pulled into sharp relief the growing pains of healthcare artificial intelligence, particularly where innovation meets intellectual property. At the center of the case are allegations that Doximity used fake physician accounts to “prompt hack” OpenEvidence’s AI platform, extracting proprietary inputs and underlying logic. Doximity denies the claims and has countersued for defamation.

AI Tools Reshape Post-Surgical Cancer Surveillance

A new class of artificial intelligence models is prompting healthcare leaders to reconsider the long-standing assumptions behind post-surgical cancer surveillance. The recent deployment of the RADAR CARE system by Samsung Medical Center offers a case in point. Trained on more than 14,000 early-stage non-small cell lung cancer (NSCLC) cases, the transformer-based model provides individualized predictions of recurrence risk within the first year after surgery.

AI Voice Agents Are Quietly Reshaping Chronic Disease Management

Early evidence from a study presented at the American Heart Association’s 2025 Hypertension Scientific Sessions suggests that AI voice agents may hold untapped potential in addressing persistent care gaps for older adults with chronic conditions. The trial, involving 2,000 Medicare-aged patients, found that AI-powered phone agents not only improved the accuracy of home blood pressure readings, but also elevated quality ratings, lowered care management costs, and improved clinical workflows.

Why Healthcare’s AI Future Still Needs a Blueprint

Despite record levels of experimentation and investment, most health systems remain underprepared to operationalize artificial intelligence at scale. New data from a joint report by the Healthcare Financial Management Association (HFMA) and Eliciting Insights shows that while 88% of organizations are using AI in some form, only 18% have achieved maturity which is a status defined by the presence of both a late-stage governance framework and a defined enterprise AI strategy.

Cleveland Clinic Deploys AI to Transform Clinical Trial Recruitment

Cleveland Clinic has launched a system-wide rollout of Dyania Health’s Synapsis AI platform following a series of successful pilot programs in cardiology, oncology, and neurology. The deployment signals a growing commitment to integrating medically trained large language models (LLMs) into clinical research workflows to accelerate trial recruitment and improve patient access to experimental therapies.

Gregg Killoren of Xsolis: How Hospitals and Health Systems Can Maintain Trust in a World of Rapid AI Adoption

The rapid adoption of artificial intelligence (“AI”) tools across the healthcare industry has vast potential to save both providers and payers time and money, simultaneously improving patient encounters and outcomes. Yet rapid adoption necessarily requires additional duties: namely, to use AI tools responsibly (AI governance), and to communicate to all parties, especially patients, how they’re being used (AI transparency).