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AI / Machine Learning

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

AI/Machine Learning

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.

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AI Tools Reshape Post-Surgical Cancer Surveillance

AI/Machine Learning

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.

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AI Voice Agents Are Quietly Reshaping Chronic Disease Management

AI/Machine Learning

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.

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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).

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AI’s Growing Influence on Provider Choice Demands a Rethink of Patient Access Strategies

As generative AI becomes more integrated into daily life, a growing number of patients are turning to it for healthcare decision-making, particularly in choosing providers. A new survey from rater8 reveals that nearly one-third of patients now use AI tools to research doctors, and more than one-quarter report that AI directly influenced their provider selection.

The Future of AI in Healthcare Will Be Won in the Workflow

Ben Scharfe’s interview offers a grounded view of AI’s role in healthcare today. Instead of treating AI as a monolithic solution, he frames it as a set of targeted, specialty-aware tools designed to enhance, not replace, clinician performance.

Ben Scharfe of Altera Digital Health on Targeted AI Adoption in Healthcare

Artificial intelligence is moving rapidly into healthcare workflows, but as explored in last week’s HIT Leaders & News editorial AI in Healthcare Is Moving Fast but Trust Is Moving Slowly, technology readiness does not guarantee successful adoption.

AI Agents Are Entering the Frontlines of Patient Experience

As artificial intelligence transitions from back-end optimization to frontline engagement, a new collaboration between Stanford Health Care and Qualtrics is positioning AI agents not just as workflow tools, but as direct actors in patient-facing care navigation. The effort aims to unify operational, social, and experiential data into a single, proactive layer of automation, capable of identifying missed appointments, arranging transportation, and translating instructions across linguistic or cultural barriers.

AI Is Becoming the Front Door to Complex Care

Venture-backed digital health platforms are no longer positioning AI as an assistive tool within existing care models. Increasingly, they’re building entire care infrastructures around it. Two newly funded startups, like Citizen Health and Isaac Health, are taking different but complementary approaches to this shift, using AI to reengineer access, navigation, and outcomes for historically underserved populations. Together, their strategies signal a broader industry recalibration: AI is becoming the care journey.

AI in Healthcare Is Moving Fast but Trust Is Moving Slowly

Artificial intelligence is no longer a theoretical capability for healthcare systems. It has moved beyond pilot projects and vendor demonstrations into live, day-to-day workflows. From pre-visit patient history summarization to automated claims processing, AI is showing up in both the exam room and the back office.

Pediatric AI Safety Anxiety Tests Regulators and Hospitals

Hospitals have spent the past decade welcoming artificial-intelligence decision tools into emergency rooms and ICUs; now many executives confess the rollout moved faster than the evidence. The patient-safety watchdog ECRI put “unproven AI in direct-care settings” at the top of its 2025 Health Technology Hazards list, warning that children are “most likely to suffer disproportionate harm.” (ECRI and ISMP)

Separating Human and AI Duties in Radiology

Artificial intelligence entered diagnostic imaging with predictions of either superseding radiologists or amplifying their productivity. A Radiology editorial by Pranav Rajpurkar and Eric Topol argues that neither extreme matches current reality. Field surveys from HIMSS show eight in ten U.S. health systems have piloted at least one imaging algorithm, yet most frontline readers remain uncertain when to rely on machine guidance.

AI Care Managers Shift Healthcare’s Administrative Frontier

Sword Health built its reputation on digital musculoskeletal therapy, winning large employer contracts in the process. The company’s latest move establishes a new division, Sword Intelligence, that pivots proven in-house automation tools toward the broader provider, payer, and public-sector markets.