clinical AI

A new Nature publication describes a human–artificial intelligence collaboration model for comanaging epilepsy in real time. The approach pairs clinicians with AI systems to monitor and manage seizures as they happen, aiming to improve treatment decisions and patient outcomes. It is part of a growing body of work exploring how AI can support doctors in neurological care rather than replace them.

Axios examines why artificial intelligence has yet to transform health care despite widespread predictions of a revolution. The piece focuses on the obstacles standing between current pilot projects and meaningful clinical impact, including integration with existing medical systems, regulatory hurdles, and trust among doctors and patients. It frames the debate over how quickly AI tools can move from experimentation to everyday use in hospitals and clinics.

The US Department of Veterans Affairs has hosted its second international symposium on artificial intelligence in dentistry, held under the theme of trust, technology, and transformation. The event brings together experts to discuss how AI tools are being developed and applied in dental care, with a focus on improving treatment for veterans and setting standards for safe, reliable clinical use of the technology.

The World Health Organization has introduced a new AI-powered hub designed for health professionals to identify alcohol-related risks in patients and open up conversations that are often avoided. The tool aims to break taboos around drinking, including raising awareness of the established link between alcohol consumption and cancer, and to support earlier interventions in clinical settings.

Nature Medicine has published a series examining the real-world deployment of artificial intelligence in clinical care. The collection focuses on how AI tools perform once integrated into hospitals and everyday medical practice, moving beyond trial settings. Medical and technology commentators are closely following the series as AI adoption in healthcare accelerates.

Mental health experts are weighing in on whether chatbots and AI tools can safely be used for emotional support and guidance. The debate centers on AI's accessibility and affordability versus risks like unreliable advice, lack of clinical oversight, and potential harm to people in crisis. Clinicians urge caution, saying AI may help with mild concerns but cannot replace professional care.

CancerNetwork examines how artificial intelligence is changing oncology, focusing on its impact on clinical trials, everyday clinical workflows, and patient outcomes. The piece traces the technology's evolution in cancer care and considers where AI tools are being integrated, from trial design and patient matching to supporting treatment decisions and reducing administrative burden for clinicians.

A Philadelphia-based startup that uses artificial intelligence for medical analysis is being acquired in a deal worth up to $270 million. The report comes from the Philadelphia Inquirer, which says the acquisition could reach that value depending on terms. The move highlights continued investor and corporate interest in AI tools for healthcare diagnostics and clinical decision-making.

Northwestern Medicine has integrated OpenEvidence, an AI-powered clinical evidence platform, into its Epic electronic health record system. The move gives clinicians access to evidence-based medical answers directly within their existing workflow. The integration is drawing attention as health systems weigh how quickly to adopt AI tools embedded in core clinical software.

Medical commentators are highlighting which core responsibilities doctors must retain rather than delegate to artificial intelligence, even as clinical AI tools spread through healthcare. The argument, reported by Medical Xpress and echoed by UVA Health, is that some duties, such as final clinical judgment, accountability for diagnoses and empathetic communication with patients, should remain firmly human, whatever the technology's growing capabilities in analysis and documentation.

Regulators are drawing clear lines for medical AI, requiring that clinical tools be safe, fair, and accountable, with patient safety and clinical control placed above automation. The debate frames medical AI governance as a human rights test: developers and health providers must show algorithms do not discriminate, remain explainable, and keep doctors in charge of final treatment decisions.

Northwestern Medicine has integrated AI-powered clinical decision support directly into its electronic health record system, according to Crain's Chicago Business. The move aims to put AI-generated insights in front of physicians at the point of care rather than in separate tools. It highlights the growing push among major US health systems to bring artificial intelligence into everyday clinical workflows.

Researchers at Duke University are preparing to test an agentic AI system designed to support cardiovascular care. Unlike conventional tools, agentic AI can plan and carry out multi-step tasks with limited supervision, raising both interest and questions about safety and oversight in medicine. The trial positions Duke among academic institutions moving generative AI research from the lab toward clinical use, and observers are watching for results on effectiveness and patient outcomes.

A group of European experts has launched a new framework intended to guide how artificial intelligence is used in mental health care across the continent. The initiative aims to set shared standards for safe and responsible deployment of AI tools in clinical and therapeutic settings, offering practitioners and policymakers a common reference point.

Lobbyists and trade groups are working to influence how US states regulate artificial intelligence in medicine, according to STAT. As state legislatures draft rules for AI used in diagnosis, clinical decisions and patient care, industry groups are pushing for frameworks that avoid patchwork requirements, while patient advocates and some lawmakers want stronger oversight. The outcome could set the tone for national policy on medical AI.

CU Anschutz has published an editorial spotlight examining what it means to use artificial intelligence to summarize new medical evidence. The piece weighs the potential of AI tools to speed up reviews of emerging research against concerns over accuracy, bias and accountability when automated summaries inform clinical decisions. It is part of a broader debate in academic medicine about how far AI should be trusted in evidence synthesis.

Johns Hopkins Medicine has announced a partnership with a major medical artificial intelligence company. The collaboration is expected to bring AI tools into clinical care and research at one of the United States' most prominent hospital systems. The Business Journals rounded up the announcement in its '8 things to know' news briefing, though further details of the deal, including the firm involved and the scope of the partnership, have not been specified.

Recursion Pharmaceuticals, the clinical-stage biotech traded on Nasdaq under RXRX, is seeing elevated interest in its stock price, quote data and company news. The clinical-stage biotech is known for applying artificial intelligence and machine learning to drug discovery, making it a closely watched name among investors following the intersection of technology and pharmaceuticals. Traders are checking its quote and history as part of broader attention to biotech and AI-related stocks.

Telefónica has published a discussion on leadership in digital health, outlining how hospitals can be transformed through technology, data, and artificial intelligence. The piece frames digital transformation in healthcare as a leadership challenge, arguing that hospitals need strategic direction to integrate data and AI into clinical practice and operations. It adds to ongoing global debate about modernising health systems through innovation.

Discussions about artificial intelligence in medicine are circulating in global news coverage. The topic covers how AI tools are being used in healthcare, from diagnostics to treatment planning, and the debate over benefits and risks for patients and doctors. Interest reflects ongoing attention to how quickly these technologies are entering clinical practice.

The American Medical Association has outlined how it intends to help set the agenda for the future of artificial intelligence in healthcare. The organisation, which represents US physicians, is positioning itself as a voice in how AI tools are developed, evaluated and adopted in clinical settings. The announcement touches on a debate gaining urgency across medicine as AI moves from research into everyday patient care.

Healthcare's growing use of artificial intelligence is prompting renewed debate about who is responsible when things go wrong. A report by News-Medical argues that while AI can support diagnosis and clinical decisions, human accountability must remain central, with doctors and institutions retaining final responsibility for patient care and outcomes.

Kate Ryder, founder and CEO of healthcare startup Maven Clinic, says she gives her children the same message every day about how artificial intelligence is changing education. Her comments, shared in a CNBC interview, reflect growing concern among parents and executives that AI could reshape the skills kids need to learn and how schools teach them.

Artificial intelligence is being adopted only slowly in clinical trials, with validation and governance gaps cited as the main obstacles. Industry commentary says sponsors and regulators still lack agreed standards for verifying AI tools before they are used in trial design, patient selection or data analysis, leaving many organisations cautious despite the technology's potential to speed up drug development.

A healthcare commentary argues that artificial intelligence alone is not what is transforming clinical data, but rather three converging waves of change hitting the field at once. The piece examines how these simultaneous shifts are altering how clinical trial data is collected, managed and used, with implications for researchers, regulators and patients alike.

The Healthcare Sector Coordinating Council has published an AI clinical resilience playbook aimed at helping healthcare organizations deploy artificial intelligence safely. The guidance covers managing patient safety risks and cybersecurity threats that come with clinical AI systems. It is intended to give hospitals and other providers a practical framework for adopting AI while protecting patients and network infrastructure.

A systematic review published by BMJ Group examines how completely clinical trials testing large language model interventions in medicine report their methods and results. The review assesses whether trial reports include enough detail on model design, evaluation and outcomes for clinicians and researchers to judge reliability. Concerns about AI in healthcare are driving scrutiny of the evidence base behind these trials.

A physician-authored commentary argues that fears of artificial intelligence going rogue in medicine distract from a subtler risk: clinicians may over-rely on AI tools that are confidently wrong, quietly eroding medical judgment and patient safety. The piece calls for attention to everyday clinical use of AI rather than dramatic failure scenarios.