Healthcare AI's next test is integration While advanced AI excels at processing clinical data, healthcare's true test lies in overcoming deeply fragmented administrative workflows. The entrance of major AI companies into healthcare is a meaningful and welcome development, accelerating the technical foundation available to the industry. Their models are increasingly capable of processing long clinical records, interpreting complex terminology, comparing documentation against evidence and generating coherent summaries from large volumes of information. For clinicians, operators, and administrative teams who spend significant time searching through fragmented data, these advances are helping reduce cognitive burden and make high-value information easier to access. But healthcare leaders should not confuse model capability with operational capability. Healthcare's administrative challenges are caused by fragmented information, fragmented workflows, and fragmented accountability, not a lack of information.

The UK needs new regulations for AI products used in the NHS and other healthcare settings, says Britain's industry watchdog. The Medicines and Healthcare Products Regulatory Agency (MHRA), which regulates all medical devices and licenses treatment drugs in the UK, has published 44 recommendations to update its policies as the use of AI in the sector rises. The technology will soon be routinely used within the NHS, MHRA chief Lawrence Tallon told the BBC. What I would expect is that patients will... increasingly see AI as part of the way that normal NHS healthcare is delivered, he said. That should happen in a way that they can maintain their trust and their confidence in what's happening.

AI-assisted modernization can reduce the time and complexity of transforming legacy systems while creating a foundation for faster innovation, says Asifa Sherazi, CIO of health insurance at Bupa and Sanjeev Tripathi, senior VP, region head of BFSI, healthcare, and public sector at Infosys. For years, legacy technology has been a problem companies knew they needed to solve, but one they often struggled to tackle. The cost, complexity, and risk of replacing business-critical systems could make modernization feel like a disruption to manage instead of an opportunity to pursue. But with the rise in customer expectations and the changes AI brought to the economics of software development, that calculation is changing. Bupa's modernization of its My Bupa mobile application offers a case study in what becomes possible when a legacy migration is treated as a business transformation rather than a technology rewrite. Bupa CIO of health insurance Asifa Sherazi describes the risks of waiting for legacy systems to become an emergency: "The end-of-life technology is a risk that compounds quietly, and then arrives all at once." For Bupa, moving its application from Xamarin to native Swift and Kotlin improved the app rating from 3.7 to 4.7, while the user-perceived crash rate fell by nearly 24 percentage points on Android and eight points on iOS. "What they'll notice is that when they need us, often at a stressful moment, it just simply works," Sherazi says. Sanjeev Tripathi, senior vice president and region head of BFSI, healthcare, and public sector for Australia, New Zealand, and Southeast Asia at Infosys, contends that AI is helping change the equation. "The emergence of AI is fundamentally shifting the economics of modernization," he says, reducing the effort, risk, and time traditionally associated with these programs. At Bupa, combining AI-assisted reverse engineering with forward engineering helped deliver the transformation in approximately 60% less time than would have been possible in the pre-AI era.

For Ache Kodo, seeing a doctor once meant saving money for the journey. Living in Bitkine, in central Chad, she said a medical visit could mean paying for transport, the consultation and other costs of travelling to a larger town. Now, she can consult a doctor remotely through a Telemedan kiosk closer to home. "Telemedan changed how my family accessed healthcare services," Ache told Al Jazeera. "It was difficult before, as it involved me saving money to travel just to see a doctor. Now I'm able to do it on time with fewer costs from where I am. It has also enhanced my economic status."

Anne Fehres and Luke Conroy AI4Media Humans Do The Heavy Data Lifting Licenced by CC-BY 4.0 The Royal Statistical Society's AI Task Force has issued a critical mandate via a new paper, AI Regulation Needs Statistics, which demands that statistical principles actively shape global AI governance. The publication escalates the core argument of their foundational work, AI is Statistics . This earlier paper argued that AI is fundamentally statistical, meaning effective and ethical deployment is impossible without statistical literacy. You can watch our expert panel discuss the topic here . A major focus of that work was around the challenges of evaluating AIs, given that they are dynamic systems that continue to evolve once they have been deployed in the real world.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Gubernatorial candidates Steve Hilton, left, and Xavier Becerra spoke Tuesday in Sacramento about their plans if elected to lead California. This is read by an automated voice. Please report any issues or inconsistencies here . See more from the L.A. Times in Google Search.

'I ran because I knew I would die': Russian drones target medics in Ukraine Inna Lytvynenko always puts on her bright orange body armour when she's out on an emergency call. Last June, the paramedic was sent out in Kherson to treat a woman injured in a Russian drone attack, when an FPV drone smashed into her ambulance. Seconds later, she heard a buzzing sound in the sky. Another drone was flying straight towards her, fast. I grabbed my trauma bag and started running, she recalls.

In healthcare settings where patients use LLMs as a medical assistant, LLM performance differs between evaluation and deployment. Closing the gap requires making assumptions explicit, testing which assumptions hold, and updating evaluation protocols accordingly. Healthcare LLM benchmarks are one of the main paradigms by which LLMs are evaluated prior to clinical settings. Benchmarks provide a stable goalpost that allow researchers to iterate quickly and measure progress consistently. However, in high-stakes domains like healthcare, that same abstraction becomes a liability.

Japan's AI gamble: Can technology offset the cost of an ageing society? Beneath the business towers of Tokyo's Otemachi district, a test is taking place to see whether artificial intelligence can help solve one of Japan's biggest economic challenges: a shrinking workforce. Deep underground, Marunouchi Heat Supply Company operates a 30km (18.6-mile) network of heating and cooling pipelines serving offices, commercial buildings and transport facilities in one of Japan's most important business areas. The company is now using AI to manage this complex infrastructure with the goal of moving towards more automated operations by 2027. Developed with the Tokyo-based AI company Preferred Networks, the project uses PlantPilot, an AI system trained on historical operating data and the expertise of experienced engineers.

Pillay is an editorial fellow at TIME. America's healthcare system is notoriously reactive. Could AI shift it from a system that treats illness to one that prevents it? The question framed a panel discussion at the inaugural TIME100 AI Leadership Forum on May 27, which featured Dr. Omar Lateef, the president and CEO of Rush University System for Health; Arianna Huffington, the founder and CEO of Thrive Global; and Neil Lindsay, senior vice president of Amazon Health Services (Amazon One Medical, an Amazon health service, was an event sponsor). The conversation was moderated by TIME senior health correspondent Alice Park.