LONDON — A U.K. commission on Thursday unveiled its recommendations for how the country should regulate artificial intelligence in medicine, as health authorities globally try to determine how to continuously review ever-changing products after they have been authorized instead of simply clearing them once for the market.
In its report, the commission sought to strike a balance between ensuring that the U.K. takes advantage of AI’s potential in medicine — the ability to review the millions o
LONDON — A U.K. commission on Thursday unveiled its recommendations for how the country should regulate artificial intelligence in medicine, as health authorities globally try to determine how to continuously review ever-changing products after they have been authorized instead of simply clearing them once for the market.
In its report, the commission sought to strike a balance between ensuring that the U.K. takes advantage of AI’s potential in medicine — the ability to review the millions of scans that are generated each year to track the eye health of patients with diabetes, for example — while prioritizing safety, tracking device performance over time, and treating patients equitably.
The 44 recommendations include some that would allow for the staged authorization of new AI models and others that would build a system for providers to report how tools are performing in their clinics — including when they malfunction or potentially inhibit patient care — as the models learn and adapt and perform differently depending on the setting.
WASHINGTON — The Trump administration permanently filled four key leadership roles at the Food and Drug Administration on Tuesday, after it nominated Heidi Overton to lead the agency late last month.
Three of the officials are serving in an acting capacity, and will take the same roles permanently, the administration said. They are Michael Davis, a psychiatrist who will be director of the Center for Drug Evaluation and Research; former Merck official Karim Mikhail, who will be director of th
WASHINGTON — The Trump administration permanently filled four key leadership roles at the Food and Drug Administration on Tuesday, after it nominated Heidi Overton to lead the agency late last month.
Three of the officials are serving in an acting capacity, and will take the same roles permanently, the administration said. They are Michael Davis, a psychiatrist who will be director of the Center for Drug Evaluation and Research; former Merck official Karim Mikhail, who will be director of the Center for Biologics Evaluation and Research; and Bret Koplow, an attorney who has been at the FDA since 2011 and will serve as director of the Center for Tobacco Products.
The fourth appointee, Jared Seehafer, will serve as the FDA’s first deputy commissioner for technology and artificial intelligence. He joined the FDA as an adviser in 2025 after founding a life science software company and working in investment advising in biotech, according to his LinkedIn. Under the Trump administration, the agency has pushed its staff to use AI to speed up its review processes.
Residue and stains plastered on production walls, floors, and manufacturing equipment. Corrosion on a product line. An employee wearing open-toed sandals.
These were among the unsanitary conditions found last April by a Food and Drug Administration inspector during a visit to Shoolin Pharma in Gujarat, India, where the company makes active pharmaceutical ingredients for more than a dozen medicines, including erectile dysfunction and anti-convulsant pills, that are sold to compounding pharmaci
Residue and stains plastered on production walls, floors, and manufacturing equipment. Corrosion on a product line. An employee wearing open-toed sandals.
These were among the unsanitary conditions found last April by a Food and Drug Administration inspector during a visit to Shoolin Pharma in Gujarat, India, where the company makes active pharmaceutical ingredients for more than a dozen medicines, including erectile dysfunction and anti-convulsant pills, that are sold to compounding pharmacies in the U.S., according to the agency.
The concerns about filth and possible contamination in the production and packaging areas — as well as incomplete laboratory testing records — prompted the agency last month to halt all product shipments by the company into the U.S., the FDA disclosed in an Aug. 18 warning letter that was posted on the FDA web site last week.
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I hope you all had a good Labor Day weekend. Robots may be able to run extremely fast now, but at least we know how to stop. Run your thoughts to me anytime: bob.herman@statnews.com.
A cash parade for a top House Democrat
UnitedHealth Group’s top executives, including CEO Stephen Hemsley, and board members recently flooded the campaign coffers of Rep. Katherine Clark of Massachusetts, the No. 2 D
UnitedHealth Group’s top executives, including CEO Stephen Hemsley, and board members recently flooded the campaign coffers of Rep. Katherine Clark of Massachusetts, the No. 2 Democrat in the House who just beat two opponents in a closely watched primary.
You’re reading the web edition of D.C. Diagnosis, STAT’s twice-weekly newsletter about the politics and policy of health and medicine. Sign up here to receive it in your inbox on Tuesdays and Thursdays.
House Republicans canceled the last two weeks of their session in September, which means House lawmakers will have gotten at least 15 weeks off between Memorial Day and the midterms on Nov. 3. Send news tips and PTO requests to John.Wilkerson@statnews.com or John_Wilkerson.07 on Signal.
Hou
You’re reading the web edition of D.C. Diagnosis, STAT’s twice-weekly newsletter about the politics and policy of health and medicine. Sign up here to receive it in your inbox on Tuesdays and Thursdays.
House Republicans canceled the last two weeks of their session in September, which means House lawmakers will have gotten at least 15 weeks off between Memorial Day and the midterms on Nov. 3. Send news tips and PTO requests to John.Wilkerson@statnews.com or John_Wilkerson.07 on Signal.
House Democrats’ agenda takes shape
In the week that the House was in town, a fuzzy outline of the health care agenda that Democrats are considering should they win back the House started to take form.
Arm has launched Arm Total Design for Physical AI alongside a new robotics framework to establish common standards across automated systems.
Physical industries – spanning mining, agriculture, manufacturing, and global transport – account for trillions of dollars in economic activity and an estimated $200 billion annual compute opportunity by the 2030s.
To address engineering fragmentation across these sectors, Arm is convening more than 80 partner organisations spanning software, hardware
Arm has launched Arm Total Design for Physical AI alongside a new robotics framework to establish common standards across automated systems.
Physical industries – spanning mining, agriculture, manufacturing, and global transport – account for trillions of dollars in economic activity and an estimated $200 billion annual compute opportunity by the 2030s.
To address engineering fragmentation across these sectors, Arm is convening more than 80 partner organisations spanning software, hardware, and AI. Initial ecosystem participants include AWS, ECARX, Hugging Face, Liquid AI, NXP, PlusAI, PSYONIC, QNX, Qwen, Siemens, and Unitree Robotics.
The initiative targets physical systems that combine AI models, runtime software, compute silicon, sensors, and actuators to sense, reason, and act in operational environments. Hardware manufacturers and software developers require standardised baselines to reduce integration risk, optimise compute workloads, and move from proof-of-concept testing to deployment at scale.
Arm standardises capability tiers for robotics systems
Robotics currently lacks a common method to describe, compare, and communicate system capabilities, according to an architectural manifesto (PDF) published by Arm chief architect Richard Grisenthwaite. This fragmentation makes robotic systems harder to design, integrate, and scale across industrial deployments.
In response, Arm has introduced the Robotics Capability Framework as a collaborative starting point for a shared technical vocabulary, patterned after the SAE Levels used for driving automation.
Arm’s new framework categorises robotic systems across progressing tiers of operational sophistication, mapping machines from reactive setups to context-aware, cognitive, and self-improving systems.
Each capability tier links real-world use cases to machine behaviours, outputs, and hardware constraints. These criteria establish parameters for system latency, compute placement, memory allocation, power constraints, determinism, and safety standards.
Arm developed the initial baseline using feedback from across the robotics sector. Participating organisations contributing to the framework include Anaxi Labs, ANYbotics, FMC³ Robotics, Fourier, GALBOT, Gravis Robotics, Lenovo, McKinsey, and Robotec.ai.
Virtual platforms accelerate pre-silicon automotive physical AI development
Arm Total Design for Physical AI extends a collaborative development structure previously used for cloud AI infrastructure. The programme brings together AI models, virtual platforms, digital twins, sensors, compute silicon, and software stacks to enable earlier development and testing cycles.
Autonomous transport and robotics face common technical requirements across sensory perception, AI processing, real-time control, safety, and power-efficient compute. Arm demonstrated this collaborative methodology in the automotive sector alongside AWS, Google, HERE, RemotiveLabs, and Siemens.
The participating automotive companies developed an integrated digital cockpit reference solution. This environment enabled software engineering teams to develop, test, and validate complex automotive code on the Arm Zena CSS platform prior to physical silicon availability.
Arm is now soliciting technical contributions from the wider engineering community to expand the Robotics Capability Framework as physical AI implementations progress.
Learn more about physical AI during thePhysical AI Expoheld in Amsterdam, London, and North America.
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.
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Motional and MIT researchers have built a system that lets self-driving cars explain their decisions in real-time, tackling the black-box problem in autonomous vehicle AI.
The work, published in Nature, comes from a team at Motional that includes CEO Laura Major, working alongside researchers from MIT’s Computer Science and Artificial Intelligence Laboratory. Their proposed method, called the Concept-Wrapper Network or CW-Net, aims to translate the internal calculations of a self-driving syst
Motional and MIT researchers have built a system that lets self-driving cars explain their decisions in real-time, tackling the black-box problem in autonomous vehicle AI.
The work, published in Nature, comes from a team at Motional that includes CEO Laura Major, working alongside researchers from MIT’s Computer Science and Artificial Intelligence Laboratory. Their proposed method, called the Concept-Wrapper Network or CW-Net, aims to translate the internal calculations of a self-driving system’s neural network into concepts a human can actually read.
If a current self-driving car brakes hard on a clear road with no obvious hazard in sight, neither the driver nor a passenger has any way of knowing why. Modern self-driving systems increasingly rely on neural networks trained on large volumes of driving data. Those networks can perform well, but they don’t expose their reasoning, which is why engineers describe them as black boxes.
Translating neural network logic into human concepts
CW-Net works by converting a self-driving system’s internal logic into concepts such as “Approaching Stopped Vehicle” or “Close to Cyclist.” These could, according to Motional, appear on a dashboard showing which concepts are influencing the vehicle’s driving decisions as they happen.
The system is designed so the explanations aren’t generated after the fact as a guess at what the network might have been doing. Instead, the vehicle’s final decision-making system takes action based directly on these human-interpretable concepts, so a braking event traces back to a specific concept that triggered it. Motional describes this as causally faithful, distinguishing it from approaches that generate natural-language explanations, which can read as plausible without necessarily being accurate.
Laura Major frames the case for this kind of interpretability against the alternative of relying purely on end-to-end deep learning to handle driving decisions.
“The general end-to-end only approach can get to a really good 80-90 percent – maybe even 95 percent – solution, but that’s not good enough to remove a driver or to earn the trust of cities, communities, and customers,” she said.
Testing explainable AI for self-driving cars around Las Vegas
Explainable AI research has largely stayed confined to computer simulations in lab settings, according to Motional. The Motional and MIT team instead deployed CW-Net on an autonomous vehicle with an experienced safety operator in the driver’s seat, collecting data on a private test track and on public roads around Las Vegas.
The team used an earlier experimental version of its deep-learning-based planning system, described as showing competitive performance but with notable shortcomings that CW-Net could help surface. Two incidents from the testing illustrate what the system caught.
In one, the autonomous vehicle repeatedly stopped near a traffic cone, and the vehicle operator assumed the cone itself was triggering the behaviour. Researchers removed the cone and the car stopped anyway. CW-Net’s display showed the actual cause: the experimental planning system was hallucinating a stopped vehicle ahead, a pattern traced back to its training data. That explanation let the researchers understand, predict, and resolve the issue.
A second test involved a cyclist. The autonomous vehicle detected and stopped for the cyclist as expected, but CW-Net revealed that the experimental planning system wasn’t actually basing its decision on the cyclist’s presence. The safety driver responded by exercising more caution around cyclists after noticing this. Follow-up analysis confirmed that caution was warranted, because the vehicle’s braking in that case came from a safety backup system rather than the experimental deep-learning-based planner.
Performance held steady against explainability
Adding layers of explainability to an AI system carries a known cost in speed and performance, and Motional acknowledges that risk. However, when researchers benchmarked CW-Net against leading autonomous driving algorithms, the difference in driving capability came in at less than one percent.
The Las Vegas incidents show why that trade-off matters operationally rather than just academically. A safety driver who can see that a stop is caused by a hallucinated vehicle, or that a backup system rather than the primary planner is responsible for a manoeuvre, can respond and report with more precision than one working from behaviour alone.
That visibility feeds directly into how quickly an engineering team can diagnose a system, and how confidently a safety operator can distinguish between an intended behaviour and a fault.
Motional connects the CW-Net work to broader pressure on autonomous vehicle operators as the technology extends into new markets and jurisdictions. Regulators are naturally asking for more transparency about how AI systems reach their decisions, and it expects tools like CW-Net could move from research projects toward a baseline requirement.
Beyond passenger vehicles, autonomous drones and even robotic surgery are cited as other safety-critical domains where operators and developers will need ways to understand a system’s capabilities, limitations, and unexpected behaviours.
Learn more about physical AI during thePhysical AI Expoheld in Amsterdam, London, and North America.
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
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