Imagine chatting with an AI about a health concern, and instead of just processing your words, it could actually look at the photo of that worrying rash or make sense of your ECG printout. That’s what Google is aiming for.

We already knew AMIE showed promise in text-based medical chats, thanks to earlier work published in Nature. But let’s face it, real medicine isn’t just about words.

Doctors rely heavily on what they can see – skin conditions, readings from machines, lab reports. As the Google team rightly points out, even simple instant messaging platforms “allow static multimodal information (e.g., images and documents) to enrich discussions.”

Text-only AI was missing a huge piece of the puzzle. The big question, as the researchers put it, was “Whether LLMs can conduct diagnostic clinical conversations that incorporate this more complex type of information.”

Google teaches AMIE to look and reason

Google’s engineers have beefed up AMIE using their Gemini 2.0 Flash model as the brains of the operation. They’ve combined this with what they call a “state-aware reasoning framework.” In plain English, this means the AI doesn’t just follow a script; it adapts its conversation based on what it’s learned so far and what it still needs to figure out.

It’s close to how a human clinician works: gathering clues, forming ideas about what might be wrong, and then asking for more specific information – including visual evidence – to narrow things down.

“This enables AMIE to request relevant multimodal artifacts when needed, interpret their findings accurately, integrate this information seamlessly into the ongoing dialogue, and use it to refine diagnoses,” Google explains.

Think of the conversation flowing through stages: first gathering the patient’s history, then moving towards diagnosis and management suggestions, and finally follow-up. The AI constantly assesses its own understanding, asking for that skin photo or lab result if it senses a gap in its knowledge.

To get this right without endless trial-and-error on real people, Google built a detailed simulation lab.

Google created lifelike patient cases, pulling realistic medical images and data from sources like the PTB-XL ECG database and the SCIN dermatology image set, adding plausible backstories using Gemini. Then, they let AMIE ‘chat’ with simulated patients within this setup and automatically check how well it performed on things like diagnostic accuracy and avoiding errors (or ‘hallucinations’).

The virtual OSCE: Google puts AMIE through its paces

The real test came in a setup designed to mirror how medical students are assessed: the Objective Structured Clinical Examination (OSCE).

Google ran a remote study involving 105 different medical scenarios. Real actors, trained to portray patients consistently, interacted either with the new multimodal AMIE or with actual human primary care physicians (PCPs). These chats happened through an interface where the ‘patient’ could upload images, just like you might in a modern messaging app.

Afterwards, specialist doctors (in dermatology, cardiology, and internal medicine) and the patient actors themselves reviewed the conversations.

The human doctors scored everything from how well history was taken, the accuracy of the diagnosis, the quality of the suggested management plan, right down to communication skills and empathy—and, of course, how well the AI interpreted the visual information.

Surprising results from the simulated clinic

Here’s where it gets really interesting. In this head-to-head comparison within the controlled study environment, Google found AMIE didn’t just hold its own—it often came out ahead.

The AI was rated as being better than the human PCPs at interpreting the multimodal data shared during the chats. It also scored higher on diagnostic accuracy, producing differential diagnosis lists (the ranked list of possible conditions) that specialists deemed more accurate and complete based on the case details.

Specialist doctors reviewing the transcripts tended to rate AMIE’s performance higher across most areas. They particularly noted “the quality of image interpretation and reasoning,” the thoroughness of its diagnostic workup, the soundness of its management plans, and its ability to flag when a situation needed urgent attention.

Perhaps one of the most surprising findings came from the patient actors: they often found the AI to be more empathetic and trustworthy than the human doctors in these text-based interactions.

And, on a critical safety note, the study found no statistically significant difference between how often AMIE made errors based on the images (hallucinated findings) compared to the human physicians.

Technology never stands still, so Google also ran some early tests swapping out the Gemini 2.0 Flash model for the newer Gemini 2.5 Flash.

Using their simulation framework, the results hinted at further gains, particularly in getting the diagnosis right (Top-3 Accuracy) and suggesting appropriate management plans.

While promising, the team is quick to add a dose of realism: these are just automated results, and “rigorous assessment through expert physician review is essential to confirm these performance benefits.”

Important reality checks

Google is commendably upfront about the limitations here. “This study explores a research-only system in an OSCE-style evaluation using patient actors, which substantially under-represents the complexity… of real-world care,” they state clearly. 

Simulated scenarios, however well-designed, aren’t the same as dealing with the unique complexities of real patients in a busy clinic. They also stress that the chat interface doesn’t capture the richness of a real video or in-person consultation.

So, what’s the next step? Moving carefully towards the real world. Google is already partnering with Beth Israel Deaconess Medical Center for a research study to see how AMIE performs in actual clinical settings with patient consent.

The researchers also acknowledge the need to eventually move beyond text and static images towards handling real-time video and audio—the kind of interaction common in telehealth today.

Giving AI the ability to ‘see’ and interpret the kind of visual evidence doctors use every day offers a glimpse of how AI might one day assist clinicians and patients. However, the path from these promising findings to a safe and reliable tool for everyday healthcare is still a long one that requires careful navigation.

(Photo by Alexander Sinn)

See also: Are AI chatbots really changing the world of work?

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 co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The tool allows businesses to create customised AI agents through a conversational interface, making it easier to monitor and act on video data from physical settings without the need for technical expertise.

Designed for industries like manufacturing, logistics, retail, construction, and healthcare, Iris builds on Spot AI’s earlier launch of out-of-the-box Video AI Agents for safety, security, and operations. While those prebuilt agents focus on common use cases, Iris gives organisations the flexibility to train agents for more specific, business-critical scenarios.

According to Spot AI, users can build video agents in a matter of minutes. The system allows training through reinforcement—using examples of what the AI should and shouldn’t detect—and can be configured to trigger real-world responses like shutting down equipment, locking doors, or generating alerts.

CEO and Co-Founder Rish Gupta said the tool dramatically shortens the time required to create specialised video detection systems.

“What used to take months of development now happens in minutes,” Gupta explained. Before Iris, creating specialised video detection required dedicated AI/ML teams with advanced degrees, thousands of annotated images, and 8 weeks of complex development,” he explained. “Iris puts that same power in the hands of any business leader through simple conversation with 8 minutes and 20 training images.”

Examples from real-world settings

Spot AI highlighted a variety of industry-specific use cases that Iris could support:

• Manufacturing: Detecting product backups or fluid leaks, with automatic responses based on severity.
• Warehousing: Spotting unsafe stacking of boxes or pallets to prevent accidents.
• Retail: Monitoring shelf stock levels and generating alerts for restocking.
• Healthcare: Distinguishing between staff and patients wearing similar uniforms to optimise traffic flow and safety.
• Security: Identifying tools like bolt cutters in parking areas to address evolving security threats.
• Safety compliance: Verifying whether workers are wearing required safety gear on-site.

Video AI agents continuously monitor critical areas and help teams respond quickly to safety hazards, operational inefficiencies, and security issues. With Iris, those agents can be developed and modified through natural language interaction, reducing the need for engineering support and making video insights more accessible across departments.

Looking ahead

Iris is part of Spot AI’s broader effort to make video data more actionable in physical environments. The company plans to discuss the tool and its capabilities at Google Cloud Next, where Rish Gupta is scheduled to speak during a media roundtable on April 9.

(Image by Spot AI)

See also: ChatGPT hits record usage after viral Ghibli feature—Here are four risks to know first

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 co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Designed to integrate seamlessly with existing clinical workflows, Nova-3 Medical aims to address the growing need for accurate and efficient transcription in the UK’s public NHS and private healthcare landscape.

As electronic health records (EHRs), telemedicine, and digital health platforms become increasingly prevalent, the demand for reliable AI-powered transcription has never been higher. However, traditional speech-to-text models often struggle with the complex and specialised vocabulary used in clinical settings, leading to errors and “hallucinations” that can compromise patient care.

Deepgram’s Nova-3 Medical is engineered to overcome these challenges. The model leverages advanced machine learning and specialised medical vocabulary training to accurately capture medical terms, acronyms, and clinical jargon—even in challenging audio conditions. This is particularly crucial in environments where healthcare professionals may move away from recording devices.

“Nova‑3 Medical represents a significant leap forward in our commitment to transforming clinical documentation through AI,” said Scott Stephenson, CEO of Deepgram. “By addressing the nuances of clinical language and offering unprecedented customisation, we are empowering developers to build products that improve patient care and operational efficiency.”

One of the key features of the model is its ability to deliver structured transcriptions that integrate seamlessly with clinical workflows and EHR systems, ensuring vital patient data is accurately organised and readily accessible. The model also offers flexible, self-service customisation, including Keyterm Prompting for up to 100 key terms, allowing developers to tailor the solution to the unique needs of various medical specialties.

Versatile deployment options – including on-premises and Virtual Private Cloud (VPC) configurations – ensure enterprise-grade security and HIPAA compliance, which is crucial for meeting UK data protection regulations.

“Speech-to-text for enterprise use cases is not trivial, and there is a fundamental difference between voice AI platforms designed for enterprise use cases vs entertainment use cases,” said Kevin Fredrick, Managing Partner at OneReach.ai. “Deepgram’s Nova-3 model and Nova-3-Medical model, are leading voice AI offerings, including TTS, in terms of the accuracy, latency, efficiency, and scalability required for enterprise use cases.”

Benchmarking Nova-3 Medical: Accuracy, speed, and efficiency

Deepgram has conducted benchmarking to demonstrate the performance of Nova-3 Medical. The model claims to deliver industry-leading transcription accuracy, optimising both overall word recognition and critical medical term accuracy.

• Word Error Rate (WER): With a median WER of 3.45%, Nova-3 Medical outperforms competitors, achieving a 63.6% reduction in errors compared to the next best competitor. This enhanced precision minimises manual corrections and streamlines workflows.
• Keyword Error Rate (KER): Crucially, Nova-3 Medical achieves a KER of 6.79%, marking a 40.35% reduction in errors compared to the next best competitor. This ensures that critical medical terms – such as drug names and conditions – are accurately transcribed, reducing the risk of miscommunication and patient safety issues.

In addition to accuracy, Nova-3 Medical excels in real-time applications. The model transcribes speech 5-40x faster than many alternative speech recognition vendors, making it ideal for telemedicine and digital health platforms. Its scalable architecture ensures high performance even as transcription volumes increase.

Furthermore, Nova-3 Medical is designed to be cost-effective. Starting at $0.0077 per minute of streaming audio – which Deepgram claims is more than twice as affordable as leading cloud providers – it allows healthcare tech companies to reinvest in innovation and accelerate product development.

Deepgram’s Nova-3 Medical aims to empower developers to build transformative medical transcription applications, driving exceptional outcomes across healthcare.

(Photo by Alexander Sinn)

See also: Autoscience Carl: The first AI scientist writing peer-reviewed papers

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 co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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ARI (Advanced Research & Insights) delivers comprehensive, accurate, and interactive reports within minutes—potentially shaking up the $250 billion management consulting industry.  

You.com claims ARI completes reports that typically require weeks of labour and cost thousands of dollars in just five minutes, at a fraction of traditional expenses.

With the ability to process over 400 sources simultaneously – a figure set to grow as the technology scales – ARI promises to deliver “verified citations and insights 3X faster than other currently available solutions.”

Bryan McCann, Co-Founder and CTO of You.com, said: “ARI’s breakthrough is its ability to maintain contextual understanding while processing hundreds of sources simultaneously.

“When combined with chain-of-thought reasoning and extended test-time compute, ARI is able to discover and incorporate adjacent research areas dynamically as analysis progresses.”

A powerful AI agent for business research

Traditional AI research tools are typically limited to processing between 30 to 40 data sources at a time. ARI stands out by handling hundreds of public and private data streams, ensuring unparalleled accuracy and scope in its analysis. The system doesn’t just stop at summarising data; it enhances user experience by producing rich, interactive graphs, charts, and visualisations for deeper insights.  

Designed to cater equally to high-level professionals and knowledge workers across industries, ARI combines advanced functionality with user-friendly accessibility. This dual-purpose design allows enterprises to deploy it as a personal assistant or as a replacement for expensive research efforts traditionally carried out by consulting firms.

At the heart of ARI is a series of capabilities:

• Simultaneous source analysis: Processes hundreds of data sources, both public and private.  
• Chain-of-Thought reasoning: Dynamically evolves research parameters as insights emerge.  
• Real-time verification: Provides direct validation for every claim and data point.  
• Interactive visualisation engine: Automatically generates and cites graphs and charts to enhance reporting.  
• Enterprise data integration: Analyses a mix of public and private datasets to deliver actionable insights.  

During its initial deployment phase, ARI has demonstrated its versatility and potential for impact across several industries:

• Consulting: By analysing market reports, competitor financials, patent filings, and social sentiment data in hours rather than weeks, ARI supports due diligence with ease.  
• Financial services: With the ability to integrate real-time data from earnings calls, SEC filings, and industry news, ARI helps support faster and more accurate investment decisions.  
• Healthcare: ARI accelerates the synthesis of clinical trials, medical journals, patient data, and treatment guidelines, providing insights that support evidence-based care.  
• Media: From audience data to trending topics and competitor activity, ARI enables the rapid identification of new story angles and anticipates emerging narratives in key markets.  

Dr Dennis Ballwieser, Managing Director and Editor at Wort & Bild Verlag, commented: “The research time has dropped from a few days to just a few hours, and the accuracy across both German and English content has been remarkable.

“What excites me most is the opportunity to democratise access to professional-grade research. With ARI’s ability to analyse hundreds of verifiable sources simultaneously while maintaining accuracy, we can now offer professional insights to organisations of all sizes at a fraction of the traditional cost.”  

Accelerating access to strategic insights  

The potential for technologies like ARI goes beyond time and cost savings. For companies such as global consultancy firm APCO Worldwide, ARI’s capabilities provide a level of quality and personalisation that aligns with the modern needs of clients.

Philip Fraser, CIO at APCO Worldwide, said: “To us, ARI represents a step-change in the quality and alignment to the needs of our clients. We are very excited about working with You.com to integrate the power of ARI into our award-winning, proprietary Margy AI platform.”  

Through such integrations, ARI has the potential to move organisations away from periodic, resource-intensive research projects towards continuous real-time intelligence that drives better decision-making across all levels.  

Richard Socher, Co-Founder and CEO of You.com, added: “When every employee has instant access to comprehensive, validated insights that previously required teams of consultants and weeks of work, it changes the speed and quality of business decision-making. ARI represents a paradigm shift in how organisations operate.”

ARI is the newest addition to You.com’s expanding AI agent ecosystem, which has already seen the development of over 50,000 custom agents since late 2024. The company has raised $99 million in funding from investors such as Salesforce Ventures, NVIDIA, and Georgian Ventures.

With ARI, You.com aims to set a new standard for an enterprise-grade AI research agent as part of broader decision-making systems.

(Photo by Jeremy Beadle)

See also: Endor Labs: AI transparency vs ‘open-washing’

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 co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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Against the backdrop of rapid technological advancements and growing societal concerns, the summit aims to build on the progress made since the 2024 Seoul Safety Summit and establish a cohesive global framework for AI governance.  

AI Action Summit is ‘a wake-up call’

French President Emmanuel Macron has described the summit as “a wake-up call for Europe,” emphasising the need for collective action in the face of AI’s transformative potential. This comes as the US has committed $500 billion to AI infrastructure.

The UK, meanwhile, has unveiled its Opportunities Action Plan ahead of the full implementation of the UK AI Act. Ahead of the AI Summit, UK tech minister Peter Kyle told The Guardian the AI race must be led by “western, liberal, democratic” countries.

These developments signal a renewed global dedication to harnessing AI’s capabilities while addressing its risks.  

Matt Cloke, CTO at Endava, highlighted the importance of bridging the gap between AI’s potential and its practical implementation.

“Much of the conversation is set to focus on understanding the risks involved with using AI while helping to guide decision-making in an ever-evolving landscape,” he said.  

Cloke also stressed the role of organisations in ensuring AI adoption goes beyond regulatory frameworks.

“Modernising core systems enables organisations to better harness AI while ensuring regulatory compliance,” he explained.

“With improved data management, automation, and integration capabilities, these systems make it easier for organisations to stay agile and quickly adapt to impending regulatory changes.”  

Governance and workforce among critical AI Action Summit topics

Kit Cox, CTO and Founder of Enate, outlined three critical areas for the summit’s agenda.

“First, AI governance needs urgent clarity,” he said. “We must establish global guidelines to ensure AI is safe, ethical, and aligned across nations. A disconnected approach won’t work; we need unity to build trust and drive long-term progress.”

Cox also emphasised the need for a future-ready workforce.

“Employers and governments must invest in upskilling the workforce for an AI-driven world,” he said. “This isn’t just about automation replacing jobs; it’s about creating opportunities through education and training that genuinely prepare people for the future of work.”  

Finally, Cox called for democratising AI’s benefits.

“AI must be fair and democratic both now and in the future,” he said. “The benefits can’t be limited to a select few. We must ensure that AI’s power reaches beyond Silicon Valley to all corners of the globe, creating opportunities for everyone to thrive.”  

Developing AI in the public interest

Professor Gina Neff, Professor of Responsible AI at Queen Mary University of London and Executive Director at Cambridge University’s Minderoo Centre for Technology & Democracy, stressed the importance of making AI relatable to everyday life.

“For us in civil society, it’s essential that we bring imaginaries about AI into the everyday,” she said. “From the barista who makes your morning latte to the mechanic fixing your car, they all have to understand how AI impacts them and, crucially, why AI is a human issue.”  

Neff also pushed back against big tech’s dominance in AI development.

“I’ll be taking this spirit of public interest into the Summit and pushing back against big tech’s push for hyperscaling. Thinking about AI as something we’re building together – like we do our cities and local communities – puts us all in a better place.”

Addressing bias and building equitable AI

Professor David Leslie, Professor of Ethics, Technology, and Society at Queen Mary University of London, highlighted the unresolved challenges of bias and diversity in AI systems.

“Over a year after the first AI Safety Summit at Bletchley Park, only incremental progress has been made to address the many problems of cultural bias and toxic and imbalanced training data that have characterised the development and use of Silicon Valley-led frontier AI systems,” he said.

Leslie called for a renewed focus on public interest AI.

“The French AI Action Summit promises to refocus the conversation on AI governance to tackle these and other areas of immediate risk and harm,” he explained. “A main focus will be to think about how to advance public interest AI for all through mission-driven and society-led funding.”  

He proposed the creation of a public interest AI foundation, supported by governments, companies, and philanthropic organisations.

“This type of initiative will have to address issues of algorithmic and data biases head on, at concrete and practice-based levels,” he said. “Only then can it stay true to the goal of making AI technologies – and the infrastructures upon which they depend – accessible global public goods.”  

Systematic evaluation  

Professor Maria Liakata, Professor of Natural Language Processing at Queen Mary University of London, emphasised the need for rigorous evaluation of AI systems.

“AI has the potential to make public service more efficient and accessible,” she said. “But at the moment, we are not evaluating AI systems properly. Regulators are currently on the back foot with evaluation, and developers have no systematic way of offering the evidence regulators need.”  

Liakata called for a flexible and systematic approach to AI evaluation.

“We must remain agile and listen to the voices of all stakeholders,” she said. “This would give us the evidence we need to develop AI regulation and help us get there faster. It would also help us get better at anticipating the risks posed by AI.”  

AI in healthcare: Balancing innovation and ethics

Dr Vivek Singh, Lecturer in Digital Pathology at Barts Cancer Institute, Queen Mary University of London, highlighted the ethical implications of AI in healthcare.

“The Paris AI Action Summit represents a critical opportunity for global collaboration on AI governance and innovation,” he said. “I hope to see actionable commitments that balance ethical considerations with the rapid advancement of AI technologies, ensuring they benefit society as a whole.”  

Singh called for clear frameworks for international cooperation.

“A key outcome would be the establishment of clear frameworks for international cooperation, fostering trust and accountability in AI development and deployment,” he said.  

AI Action Summit: A pivotal moment

The 2025 AI Action Summit in Paris represents a pivotal moment for global AI governance. With calls for unity, equity, and public interest at the forefront, the summit aims to address the challenges of bias, regulation, and workforce readiness while ensuring AI’s benefits are shared equitably.

As world leaders and industry experts converge, the hope is that actionable commitments will pave the way for a more inclusive and ethical AI future.

(Photo by Jorge Gascón)

See also: EU AI Act: What businesses need to know as regulations go live

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 co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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Taxpayer-funded services in the UK, from the NHS to local councils, are losing out on potential productivity savings of £45 billion per year due to an overwhelming reliance on outdated technology—a figure equivalent to the total cost of running every primary school in the country for a year.   

A report published this week highlights how nearly half of public services are still not accessible online. This forces British citizens to engage in time-consuming and frustrating processes such as applying for support in person, enduring long wait times on hold, or travelling across towns to council offices. Public sector workers are similarly hindered by inefficiencies, such as sifting through mountains of physical letters, which slows down response times and leaves citizens to bear the brunt of government red tape.

“As this report has shown, there is clearly a gap between what the government and public bodies intend to achieve with their digital projects and what they actually deliver,” explained Malaguti. “The public sector still relies heavily upon legacy systems and has clearly struggled to tackle existing poor data structures and inefficiencies across key departments. No doubt this has had a clear impact on decision-making and hindered vital services for vulnerable citizens.”

The struggles persist even in deeply personal and critical scenarios. For example, the current process for registering a death still demands a physical presence, requiring grieving individuals to manage cumbersome bureaucracy while mourning the loss of a loved one. Other outdated processes unnecessarily burden small businesses—one striking example being the need to publish notices in local newspapers simply to purchase a lorry licence, creating further delays and hindering economic growth.

A lack of coordination between departments amplifies these challenges. In some cases, government bodies are using over 500 paper-based processes, leaving systems fragmented and inefficient. Vulnerable individuals suffer disproportionately under this disjointed framework. For instance, patients with long-term health conditions can be forced into interactions with up to 40 different services, repeating the same information as departments repeatedly fail to share data.

“The challenge is that government leaders have previously focused on technology and online interactions, adding layers to services whilst still relying on old data and legacy systems—this has ultimately led to inefficiencies across departments,” added Malaguti.

“Put simply, they have failed to address existing issues or streamline their day-to-day operations. It is critical that data is more readily available and easily shared between departments, particularly if leaders are hoping to employ new technology like AI to analyse this data and drive better outcomes or make strategic decisions for the public sector as a whole.”

Ageing Infrastructure: High costs and security risks

The report underscores that ageing infrastructure comes at a steep financial and operational cost. More than one-in-four digital systems used across the UK’s central government are outdated, with this figure ballooning to 70 percent in some departments. Maintenance costs for legacy systems are significantly higher, up to three-to-four times more, compared to keeping technology up-to-date.  

Furthermore, a growing number of these outdated systems are now classified as “red-rated” for reliability and cybersecurity risk. Alarmingly, NHS England experienced 123 critical service outages last year alone. These outages often meant missed appointments and forced healthcare workers to resort to paper-based systems, making it harder for patients to access care when they needed it most.

Malaguti stresses that addressing such challenges goes beyond merely upgrading technology.

“The focus should be on improving data structure, quality, and timeliness. All systems, data, and workflows must be properly structured and fully optimised prior to implementation for these technologies to be effective. Public sector leaders should look to establish clear measurable objectives, as they continue to improve service delivery and core mission impacts.”

Transforming public services

In response to these challenges, Technology Secretary Peter Kyle is announcing an ambitious overhaul of public sector technology to usher in a more modern, efficient, and accessible system. Emphasising the use of AI, digital tools, and “common sense,” the goal is to reform how public services are designed and delivered—streamlining operations across local government, the NHS, and other critical departments.

A package of tools known as ‘Humphrey’ – named after the fictional Whitehall official in popular BBC drama ‘Yes, Minister’ – is set to be made available to all civil servants soon, with some available today.

Humphrey includes:

• Consult: Analyses the thousands of responses received during government consultations within hours, presenting policymakers and experts with interactive dashboards to directly explore public feedback.
• Parlex: A tool that enables policymakers to search and analyze decades of parliamentary debate, helping them refine their thinking and manage bills more effectively through both the Commons and the Lords.
• Minute: A secure AI transcription service that creates customisable meeting summaries in the formats needed by public servants. It is currently being used by multiple central departments in meetings with ministers and is undergoing trials with local councils.
• Redbox: A generative AI tool tailored to assist civil servants with everyday tasks, such as summarising policies and preparing briefings.
• Lex: A tool designed to support officials in researching the law by providing analysis and summaries of relevant legislation for specific, complex issues.

The new tools and changes will help to tackle the inefficiencies highlighted in the report while delivering long-term cost savings. By reducing the burden of administrative tasks, the reforms aim to enable public servants, such as doctors and nurses, to spend more time helping the people they serve. For businesses, this could mean faster approvals for essential licences and permits, boosting economic growth and innovation.

“The government’s upcoming reforms and policy updates, where it is expected to deliver on its ‘AI Opportunities Action Plan,’ [will no doubt aim] to speed up processes,” said Malaguti. “Public sector leaders need to be more strategic with their investments and approach these projects with a level head, rolling out a programme in a phased manner, considering each phase of their operations.”

This sweeping transformation will also benefit from an expanded role for the Government Digital Service (GDS). Planned measures include using the GDS to identify cybersecurity vulnerabilities in public sector systems that could be exploited by hackers, enabling services to be made more robust and secure. Such reforms are critical to protect citizens, particularly as the reliance on digital solutions increases.

The broader aim of these reforms is to modernise the UK’s public services to reflect the convenience and efficiencies demanded in a digital-first world. By using technologies like AI, the government hopes to make interactions with public services faster and more intuitive while saving billions for taxpayers in the long run.

As technology reshapes the future of how services are delivered, leaders must ensure they are comprehensively addressing the root causes of inefficiency—primarily old data infrastructure and fragmented workflows. Only then can technological solutions, whether AI or otherwise, achieve their full potential in helping services deliver for the public.

(Photo by Claudio Schwarz)

See also: Biden’s executive order targets energy needs for AI data centres

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 co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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The government has committed to all 50 recommendations outlined in the ambitious AI Opportunities Action Plan created by Matt Clifford CBE, tech entrepreneur and chair of the Advanced Research and Invention Agency.

“Our plan will make Britain the world leader,” declared Starmer. “That means more jobs and investment in the UK, more money in people’s pockets, and transformed public services. That’s the change this government is delivering.”

The plan positions AI as a key driver of national progress, aiming to revolutionise public services, boost productivity, and establish the UK as a global leader in the field.

Chris Lehane, Chief Global Affairs Officer at OpenAI, said: “From the locomotive to the Colossus computer, the UK has a rich history of leadership in tech innovation and the research and development of AI.

“The government’s AI action plan – led by the Prime Minister and Secretary Peter Kyle – recognises where AI development is headed and sets the UK on the right path to benefit from its growth. The UK has an enormous national resource in the talent of its people, institutions, and businesses which together can leverage AI to advance the country’s national interest.”

A plan to unlock economic potential

The economic benefits of AI adoption form a central tenet of the government’s strategy. The International Monetary Fund estimates that embracing AI could increase productivity by 1.5 percentage points annually. Over a decade, this could add up to £47 billion in economic gains per year, according to Treasury calculations.

The strategy aims not only to grow the economy but to create tangible benefits for citizens.

Dr Jean Innes, CEO of the Alan Turing Institute, said: “This plan offers an exciting route map, and we welcome its focus on adoption of safe and responsible AI, AI skills, and an ambition to sustain the UK’s global leadership, putting AI to work driving growth, and delivering benefits for society.

“We share these ambitions and look forward to working with the government, universities, industry and civil society to shape the future of these technologies to support the UK’s success and improve people’s lives.”

Three major companies – Vantage Data Centres, Nscale, and Kyndryl – have already pledged a combined £14 billion in investments, creating 13,250 jobs across the country and reinforcing confidence in the UK’s AI potential. This adds to the £25 billion in commitments secured at last year’s International Investment Summit.

Vantage Data Centres is set to invest over £12 billion in UK data centre projects, including one of Europe’s largest data centre campuses in Wales. Meanwhile, Kyndryl will establish a new tech hub in Liverpool—creating up to 1,000 AI-related jobs. Nscale plans to back UK data centre infrastructure with a $2.5 billion investment, which includes the construction of the nation’s largest sovereign AI data centre in Essex by 2026.

Alison Kay, VP for UK and Ireland at Amazon Web Services (AWS), said: “At AWS, we’ve seen first-hand the benefits that digital technologies like AI can bring, and that’s why we’ve announced plans to invest £8 billion over the next five years building, operating, and maintaining data centres in the UK.

“By putting close industry collaboration and public-private partnership at the heart of the government’s agenda, every citizen, community and business in every region of the UK will have the opportunity to realise the benefits of AI, and thrive and grow.”

Zahra Bahrololoumi CBE, CEO of Salesforce UK and Ireland, added: “Businesses in the UK are in a prime position to fully unlock the opportunities of AI, thanks to a strong innovation culture and risk-based laws. That is why Salesforce chose the UK as the location of its first-ever AI Centre, building on our commitment to invest $4 billion in our UK operations to harness the transformational power of AI.”

Transforming public services  

AI is already being deployed within UK hospitals, helping to modernise the NHS by diagnosing conditions such as breast cancer more swiftly, improving care for non-verbal patients, and expediting patient discharges.

“NHS datasets could be invaluable for impactful AI innovations in healthcare,” said Dr Bilal, Professor of Applied AI and Technology Ethics at Birmingham City University. “But they remain largely inaccessible to many researchers due to prohibitive costs and logistical hurdles.”

“Initiatives like NHS Secure Data Environments are a great start but must be made more affordable, or ideally free, for academic institutions.

Beyond healthcare, the government is betting that broader adoption of AI could achieve similar efficiencies in other public sectors. For instance, the plan highlights its potential to reduce administrative workloads for teachers, speed up planning consultations to facilitate new housing developments, and even identify potholes on roads via AI-enhanced cameras to expedite repairs.

The new strategy introduces AI Growth Zones, designed to accelerate the development of critical infrastructure. These zones will not only expedite planning permissions but also ensure dedicated energy connections to power AI projects. The first such zone will be established in Culham, Oxfordshire.  

Building AI infrastructure  

Supporting the UK’s AI aspirations requires significant investment in infrastructure. The plan includes:  

• A twentyfold increase in public compute capacity, with immediate work commencing on a new supercomputer to support AI advancements.  
• The creation of a National Data Library, designed to safely unlock the potential of public data to fuel AI innovation.  
• The establishment of a dedicated AI Energy Council, chaired by the Science and Energy Secretaries, to address the energy demands of AI development. This aligns with the government’s goal of becoming a clean energy superpower.  
• A new government team tasked with developing the UK’s sovereign AI capabilities.  

The government also aims to provide stability for businesses by balancing the dynamism of the US and the regulatory guardrails seen in the EU.

Science, Innovation, and Technology Secretary Peter Kyle said the UK would leverage its unique strengths: “We already have remarkable strengths we can tap into when it comes to AI—building our status as the cradle of computer science and intelligent machines and establishing ourselves as the third largest AI market in the world.”

Reservations and risks with the AI action plan

While the ambitious plan has been met with enthusiasm by many, industry experts caution against overlooking the potential risks posed by unregulated AI deployment.

Dr Pia Hüsch, Research Fellow in Cyber, Technology and National Security at RUSI, commented: “Labour’s AI Opportunities Action Plan has economic growth as the top priority, shifting focus away from the previous government’s priorities around AI safety and regulation.

“While the focus on investing in infrastructure such as computing power and a national data library is welcome, the UK Government must not forget risks posed by AI technologies or the international partnerships that are needed to secure long-term benefit from AI technologies.”

Similarly, Deryck Mitchelson, Global Chief Information Security Officer at Check Point Software, expressed concerns about security and ethics: “AI integration without airtight security measures will only amplify these risks. External oversight of AI models and training datasets is essential—not optional.

“We need built-in equality, ethics, and a transparent framework to measure outcomes and prove these systems genuinely enhance performance, not just cut costs.”  

Mitchelson warned that hasty deployment could erode public trust in AI-driven services and deepen inequality. He emphasised that the government must present this initiative as more than a cost-saving strategy and instead prioritise transparency, accountability, and robust safeguards.  

The AI Opportunities Action Plan is a key pillar of the government’s Industrial Strategy and the first stage of a forthcoming Digital and Technology Sector Plan. It also ties into the government’s broader Plan for Change, aiming to ensure AI’s benefits are felt across every corner of the UK.

Chancellor Rachel Reeves MP underscored the initiative’s dual focus on economic growth and public service transformation: “AI is a powerful tool that will help grow our economy, make our public services more efficient and open up new opportunities to help improve living standards.

“This action plan is the government’s modern industrial strategy in action.”  

As the UK accelerates efforts to harness AI, it faces the dual challenge of staying ahead in the global race for innovation while ensuring the potential pitfalls of the technology are minimised. Today’s announcements mark a bold step forward, but the road ahead will require careful navigation.

See also: Sam Altman, OpenAI: ‘Lucky and humbling’ to work towards superintelligence

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AI Airlock aims to refine the process of regulating AI-driven medical devices and help fast-track their safe introduction to the UK’s National Health Service (NHS) and patients in need.

The technologies chosen for this scheme include solutions targeting cancer and chronic respiratory diseases, as well as advancements in radiology diagnostics. These AI systems promise to revolutionise the accuracy and efficiency of healthcare, potentially driving better diagnostic tools and patient care.

The AI Airlock, as described by the MHRA, is a “sandbox” environment—an experimental framework designed to help manufacturers determine how best to collect real-world evidence to support the regulatory approval of their devices.

Unlike traditional medical devices, AI models continue to evolve through learning, making the establishment of safety and efficacy evidence more complex. The Airlock enables this exploration within a monitored virtual setting, giving developers insight into the practical challenges of regulation while supporting the NHS’s broader adoption of transformative AI technologies.

Safely enabling AI healthcare innovation  

Laura Squire, the lead figure in MedTech regulatory reform and Chief Officer at the MHRA, said: “New AI medical devices have the potential to increase the accuracy of healthcare decisions, save time, and improve efficiency—leading to better outcomes for the NHS and patients across all healthcare settings. 

“But we need to be confident that AI-powered medical devices introduced into the NHS are safe, stay safe, and perform as intended through their lifetime of use.”

Squire emphasised that the AI Airlock pilot allows collaboration “in partnership with technology specialists, developers and the NHS,” facilitating the exploration of best practices and accelerating safe patient access to innovative solutions.

Government representatives have praised the initiative for its forward-thinking framework.

Karin Smyth, Minister of State for Health, commented: “As part of our 10-Year Health Plan, we’re shifting NHS care from analogue to digital, and this project will help bring the most promising technology to patients.

“AI has the power to revolutionise care by supporting doctors to diagnose diseases, automating time-consuming admin tasks, and reducing hospital admissions by predicting future ill health.”

Science Minister Lord Vallance lauded the AI Airlock pilot as “a great example of government working with businesses to enable them to turn ideas into products that improve lives.” He added, “This shows how good regulation can facilitate emerging technologies for the benefit of the UK and our economy.”

Selected technologies  

The deployment of AI-powered medical devices requires meeting stringent criteria to ensure innovation, patient benefits, and regulatory challenge readiness. The five technologies selected for this inaugural pilot offer vital insights into healthcare’s future: 

1. Lenus Stratify

Patients with Chronic Obstructive Pulmonary Disease (COPD) are among those who stand to benefit significantly from AI innovation. Lenus Stratify, developed by Lenus Health, analyses patient data to predict severe lung disease outcomes, reducing unscheduled hospital admissions. The system empowers care providers to adopt earlier interventions, affording patients an improved quality of life while alleviating NHS resource strain.  

2. Philips Radiology Reporting Enhancer

Philips has integrated AI into existing radiology workflows to enhance the efficiency and accuracy of critical radiology reports. This system uses AI to prepare the “Impression” section of reports, summarising essential diagnostic information for healthcare providers. By automating this process, Philips aims to minimise workload struggles, human errors, and miscommunication, creating a more seamless diagnostic experience.  

3. Federated AI Monitoring Service (FAMOS)

One recurring AI challenge is the concept of “drift,” when changing real-world conditions impair system performance over time. Newton’s Tree has developed FAMOS to monitor AI models in real time, flagging degradation and enabling rapid corrections. Hospitals, regulators, and software developers can use this tool to ensure algorithms remain high-performing, adapting to evolving circumstances while prioritising patient safety.  

4. OncoFlow Personalised Cancer Management

Targeting the pressing healthcare challenge of reducing waiting times for cancer treatment, OncoFlow speeds up clinical workflows through its intelligent care pathway platform. Initially applied to breast cancer protocols, the system later aims to expand across other oncology domains. With quicker access to tailored therapies, patients gain increased survival rates amidst mounting NHS pressures.  

5. SmartGuideline

Developed to simplify complex clinical decision-making processes, SmartGuideline uses large-language AI trained on official NICE medical guidelines. This technology allows clinicians to ask routine questions and receive verified, precise answers, eliminating the ambiguity associated with current AI language models. By integrating this tool, patients benefit from more accurate treatments grounded in up-to-date medical knowledge.  

Broader implications  

The influence of the AI Airlock extends beyond its current applications. The MHRA expects pilot findings, due in 2025, to inform future medical device regulations and create a clearer path for manufacturers developing AI-enabled technologies. 

The evidence derived will contribute to shaping post-Brexit UKCA marking processes, helping manufacturers achieve compliance with higher levels of transparency. By improving regulatory frameworks, the UK could position itself as a global hub for med-tech innovation while ensuring faster access to life-saving tools.

The urgency of these developments was underscored earlier this year in Lord Darzi’s review of health and care. The report outlined the “critical state” of the NHS, offering AI interventions as a promising pathway to sustainability. The work on AI Airlock by the MHRA addresses one of the report’s major recommendations for enabling regulatory solutions and “unlocking the AI revolution” for healthcare advancements.

While being selected into the AI Airlock pilot does not indicate regulatory approval, the technologies chosen represent a potential leap forward in applying AI to some of healthcare’s most pressing challenges. The coming years will test the potential of these solutions under regulatory scrutiny.

If successful, the initiative from the MHRA could redefine how pioneering technologies like AI are adopted in healthcare, balancing the need for speed, safety, and efficiency. With the NHS under immense pressure from growing demand, AI’s ability to augment clinicians, predict illnesses, and streamline workflows may well be the game-changer the system urgently needs.

(Photo by National Cancer Institute)

See also: AI’s role in helping to prevent skin cancer through behaviour change

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Skin cancer is no exception, and we expect AI diagnostic tools to be widely implemented across this clinical arena in the future. What does AI assistance look like for skin cancer? A 2024 study led by researchers at Stanford Medicine compared the performance of clinicians diagnosing at least one skin cancer with and without deep learning-based AI assistance. In an experimental environment, clinicians without AI assistance achieved an average sensitivity of 74.8% while for AI-assisted clinicians, sensitivity was around 81.1%.

What’s intriguing is AI helped medical professionals at all levels, with the largest improvement seen among non-dermatologists.

AI for skin cancer can impact behaviour change

Cancer is on the rise among younger people. According to a study published in BMJ Oncology, the number of under-50s worldwide being diagnosed with cancer has risen by nearly 80% in three decades. And, over the last decade melanoma skin cancer incidence rates have increased by almost two-fifths (38%) with Spain seeing a steady incidence increase of 2.4% during this time.

If detected early enough, skin cancer is easily treated and prognosis is very good. But busy lives and competing concerns mean fewer people are getting checked out, resulting in delays to diagnosis and treatment, which is dramatically changing the survival rates. Those who do, often wait to speak to a doctor. In fact, new research from Bupa, Attitudes Towards Digital Healthcare, indicates only 9% of people would immediately go to get a mole they were concerned about examined by a professional.

However, the same research found that if people were able to have a mole assessed by an AI-powered phone app at the time of their choosing, that percentage increases more than threefold (33%). This signifies emerging technology can have a significant impact on positive behaviour change in healthcare and improve clinical outcome of a potentially severe disease.

Bupa now offer an at-home dermatology tool

At Bupa, we see lots of opportunities to use AI and are exploring its use to enhance patient care, improve operational efficiency, and help our customers to live longer, healthier and happier lives. We know that people want their healthcare partner to be by their side, not just when they are sick, but supporting them constantly to keep them well.

That’s why we launched Blua, our digital healthcare service that’s available in over 200 countries. Blua provides access to three lifechanging healthcare innovations that drive convenience and accessibility. They are virtual consultations so that a customer can connect to a health professional from wherever they choose. Digital health programmesthat allow customers to proactively manage their health and remote healthcare services such as prescription delivery and at home monitoring equipment.  

For customers in Spain, we offer an at-home dermatology assessment service through Blua. How does this work? Customers who’re worried about a skin lesion can take high resolution photos of it using their smartphone. Once taken, the photos are uploaded to Blua and using AI are compared with a database of millions of other images of skin lesions to check for signs of malignancy.

The tool’s algorithms are able to discern between 302 different skin pathologies. If the tool suspects that there is a cause for concern it will let the customer know to book a follow up appointment with a doctor so that it can be looked at further and preventative action can be taken if needed.

The future of healthcare means early detection

Digital healthcare, together with AI, is going to play a crucial role in removing the barriers that stop people from getting health concerns like moles checked out in a timely manner, promoting positive behaviour change that can save lives. This is why Blua is especially useful in today’s fast-paced world where convenience is paramount and virtual consultations and at home tests will empower individuals to prioritise their health, without the need to sacrifice their time. 

(Photo by Nsey Benajah)

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AlphaProteo can generate new protein binders for diverse target proteins, including VEGF-A, which is associated with cancer and diabetes complications. Notably, this is the first time an AI tool has successfully designed a protein binder for VEGF-A.

The system’s performance is particularly impressive, achieving higher experimental success rates and binding affinities that are up to 300 times better than existing methods across seven target proteins tested:

Trained on vast amounts of protein data from the Protein Data Bank and over 100 million predicted structures from AlphaFold, AlphaProteo has learned the intricacies of molecular binding. Given the structure of a target molecule and preferred binding locations, the system generates a candidate protein designed to bind at those specific sites.

To validate AlphaProteo’s capabilities, the team designed binders for a diverse range of target proteins, including viral proteins involved in infection and proteins associated with cancer, inflammation, and autoimmune diseases. The results were promising, with high binding success rates and best-in-class binding strengths observed across the board.

For instance, when targeting the viral protein BHRF1, 88% of AlphaProteo’s candidate molecules bound successfully in wet lab testing. On average, AlphaProteo binders exhibited 10 times stronger binding than the best existing design methods across the targets tested.

The system’s performance suggests it could significantly reduce the time required for initial experiments involving protein binders across a wide range of applications. However, the team acknowledges that AlphaProteo has limitations, as it was unable to design successful binders against TNFɑ (a protein associated with autoimmune diseases like rheumatoid arthritis.)

To ensure responsible development, Google DeepMind is collaborating with external experts to inform their phased approach to sharing this work and contributing to community efforts in developing best practices—including the NTI’s new AI Bio Forum.

As the technology evolves, the team plans to work with the scientific community to leverage AlphaProteo on impactful biology problems and understand its limitations. They are also exploring drug design applications at Isomorphic Labs.

While AlphaProteo represents a significant step forward in protein design, achieving strong binding is typically just the first step in designing proteins for practical applications. There remain many bioengineering challenges to overcome in the research and development process.

Nevertheless, Google DeepMind’s advancement holds tremendous potential for accelerating progress across a broad spectrum of research, including drug development, cell and tissue imaging, disease understanding and diagnosis, and even crop resistance to pests.

You can find the full AlphaProteo whitepaper here (PDF)

See also: Paige and Microsoft unveil next-gen AI models for cancer diagnosis

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The Virchow2 and Virchow2G models are based on an enormous dataset that Paige has accumulated. Paige has gathered more than three million pathology slides from over 800 labs across 45 countries, on which the models were trained. Such a volume of data is, unsurprisingly, highly beneficial. This data was obtained from over 225,000 patients, all de-identified to create a rich and representative dataset encompassing all genders, races, ethnic groups, and regions across the globe.

What makes these models truly remarkable is their scope. They cover over 40 different tissue types and various staining methods, making them applicable to a wide range of cancer diagnoses. Virchow2G, with its 1.8 billion parameters, stands as the largest pathology model ever created and sets new standards in AI training, scale, and performance.

As Dr. Thomas Fuchs, founder and chief scientist of Paige, comments: “We’re just beginning to tap into what these foundation models can achieve in revolutionising our understanding of cancer through computational pathology.” He believes these models will significantly improve the future for pathologists, and he agrees that this technology is becoming an important step in the progression of diagnostics, targeted medications, and customised patient care.

Similarly, Razik Yousfi, Paige’s senior vice president of technology, states that these models are not only making precision medicine a reality but are also improving the accuracy and efficiency of cancer diagnosis, and pushing the boundaries of what’s possible in pathology and patient care.

So, how is this relevant to cancer diagnosis today? Paige has developed a clinical AI application that pathologists can use to recognise cancer in over 40 tissue types. This tool allows potentially hazardous areas to be identified more quickly and accurately. In other words, the diagnostic process becomes more efficient and less prone to errors, even for rare cancers, with the help of this tool.

Beyond diagnosis, Paige has created AI modules that can benefit life sciences and pharmaceutical companies. These tools can aid in therapeutic targeting, biomarker identification, and clinical trial design, potentially leading to more successful trials and faster development of new therapies.

The good news for researchers is that Virchow2 is available on Hugging Face for non-commercial research, while the entire suite of AI modules is now available for commercial use. This accessibility could accelerate advancements in cancer research and treatment across the scientific community.

In summary, the recently introduced AI models represent a major advancement in the fight against cancer. Paige and Microsoft have chosen the right path by combining the power of data with state-of-the-art AI technologies. These companies have created new opportunities for more accurate cancer prediction, paving the way for tailored solutions and innovative research in oncology.

(Photo by National Cancer Institute)

See also: The hidden climate cost of AI: How tech giants are struggling to go green

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The participating hospitals – North Bristol Trust Southmead Hospital, University Hospitals Coventry and Warwickshire, and Oxford University NHS Foundation Trust – are now incorporating Paige’s AI technology into their standard of care. This multisite trial aims to evaluate the potential of AI to improve patient outcomes against a backdrop of rising prostate cancer cases.

Professor Clare Verrill, OUH Cellular Pathology Consultant, Associate Professor and Principal Investigator of ARTICULATE PRO, said “The central focus of ARTICULATE PRO is patients. We are striving towards our goal to safely and effectively ensure they benefit the most from powerful AI technology.

“With the multisite live use of The Paige Prostate Suite, we can systematically study benefits to patients in clinical settings.”

The Prostate Suite – the AI system being trialled – is designed to assist pathologists in detecting, grading, and measuring tumours in prostate biopsies and tissue samples. Pathologists at the three hospitals are assessing how this AI technology impacts their clinical decision-making, pathology service delivery, and resource utilisation in real-world settings.

Dr Jon Oxley, Uropathologist and Bristol lead of ARTICULATE PRO, commented: “I have studied the disease and progression of prostate cancer in clinical research for over 25 years, it is a significant advancement that Paige’s AI applications have achieved a level of validation and performance that allows safe and effective live clinical use.

“Using Paige Prostate Suite alongside our standard of care has the promise to increase efficiency and improve reproducibility of results for patients.”

The study is notable for its implementation across hospitals using different digital pathology scanners and information systems, serving distinct patient populations. This diversity allows for a comprehensive assessment of how Paige’s AI technology can best serve patients, histopathologists, and hospital systems in prostate cancer diagnosis.

Dr Bidisa Sinha, Uropathologist at University Hospitals Coventry and Warwickshire, added: “We believe AI can help to improve the accuracy and consistency of grading cancer and assist in detection of small areas of cancer which are easy to miss.

“This is world-leading research being carried out at UHCW. We are proud to be a global leader in the field of digital and computational pathology.”

The ARTICULATE PRO study is funded by the Accelerated Access Collaborative (AAC) Artificial Intelligence in Health and Care Award, overseen by the Department of Health and Social Care.

As prostate cancer rates continue to rise, the integration of AI in diagnosis could potentially lead to earlier detection, more accurate grading, and ultimately improved patient outcomes. The results of this trial could pave the way for wider adoption of AI in cancer diagnosis across the UK and beyond.

(Image Credit: Paige)

See also: AI could unleash £119 billion in UK productivity

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