Interdisciplinary approaches

Google AI advances beyond diagnosis into patient care

AMIE, a medical AI system designed for clinical reasoning, is being extended from diagnostic support into long-term disease management, according to new research published in Nature. The system uses advanced long-context AI models to interpret clinical guidelines, drug formularies and patient data across extended treatment periods.

Built on Google’s Gemini models, AMIE combines a conversational interface with a reasoning engine designed to cross-reference large volumes of clinical and medical knowledge. The approach enables continuous patient interaction alongside structured clinical decision support, particularly for chronic condition management.

In a blinded study involving simulated patient interactions, AMIE was evaluated against 21 primary care physicians and achieved performance comparable to clinicians in overall management reasoning. The system also achieved higher scores in treatment precision and adherence to clinical guidelines, suggesting potential for AI-assisted care models that support clinicians in ongoing decision-making.

Researchers plan to further assess AMIE in real-world healthcare environments, including nationwide studies focused on virtual care settings. Early findings suggest that AI tools could reduce administrative burdens and support clinical workflows, potentially allowing physicians to devote more time to direct patient care.

Why does it matter?

The research reflects a significant shift in healthcare AI, from supporting individual diagnostic decisions to assisting with the long-term management of chronic conditions. If such systems prove effective in real-world settings, they could help improve treatment consistency, support evidence-based care and reduce administrative workloads for healthcare professionals.

At the same time, expanding AI into ongoing patient management raises important questions about accountability, safety and oversight. Healthcare providers and regulators will need to determine how AI-generated recommendations are validated, how responsibility is assigned when errors occur and how patient trust can be maintained as AI becomes more deeply integrated into clinical care. The study therefore highlights both the potential and the governance challenges of the next generation of medical AI systems.

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US backs photonics expansion for AI data centres under CHIPS Act

The Department of Commerce’s CHIPS Program Office has signed a letter of intent to provide up to $50 million in direct funding to Coherent Corp. under the CHIPS and Science Act.

According to the CHIPS Program Office, the proposed funding would support the expansion of Coherent’s facility in Sherman, Texas, which it describes as the first and largest high-volume 150mm indium phosphide semiconductor manufacturing facility in the United States.

The expansion would add wafer fabrication equipment and cleanroom capacity to increase production of indium phosphide-based photonic components. These components are used in high-speed optical interconnects that enable rapid data transfer within advanced AI data centres.

The Department of Commerce said the project would create high-skilled manufacturing jobs and strengthen domestic supply chains for critical photonics technologies that support next-generation computing and AI infrastructure.

Why does it matter?

The announcement highlights the growing importance of photonics technologies in the AI economy. As demand for AI computing continues to rise, data centres require increasingly efficient methods for transferring vast amounts of information between processors, servers and storage systems. Optical interconnect technologies based on indium phosphide semiconductors are becoming a critical part of that infrastructure.

The investment also reflects broader US industrial policy goals under the CHIPS and Science Act. Beyond traditional semiconductor manufacturing, policymakers are increasingly targeting specialised components and supply chains considered strategically important for AI competitiveness, economic security and technological resilience.

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UK unveils AI tools to speed up planning decisions and housing delivery

The Department for Science, Innovation and Technology (DSIT) and the Ministry of Housing, Communities and Local Government have unveiled two AI tools designed to modernise England’s planning system and accelerate housing delivery.

One new AI prototype is being tested by Barnet, Camden and Dorset councils and aims to reduce average decision times for routine householder planning applications from eight weeks to four. The system triages applications and provides planning officers with preliminary assessments to support decision-making.

A second tool, called Extract, has been made available to local authorities across England. It uses AI to convert decades of planning documents and maps into structured digital data, reducing the need for manual processing and allowing staff to focus on more complex cases.

The government said the initiatives support its target of building 1.5 million homes during this Parliament while improving the efficiency of public services through technology. Subject to successful trials, the new planning application tool is expected to be rolled out nationally in England from 2027.

Why does it matter?

The initiative illustrates how governments are increasingly using AI to address administrative bottlenecks and improve public-service delivery. Planning systems often face challenges related to outdated records, resource constraints and lengthy approval processes, making them a key target for digital transformation efforts.

The UK’s approach also highlights the growing role of AI in housing and infrastructure policy. If successful, the tools could help accelerate housing development, improve the use of public-sector resources and demonstrate how AI can support decision-making while leaving final judgments in the hands of public officials.

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New benchmark tests AI on unpublished mathematics problems

AI systems have demonstrated growing capabilities in advanced mathematics, according to benchmark results published by the non-profit organisation First Proof.

The organisation evaluated four frontier AI systems, including ChatGPT 5.5 Pro, against ten unpublished research-level mathematical problems contributed by leading mathematicians.

The benchmark found that seven of the ten problems received at least one solution judged to be correct by expert reviewers across the participating systems. One notable result involved a stochastic partial differential equations problem, where an AI system produced a correct solution using an approach different from the human-developed proof, drawing praise from expert referees for its originality.

Despite the progress, significant limitations remain.

Several problems remained unsolved, including a metric geometry challenge on which none of the systems made meaningful progress. Reviewers also reported that AI systems handled routine mathematical reasoning effectively but continued to struggle with the most challenging conceptual and creative aspects of proof construction.

Why does it matter?

The benchmark offers one of the most demanding independent tests of AI performance in advanced mathematics, a field often viewed as a proxy for higher-level reasoning and scientific problem-solving. The results suggest that frontier AI systems are increasingly capable of contributing to specialised research tasks and, in some cases, generating approaches that differ from those developed by human experts.

At the same time, the findings highlight the limits of current AI systems. While they can assist with complex reasoning and formal problem-solving, they continue to struggle with the deepest conceptual challenges that often drive mathematical breakthroughs. This suggests that AI may increasingly serve as a research assistant and discovery tool, while human expertise remains essential for guiding and validating scientific advances.

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European Commission opens applications for RAISE AI research advisory board

The European Commission has opened applications for the RAISE High-Level Academic Advisory Board, inviting leading researchers in AI and AI-enabled science to help shape Europe’s future AI research agenda.

The advisory board will support the implementation of the EU’s AI in Science Strategy and provide independent scientific guidance on the development of RAISE (Resource for AI Science in Europe).

RAISE was launched in 2025 under Horizon Europe to strengthen European leadership in both fundamental AI research and the application of AI across scientific disciplines.

The Commission is seeking academics with expertise in AI research or experience applying AI in fields such as medicine, climate science and advanced materials. Board members will provide strategic recommendations on research priorities, governance structures, benchmarks and framework conditions needed to accelerate AI-enabled scientific discovery.

Through RAISE, the EU aims to bring together leading researchers, computational resources, data and funding within a coordinated ecosystem that supports scientific excellence and strengthens Europe’s position in global AI research and innovation.

Why does it matter?

The initiative reflects growing recognition that AI is becoming a foundational tool for scientific discovery across disciplines ranging from healthcare and climate research to materials science and physics. Governments are increasingly investing in AI research infrastructure to ensure that researchers have access to the computing power, data and expertise needed to remain globally competitive.

The advisory board also highlights Europe’s ambition to play a larger role in shaping the future of AI-enabled science. By coordinating talent, infrastructure and funding through initiatives such as RAISE, the EU aims to strengthen both its scientific capacity and its position in the global race for AI innovation.

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Japanese researchers develop interpretable AI for materials discovery

Researchers in Japan have developed an interpretable AI method to explain how AI models make predictions in materials discovery. The method analyses features learned by a trained AI model and uses them to identify relationships between atomic structure and optical spectra.

The study was led by researchers from the Institute of Science Tokyo, in collaboration with Tohoku University. The work is expected to be published in the journal Advanced Intelligent Discovery.

AI is increasingly used in materials research to predict how materials behave based on atomic structure. Such models can accelerate materials discovery and reduce reliance on trial-and-error experimentation, but many operate as black boxes, making it difficult to understand how they arrive at specific predictions.

The researchers addressed this problem by analysing a trained AI model that predicts optical absorption spectra from atomic structural data. They extracted features from the model’s internal layers and clustered materials according to shared structural and spectral characteristics.

The team used an atomistic line graph neural network trained on data from 2,681 metal oxides, chalcogenides, and related compounds. The clustering process classified materials into groups sharing structural characteristics such as elemental composition, atomic coordination, bond lengths, bond angles and similar spectral signatures.

According to the researchers, the model learned meaningful relationships between atomic structure and material properties without being explicitly provided oxidation states or electronic configurations as input. The interpretable AI method could therefore help researchers identify the factors behind desired spectral shapes and support more rational materials design.

The approach could also be applied beyond optical absorption spectra. Researchers said the approach could also help explain how atomic arrangements influence other material properties under varying conditions, such as temperature and pressure, opening new possibilities for designing materials with targeted characteristics.

Why does it matter?

One of the main challenges facing the use of AI in scientific research is explainability. While AI systems can identify patterns and generate accurate predictions, researchers often need to understand the reasoning behind those predictions before they can confidently apply them in experimental settings.

By revealing how AI models connect atomic structures with material properties, interpretable AI could make machine learning a more effective tool for scientific discovery. The approach may help accelerate the development of advanced materials for applications ranging from renewable energy and electronics to sensors and next-generation manufacturing, while improving trust in AI-assisted research.

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South Korea selects site for AI defence robotics hub

South Chungcheong Province and the city of Nonsan have been selected to host a new AI defence robotics innovation cluster in South Korea.

The project was chosen under the Defense Acquisition Program Administration’s 2026 defence innovation cluster programme and will run for five years, from 2026 to 2030. It will receive a total of 49.9 billion won in national and local funding, including 24.5 billion won from the central government.

The cluster will be developed around Naedong and Yeonmu-eup in Nonsan and will focus on building an AI defence robotics ecosystem. The project is intended to support the full development cycle, from technology research and testing to demonstration and commercialisation.

Plans include a 45,190-square-metre testing and certification facility in Yeonmu-eup, designed to support research and development, test evaluation and demonstration activities in one location.

The initiative will involve Chungnam Techno Park, Konyang University, the Korea Testing Laboratory, the Korea Institute of Industrial Technology, the Korea Automotive Technology Institute and KAIST’s Mobility AX Research Institute.

Provincial officials said Nonsan’s existing defence infrastructure, including the Nonsan Defence National Industrial Complex, the headquarters of South Korea’s three armed services and Korea National Defense University, helped support the site’s selection.

Why does it matter?

The project shows how South Korea is linking AI, robotics and defence industrial policy through testing and certification infrastructure. For digital policy, the relevant signal is the institutionalisation of AI-enabled military robotics development, including facilities for experimentation, evaluation and commercialisation. It also reflects the growing importance of regional defence-tech clusters as governments seek to build domestic capacity in autonomous and unmanned systems.

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UK to test AI legal assistants to help reduce court delays

The UK government will develop and test AI legal assistants as part of a broader set of technology initiatives aimed at reducing court delays and improving the efficiency of the justice system. The Ministry of Justice said the tools will support routine casework, including research and case analysis, before any possible use in the Crown Court.

The AI legal assistants will be developed in collaboration with legal professionals and AI developers, with initial testing taking place in controlled environments. The government said the trials will help establish standards for the safe and ethical use of AI in legal settings and ensure any future systems meet the expectations of judges and legal practitioners before wider deployment.

Judges are also preparing to test an AI tool designed to identify trial-ready cases and group similar hearings together. The government said the tool is intended to better use judicial, prosecutorial, and court resources, helping cases move more quickly for victims.

The announcement also covers Justice Transcribe, an AI tool now available to every probation officer in England and Wales. The tool records and transcribes conversations with offenders, reducing the administrative burden associated with transferring handwritten notes into digital systems.

According to the government, Justice Transcribe could free up the equivalent of 18,750 days annually, enabling probation officers to spend more time supervising offenders and supporting efforts to reduce reoffending. A similar transcription tool is being trialled in Immigration and Asylum Tribunals to support judges with case notes and reduce administrative pressure.

The projects form part of the Prime Minister’s AI Exemplars programme, which aims to accelerate the adoption of AI across public services. The government also pointed to AI Growth Labs, secure testing environments intended to help the UK lawtech sector develop and refine AI products before bringing them to market.

Why does it matter?

Justice systems around the world are exploring how AI can help address growing caseloads, administrative burdens and resource constraints. Applications such as legal research assistance, transcription services and case management tools have the potential to improve efficiency while allowing legal professionals to focus on higher-value tasks.

At the same time, the use of AI in judicial and legal contexts raises important questions about accountability, transparency, fairness and human oversight. The UK’s emphasis on controlled testing and ethical safeguards reflects growing recognition that AI deployment in the justice sector requires robust governance alongside technological innovation.

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Researchers develop AI governance tools for public health across the Global Majority

A research team led by Professor Jude Kong from the University of Toronto is developing new tools to monitor, assess, and govern the use of AI in public health across the Global Majority, with a particular focus on Africa.

The team, which includes Jake Effoduh, Jim Hinton, Abbas Yazdinejad, and Maral Niaz, has begun mapping how AI is being integrated into healthcare systems and infrastructure. The work focuses on identifying key actors, technologies and use cases, providing a clearer picture of how AI is becoming embedded in public health systems.

The next phase involves developing a dynamic dashboard designed to track AI systems and support evidence-based decision-making. Rather than relying solely on top-down governance frameworks, the team aims to co-develop tools that policymakers, civil society organisations, educators and practitioners can use in their own contexts.

In practice, this means creating tools that are not only technically robust but also socially legitimate and locally relevant. While strengthening AI literacy and governance capacity across the Global Majority, the initiative aims to empower policymakers with evidence-based insights, support civil society in understanding AI systems, and enable more informed and inclusive decision-making processes.

By bringing together expertise in technology, law, public policy and social impact, the project reflects the multidisciplinary nature of AI governance. The team will present its findings at the AI for Good Global Summit in Geneva, during ITU’s Kaleidoscope sessions on Thursday, 9 July 2026, from 15:30 to 16:30.

Why does this matter in AI world?

AI for the Global Majority (AI4GM) is a joint initiative of the Geneva Graduate Institute, Microsoft and the International Telecommunication Union. The initiative supports research on how AI can benefit majority populations in areas including governance, education, health, finance, and digital innovation.

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UAE laboratory introduces AI-powered prostate cancer diagnostics

M42’s National Reference Laboratory has introduced an AI-powered tool for prostate cancer diagnostics in the UAE in partnership with digital pathology company Qritive. The platform will be integrated into the laboratory’s diagnostic workflow at Cleveland Clinic Abu Dhabi.

The system analyses digital pathology slides, highlights suspicious findings and provides structured insights to help pathologists detect prostate cancer and assess disease severity. It is designed to identify cancerous tissue, assess tumour patterns, support grading according to internationally recognised standards and measure tumour burden.

M42 said the technology is intended to assist medical professionals rather than replace them, with final clinical decisions remaining the responsibility of physicians. The company said AI can help pathologists make complex decisions more efficiently, reduce interpretation variation, and support better patient outcomes.

NRL said the platform could help healthcare providers manage growing diagnostic demand while giving patients and clinicians faster access to critical information. Prostate cancer remains a growing health concern in the Middle East, with officials citing an estimated 50,000 new cases diagnosed each year.

The introduction of the platform forms part of NRL’s wider strategy to strengthen oncology services and expand the use of digital pathology and AI-enabled diagnostics. Officials said the initiative supports the UAE’s broader objectives of advancing healthcare innovation, improving patient outcomes and building a more data-driven health system under UAE Vision 2031.

Why does it matter?

AI-assisted diagnostics are increasingly being adopted to help healthcare professionals manage growing workloads, improve consistency in clinical assessments and accelerate access to diagnostic results. In pathology, AI tools can help identify patterns in medical images and support decision-making, particularly in areas where demand for specialist expertise is rising.

The deployment also reflects the UAE’s broader strategy of integrating AI into healthcare services as part of its digital transformation agenda. As healthcare systems seek to improve efficiency and patient outcomes, AI-enabled diagnostics are becoming an increasingly important component of modern medical practice.

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