Adaptive optics meets AI for cellular-scale eye care

AI is moving from lab demos to frontline eye care, with clinicians using algorithms alongside routine fundus photos to spot disease before symptoms appear. The aim is simple: catch diabetic retinopathy early enough to prevent avoidable vision loss and speed referrals for treatment.

New imaging workflows pair adaptive optics with machine learning to shrink scan times from hours to minutes while preserving single-cell detail. At the US National Eye Institute, models recover retinal pigment epithelium features and clean noisy OCT data to make standard scans more informative.

Duke University’s open-source DCAOSLO goes further by combining multiplexed light signals with AI to capture cellular-scale images quickly. The approach eases patient strain and raises the odds of getting diagnostic-quality data in busy clinics.

Clinic-ready diagnostics are already changing triage. LumineticsCore, the first FDA-cleared AI to detect more-than-mild diabetic retinopathy from primary-care images, flags who needs urgent referral in seconds, enabling earlier laser or pharmacologic therapy.

Researchers also see the retina as a window on wider health, linking vascular and choroidal biomarkers to diabetes, hypertension and cardiovascular risk. Standardised AI tools promise more reproducible reads, support for trials and, ultimately, home-based monitoring that extends specialist insight beyond the clinic.

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AI system links hidden signals in patient records to improve diagnosis

Researchers at Mount Sinai and UC Irvine have developed a novel AI system, InfEHR, which creates a dynamic network of an individual’s medical events and relationships over time. The system detects disease patterns that traditional approaches often miss.

InfEHR transforms time-ordered data, visits, labs, medications, and vital signs, into a graphical network for each patient. It then learns which combinations of clues across that network tend to correlate with hidden disease states.

In testing, with only a few physician-annotated examples, the AI system identified neonatal sepsis without positive blood cultures at rates 12–16× higher than current methods, and post-operative kidney injury with 4–7× more sensitivity than baseline clinical rules.

As a safety feature, InfEHR can also respond ‘not sure’ when the record lacks enough signal, reducing the risk of overconfident errors.

Because it adapts its reasoning per patient rather than applying the same rules to all, InfEHR shows promise for personalized diagnostics across hospitals and populations, even with relatively small annotated datasets.

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Oracle and Microsoft partner to bring real-time AI insights into supply chains

Oracle announced a collaboration with Microsoft aimed at improving supply chain responsiveness and efficiency. The project centres on a new integration blueprint that bridges Oracle Fusion Cloud SCM with Microsoft Azure IoT Operations and Microsoft Fabric.

Under this plan, sensor and equipment data from factory floors is captured in real time via Azure IoT and forwarded through Fabric. That data will then feed directly into Oracle SCM workflows.

The goal: more visibility, faster decisions and automated responses, such as triggering maintenance, quality checks or inventory adjustments.

Among the features highlighted are secure, real-time intelligence and data flows from shop floor equipment into enterprise systems, automated business events that respond to changes (e.g. imbalance, faults, demand shifts), standardised best practices with reference architectures and prescriptive guidance for integration and embedded AI assistant capabilities in SCM to augment decision making and resilience.

Oracle frames this as part of its Smart Operations vision, where systems are more connected and responsive by design. Microsoft emphasises that Azure’s edge processing and Fabric’s real-time analytics are critical to turning raw IoT signals into actionable business events.

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Capita hit with £14 million fine after major data breach

The UK outsourcing firm Capita has been fined £14 million after a cyber-attack exposed the personal data of 6.6 million people. Sensitive information, including financial details, home addresses, passport images, and criminal records, was compromised.

Initially, the fine was £45 million, but it was reduced after Capita improved its cybersecurity, supported affected individuals, and engaged with regulators.

A breach that affected 325 of the 600 pension schemes Capita manages, highlighting risks for organisations handling large-scale sensitive data.

The Information Commissioner’s Office (ICO) criticised Capita for failing to secure personal information, emphasising that proper security measures could have prevented the incident.

Experts note that holding companies financially accountable reinforces the importance of data protection and sends a message to the market.

Capita’s CEO said the company has strengthened its cyber defences and remains vigilant to prevent future breaches.

The UK government has advised companies like Capita to prepare contingency plans following a rise in nationally significant cyberattacks, a trend also seen at Co-op, M&S, Harrods, and Jaguar Land Rover earlier in the year.

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Apple launches M5 with bigger AI gains

Apple unveiled the M5 chip, targeting a major jump in on-device AI. Apple says peak GPU compute for AI is over four times M4, with a Neural Accelerator in each of the 10 GPU cores.

The CPU pairs up to four performance cores with six efficiency cores for up to 15 percent faster multithreaded work versus M4. A faster 16-core Neural Engine and higher unified memory bandwidth at 153 GB/s aim to speed Apple Intelligence features.

Graphics upgrades include third-generation ray tracing and reworked caching for up to 45 percent higher performance than M4 in supported apps. With the help of AI, Apple notes smoother gameplay and quicker 3D renders, plus Vision Pro refresh up to 120 Hz.

The M5 chip reaches the 14-inch MacBook Pro, iPad Pro, and Apple Vision Pro, with pre-orders open. Apple highlights tighter tie-ins with Core ML, Metal 4 and Tensor APIs, and support for larger local models via unified memory up to 32 GB.

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Microsoft to support UAE investment analytics with responsible AI tools

The UAE Ministry of Investment and Microsoft signed a Memorandum of Understanding at GITEX Global 2025 to apply AI to investment analytics, financial forecasting, and retail optimisation. The deal aims to strengthen data governance across the investment ecosystem.

Under the MoU, Microsoft will support upskilling through its AI National Skilling Initiative, targeting 100,000 government employees. Training will focus on practical adoption, responsible use, and measurable outcomes, in line with the UAE’s National AI Strategy 2031.

Both parties will promote best practices in data management using Azure services such as Data Catalog and Purview. Workshops and knowledge-sharing sessions with local experts will standardise governance. Strong controls are positioned as the foundation for trustworthy AI at scale.

The agreement was signed by His Excellency Mohammad Alhawi and Amr Kamel. Officials say the collaboration will embed AI agents into workflows while maintaining compliance. Investment teams are expected to gain real-time insights and automation that shorten the time to action.

The partnership supports the ambition to make the UAE a leader in AI-enabled investment. It also signals deeper public–private collaboration on sovereign capabilities. With skills, standards, and use cases in place, the ministry aims to attract capital and accelerate diversification.

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Agentic AI at scale with Salesforce and AWS

Salesforce and AWS outlined a tighter partnership on agentic AI, citing rapid growth in enterprise agents and usage. They set four pillars for the ‘Agentic Enterprise’: unified data, interoperable agents, modernised contact centres and streamlined procurement via AWS Marketplace.

Data 360 ‘Zero Copy’ accesses Amazon Redshift without duplication, while Data 360 Clean Rooms integrate with AWS Clean Rooms for privacy-preserving collaboration. 1-800Accountant reports agents resolving most routine inquiries so human experts focus on higher-value work.

Agentforce supports open standards such as Model Context Protocol and Agent2Agent to coordinate multi-vendor agents. Pilots link Bedrock-based agents and Slack integrations that surface Quick Suite tools, with Anthropic and Amazon Nova models available inside Salesforce’s trust boundary.

Contact centres extend agentic workflows through Salesforce Contact Center with Amazon Connect, adding voice self-service plus real-time transcription and sentiment. Complex issues hand off to representatives with full context, and Toyota Motor North America plans automation for service tasks.

Procurement scales via AWS Marketplace, where Salesforce surpassed $2bn in lifetime sales across 30 countries. AgentExchange listings provide prebuilt, customisable agents and workflows, helping enterprises adopt agentic AI faster with governance and security intact.

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New Cisco study shows most companies aren’t AI-ready

Most firms are still struggling to turn AI pilots into measurable value, Cisco’s 2025 AI Readiness Index finds. Only 13% are ‘AI-ready’, having scaled deployments with results. The rest face gaps in data, security and governance.

Southeast Asia outperforms the global average at 16% ready. Indonesia reaches 23% and Thailand 21%, ahead of Europe at 11% and the Americas at 14%. Cisco says lower tech debt helps some emerging markets leapfrog.

Infrastructure debt is mounting: limited GPU capacity, fragmented data and constrained networks slow progress. Just 34% say their tech stack can adapt and scale for evolving compute needs. Most remain stuck in pilots.

Adoption plans are ambitious: 83% intend to deploy AI agents, with almost 40% expecting them to support staff within a year. Yet only one in three have change-management programmes, risking stalled workplace integration.

The leaders pair strong digital foundations with clear governance and cybersecurity embedded by design. Cisco urges broader collaboration among industry, government and tech firms, arguing that trust, regulation and investment will determine who monetises AI first.

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Scaling a cell ‘language’ model yields new immunotherapy leads

Yale University and Google unveiled Cell2Sentence-Scale 27B, a 27-billion-parameter model built on Gemma to decode the ‘language’ of cells. The system generated a novel hypothesis about cancer cell behaviour, and CEO Sundar Pichai called it ‘an exciting milestone’ for AI in science.

The work targets a core problem in immunotherapy: many tumours are ‘cold’ and evade immune detection. Making them visible requires boosting antigen presentation. C2S-Scale sought a ‘conditional amplifier’ drug that boosts signals only in immune-context-positive settings.

Smaller models lacked the reasoning to solve the problem, but scaling to 27B parameters unlocked the capability. The team then simulated 4,000 drugs across patient samples. The model flagged context-specific boosters of antigen presentation, with 10–30% already known and the rest entirely novel.

Researchers emphasise that conditional amplification aims to raise immune signals only where key proteins are present. That could reduce off-target effects and make ‘cold’ tumours discoverable. The result hints at AI-guided routes to more precise cancer therapies.

Google has released C2S-Scale 27B on GitHub and Hugging Face for the community to explore. The approach blends large-scale language modelling with cell biology, signalling a new toolkit for hypothesis generation, drug prioritisation, and patient-relevant testing.

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Report warns of AI-driven divide in higher education

A new report from the Higher Education Policy Institute warns of an urgent need to improve AI literacy among staff and students in the UK. The study argues that without coordinated investment in training and policy, higher education risks deepening digital divides and losing relevance in an AI-driven world.

British report contributors say universities must move beyond acknowledging AI’s presence and instead adopt structured strategies for skill development. Kate Borthwick adds that both staff and students require ongoing education to manage how AI reshapes teaching, assessment, and research.

The publication highlights growing disparities in access and use of generative AI based on gender, wealth, and academic discipline. In a chapter written by ChatGPT, the report suggests universities create AI advisory teams within research offices and embed AI training into staff development programmes.

Elsewhere, Ant Bagshaw from the Australian Public Policy Institute warns that generative AI could lead to cuts in professional services staff as universities seek financial savings. He acknowledges the transition will be painful but argues that it could drive a more efficient and focused higher education sector.

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