Among the big players in technology, Cisco is one of the sector’s leaders that’s advancing operational deployments of AI internally to its own operations, and the tools it sells to its customers around the world. As a large company, its activities encompass many areas of the typical IT stack, including infrastructure, services, security, and the design of entire enterprise-scale networks.

Cisco’s internal teams use a blend of machine learning and agentic AI to help them improve their own service delivery and personalise user experiences for its customers. It’s built a shared AI fabric built on patterns of compute and networking that are the product of years spent checking and validating its systems – battle-hardened solutions it then has the confidence to offer to customers. The infrastructure in play relies on high-performance GPUs, of course, but it’s not just raw horse-power. The detail is in the careful integration between compute and network stacks used in model training and the quite different demands from the ongoing load of inference.

Having made its name as the de facto supplier of networking infrastructure for the enterprise, it comes as no shock that it’s in network automation that some of its better-known uses of AI finds their place. Automated configuration workflows and identity management combine into access solutions that are focused on rapid network deployments generated by natural language.

For organisations looking to develop into the next generation of AI users, Cisco has been rolling out hardware and orchestration tools that are aimed explicitly to support AI workloads. A recent collaboration with chip giant NVIDIA led to the emergence of a new line of switches and the Nexus Hyperfabric line of AI network controllers. These aim to simplify the deployment of the complex clusters needed for top-end, high-performance artificial intelligence clusters.

Cisco’s Secure AI Factory framework with partners like NVIDIA and Run:ai is aimed at production-grade AI pipelines. It uses distributed orchestration, GPU utilisation governance, Kubernetes microservice optimisation, and storage, under the umbrella product description Intersight. For more local deployments, Cisco Unified Edge brings all the necessary elements – compute, networking, security, and storage – close to where data gets generated and processed.

In environments where latency metrics are critically important, AI processing at the edge is the answer. But Cisco’s approach is not necessarily to offer dedicated IIoT-specific solutions. Instead, it tries to extend the operational models typically found in a data centre and applies the same technology (if not the same exact methodology) to edge sites. It’s like data centre-grade security policies and configurations available to remote installations. Having the same precepts and standards in cloud and edge mean that Cisco accredited engineers can manage and maintain data centres or small edge deployments using the same skills, accreditation, knowledge, and experience.

Security and risk management figure prominently in the Cisco AI narrative. Its Integrated AI Security and Safety Framework applies high standards of safety and security throughout the life-cycle of AI systems. It considers adversarial threats, supply chain weakness, the risk profiles of multi-agent interactions, and multi-modal vulnerabilities as issues that have to be addressed regardless of the nature or size of any deployment.

Cisco’s work on operational AI also reflects broader ecosystem conversations. The company markets products for organisations wanting to make the transition from generative to agentic AI, where autonomous software agents carry out operational tasks. In most cases, this requires new tooling and new operational protocols.

Cisco’s future AI plans include continuing its central work in infrastructure provision for AI workloads. It’s also pursuing broader adoption of AI-ready networks, including next-gen wireless and unified management systems that will control systems across campus, branch, and cloud environments. The company is also expanding its software and platform investments, including its most recent acquisition (NeuralFabric), to help it build a more comprehensive software stack and product portfolio.

In summary, Cisco’s AI deployment strategy combines hardware, software, and service elements that embed AI into operations, giving organisations a route to production-grade systems. Its work can be found in large-scale infrastructure, systems for unified management, risk mitigation, and anywhere that connects distributed, cloud, and edge computing.

(Image source: Pixabay)

 

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arXiv:2602.02629v1 Announce Type: cross Abstract: The digitization of healthcare has generated massive volumes of Electronic Health Records (EHRs), offering unprecedented opportunities for training Artificial Intelligence (AI) models. However, stringent privacy regulations such as GDPR and HIPAA have created data silos that prevent centralized training. Federated Learning (FL) has emerged as a promising solution that enables collaborative model training without sharing raw patient data. Despite its potential, FL remains vulnerable to poisoning and Sybil attacks, in which malicious participants corrupt the global model or infiltrate the network using fake identities. While recent approaches integrate Blockchain technology for auditability, they predominantly rely on probabilistic reputation systems rather than robust cryptographic identity verification. This paper proposes a Trustworthy Blockchain-based Federated Learning (TBFL) framework integrating Self-Sovereign Identity (SSI) standards. By leveraging Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), our architecture ensures only authenticated healthcare entities contribute to the global model. Through comprehensive evaluation using the MIMIC-IV dataset, we demonstrate that anchoring trust in cryptographic identity verification rather than behavioral patterns significantly mitigates security risks while maintaining clinical utility. Our results show the framework successfully neutralizes 100% of Sybil attacks, achieves robust predictive performance (AUC = 0.954, Recall = 0.890), and introduces negligible computational overhead (

arXiv:2602.03154v1 Announce Type: cross Abstract: Front-end personalization has traditionally relied on static designs or rule-based adaptations, which fail to fully capture user behavior patterns. This paper presents an AI driven approach for dynamic front-end personalization, where UI layouts, content, and features adapt in real-time based on predicted user behavior. We propose three strategies: dynamic layout adaptation using user path prediction, content prioritization through reinforcement learning, and a comparative analysis of AI-driven vs. rule-based personalization. Technical implementation details, algorithms, system architecture, and evaluation methods are provided to illustrate feasibility and performance gains.

This AI doctor is licensed in all 50 states, the startup says. The deal was led by CRV and Kleiner Perkins.

When is an AI doctor a medical device?

Call it a sign of things to come. A startup called Doctronic made a splash recently when it announced the use AI to renew prescriptions without clinician input in the state of Utah. Something didn’t sit right with me about the announcement. Sure it got approval from Utah, but why isn’t it a medical device subject to Food and Drug Administration review? The company claimed it was “the practice of medicine” and so exempt from FDA authority. That didn’t seem entirely right either. 

So I did some asking around and after talking to over a dozen executives, legal scholars, and policy experts, it turns out the question is not nearly as clear-cut as Doctronic would have us believe. Indeed, it appears the company may be planning to market a medical device without authorization. In my story, I explain the law and why it all matters.

Read more here

Continue to STAT+ to read the full story…

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Utah’s recent announcement that it was partnering with a health tech startup that will use artificial intelligence to renew drug prescriptions may offer a glimpse of the futuristic version of AI medicine that’s long been foretold by technologists and venture capitalists.

But it’s only possible because of some pre-approved rule breaking — and may prove a broader test of the Food and Drug Administration’s authority to evaluate a new wave of clinical AI products, according to interviews with executives and experts.

In January, Utah regulators said they had signed an agreement with a startup called Doctronic to launch an AI system that will perform a clinical evaluation of patients and, when deemed appropriate, renew some 200 common medications autonomously.

Continue to STAT+ to read the full story…

© Illustration: Camille MacMillin/STAT; Photos: Adobe

npj Digital Medicine, Published online: 03 February 2026; doi:10.1038/s41746-026-02385-z

Digital intervention mylovia improves sexual functioning in women with sexual dysfunction in randomized controlled trial

Background: The transformation of digital health technologies has reshaped how healthcare is delivered, particularly in primary care. However, despite the advantages of these innovations, older adults remain among the most resistant users. Traditional technology adoption models may not fully capture the complexity of this reluctance, which is shaped not only by usability challenges but also by emotional, psychological, and identity-related concerns. Innovation Resistance Theory (IRT) offers a complementary framework focused on understanding barriers to adoption rather than solely on facilitators. Objective: To map and synthesize evidence on older adults’ resistance to digital health technologies in primary care through the lens of IRT, and to examine how empirically observed resistance factors align with, extend, or refine IRT’s functional and psychological barriers. Methods: A scoping review combined with concept-driven thematic synthesis was conducted. Empirical studies published between 2014 and 2025 were identified through systematic searches across five databases: PubMed, CINAHL, Ovid Medline, Web of Science, and Scopus. Inclusion criteria focused on studies examining barriers or resistance to digital health use among older adults aged 60 and above in primary care settings. The search was guided by terms related to “older adults”, “digital health”, “eHealth”, “telemedicine”, and “technology resistance”. After screening and reviewing the full texts, data were extracted into a structured matrix, and findings were organized according to the five dimensions of the IRT: usage, value, risk, tradition, and image barriers. Results: Of 4,976 identified records, seventeen studies met the inclusion criteria. Functional barriers included usability challenges, interface complexity, and age-related impairments. Psychological resistance was frequently linked to emotional discomfort, symbolic misalignment, and concerns about the loss of relational care. Value and risk concerns included distrust in diagnostics accuracy, concerns regarding privacy and data security, and skepticism about care quality. Traditional preferences for face-to-face interactions and generational digital divides further reinforced image-based resistance. A key finding was the interaction between barriers, where low self-efficacy and technology anxiety create feedback loops that reinforce avoidance behaviors. Conclusions: Resistance to digital health among older adults is not simply a lack of adoption but a complex, emotionally grounded process involving functional, psychological, and identity-based barriers. Interventions must go beyond technical usability to rebuild emotional trust, preserve the relational aspects of care, and align digital solutions with the values and expectations of older adults. Innovation Resistance Theory offers a comprehensive framework for understanding these multifaceted dynamics and serves as a valuable guide for policy development, user-centered design, and future research Clinical Trial: None

Background: Online health communities have evolved into digital marketplaces where physicians have to compete for patients. Existing research examines physician-patient dynamics through a patient-centric lens, treating physicians as passive recipients of ratings and reviews, while the strategic role of physician self-disclosure remains unexamined. This gap constrains a comprehensive understanding of how physicians can actively shape patient decisions, making the investigation of strategic self-disclosure imperative. Objective: This study aims to investigate the relationship between physician self-disclosure breadth (scope of information) and depth (detailed expertise) and patient decision-making, as well as whether regional digital health care level (DHL) moderates these relationships. Methods: We conducted a cross-sectional analysis of observational data to test these relationships. Data were collected from China’s online health care platform Haodf from September to December 2024. Self-disclosure breadth (including clinical performance, academic experience, and social reputation), self-disclosure depth (including expertise coverage, richness, and granularity), and patient decision-making (total visits) were captured through manual content coding and quantitative measurement. We used structured content analysis to extract the disclosure components, informational scope, and descriptive details of each profile. Then, using validated operational formulas, we calculated the composite indices for disclosure breadth and depth based on the coded dimensions. The study generated 1798 final physician samples with complete data across 14 focal variables. The hypotheses were tested using an ordinary least squares regression model, and 4 robustness checks were conducted, including variable substitution and different resampling techniques. Results: In the primary ordinary least squares regression models, self-disclosure breadth was significantly and positively associated with patient visits (β=0.255, 95% CI 0.054-0.456; P=.01), as was self-disclosure depth (β=0.098, 95% CI 0.030-0.167; P=.005). The breadth×DHL interaction was positive and significant (β=0.261, 95% CI 0.061-0.461; P=.01). Similarly, the depth×DHL interaction was positive and significant (β=0.070, 95% CI 0.002-0.138; P=.045). It should be noted that the association for self-disclosure breadth was stronger than that of self-disclosure depth. DHL strengthened the relationship between the disclosure strategies with patient visits. This contextual amplification indicates that DHL serves as a critical boundary condition, determining the degree to which physician self-disclosure strategies translate into patient acquisition outcomes. Conclusions: This study reconceptualizes physicians as strategic agents shaping patient decision-making through purposeful self-disclosure. Different from existing studies treating physicians as passive recipients of ratings and reviews, our research demonstrates that physicians can strategically shape patient acquisition through self-disclosure breadth and depth. This study brings new insights to digital health markets by demonstrating that self-disclosure operates as a viable patient acquisition mechanism, wherein the DHL acts as a critical boundary condition. The findings have real-world implications: (1) physicians can leverage evidence-based disclosure strategies, (2) platforms should implement context-adaptive features, and (3) policymakers should prioritize digital infrastructure investments to enhance physicians' competitive capabilities and patient decision-making quality.

New research from Accenture has discovered insurance executives are planning on increased investment into AI during 2026 despite a widening skills gap in insurance organisations.

Surveying 3,650 C-suite leaders over 20 industries and 20 countries, the Pulse of Change poll revealed 90% of the 218 senior insurance executives intend to spend more on AI over the next year. In all, 85% of the respondents view AI as a tool for revenue expansion not one that reduces costs.

While organisations are upping their AI investment to drive growth, 35% of leaders acknowledge that true progress depends on getting core data strategies and digital abilities right. 54% of employees reported that low-quality or misleading AI outputs are undermining AI’s benefits, leading to reduced productivity and time-wasting.

AI investment may not be enough, Accenture says. Its survey suggests sustainable growth relies on data quality and trusted outputs.

AI adoption enters enterprise scale

The Pulse of Change survey indicates a shift in AI adoption as it goes beyond experimental phases to large scale organisational levels. With 34% of insurance companies now rolling out AI agents in multiple functions, insurers are heading into operational use and away from isolated experiments.

almost a third of senior C-suite leaders are frequently using generative AI, highlighting increased implementation at the highest level. Therefore, AI is undoubtedly shaping workflows, strategies, and key decisions, affecting all facets of businesses.

Nearly a third of businesses are rebuilding entire processes with AI. No longer is the technology a supporting addition to existing workflows; it has become a central component, signalling a more mature stage of AI adoption.

Despite redesigning processes to include AI, fewer than 10% are redesigning employee roles to match such changes, resulting in many employees feeling unprepared. Just 40% claimed their training has equipped them for new AI responsibilities, and only 20% feel like they have any say in how AI affects their work.

AI adoption by companies may be accelerating, but employee use lags behind. There has been a 10 percentage point drop in regular AI use by employees since summer 2025, while only 39% are trying AI tools independently, a drop of 15 points. For effective AI use and to speed up AI adoption among the workforce, companies must be prepared to redesign job roles, align incentives, and provide improved training programmes as, right now, employees are feeling hesitant and unprepared to use AI on their own.

AI investment still fuelling executive optimism amid bubble fears

Although talks around a potential AI bubble continue to cloud the industry, insurance executives remain confident. 47% claimed they would increase AI spending if the bubble burst, and 37% would escalate recruitment.

Altogether, 6% said they would “decrease investments ([by] 20% or more),” 22% would “somewhat decrease investments ([by] up to 20%),” 24% would make “no change,” 40% would “somewhat increase investments (up to 20%),” and 7% would “increase investments (20% or more).”

Khalid Lahraoui, Accenture’s insurance industry group lead, commented, “It’s clear that insurance leaders are confident in AI’s capacity to drive growth, and as such, they are decisively increasing investments, despite ROI uncertainty.”

Lack of AI skills blocking AI’s potential value

As insurance executives prepare to invest heavily in AI, obstacles lie in wait. For instance, a quarter of executives said skill shortages are a core concern and a key player in determining the value they extract from AI. Although these challenges persist in different industries, just 24% of respondents have implemented continuous learning programmes associated with AI. Moreover, only 5% said they are adjusting job positions to support the adoption of AI.

AI adoption disconnect

The disconnect between C-suite leaders and employees is evident from the survey’s data. Although talent is the main driver of AI scaling, employees feel less confident and secure than leadership assumes. 23% of C-suite leaders said improved access to skilled talent would accelerate their AI implementation strategies. 38% of employees believe their organisation would respond effectively to technological disruption, but just 30% feel confident about how their company would handle talent disruption.

Job security is also waning, with 48% feeling secure in their roles, down from 59% in summer 2025. Meanwhile, 59% of workers believe young professionals are finding it more challenging to find jobs due to automation and AI. Leadership may see talent as an accelerator for AI, but anxiety around job security and organisational readiness persists.

Key focus is on investment

Approximately two thirds of executives are prioritising investments in digital technologies and AI amid the rapid changes facing global industries. 67% reported feeling well-prepared for technological disruption, but only 39% felt confident if there was environmental disruption, and 44% for geopolitical disruption.

Again, there is a divide between leadership and employees, with only 29% of insurance workers feeling confident during economic disruption compared to 43% of leaders.

Optimism among insurance executives and C-suite leaders as a whole remains high, despite 82% expecting further changes in 2026, a 24 percentage gap with employees. 78% anticipate stronger and faster revenue growth in the next year and 82% have plans to increase recruitment.

According to Accenture’s report, the key challenge is not AI technology itself; it’s getting employees on board, engaged, and ready to work with AI.

As the report notes, bridging the gap between technology and people is the key to success. “2026 will favour those that align the confidence in their technological investments with commitment to workforce needs,” the report concludes.

(Image source: “Accenture Building City View Plaza San Jose” by mrkathika is licensed under CC BY-SA 2.0.)

 

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When I first heard about OpenAI’s ChatGPT Health, I felt a familiar itch.

Since being diagnosed with a malignant brain tumor 18 years ago, at age 29, I’ve developed a deep curiosity about my own health. That curiosity has driven me to enroll in numerous studies, connect my health records to the NIH All of Us research program, and even donate my brain tissue for research-grade genomic sequencing.

Read the rest…

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A new report from Deloitte has warned that businesses are deploying AI agents faster than their safety protocols and safeguards can keep up. Therefore, serious concerns around security, data privacy, and accountability are spreading.

According to the survey, agentic systems are moving from pilot to production so quickly that traditional risk controls, which were designed for more human-centred operations, are struggling to meet security demands.

Just 21% of organisations have implemented stringent governance or oversight for AI agents, despite the increased rate of adoption. Whilst 23% of companies stated that they are currently using AI agents, this is expected to rise to 74% in the next two years. The share of businesses yet to adopt this technology is expected to fall from 25% to just 5% over the same period.

Poor governance is the threat

Deloitte is not highlighting AI agents as inherently dangerous, but states the real risks are associated with poor context and weak governance. If agents operate as their own entities, their decisions and actions can easily become opaque. Without robust governance, it becomes difficult to manage and almost impossible to insure against mistakes.

According to Ali Sarrafi, CEO & Founder of Kovant, the answer is governed autonomy. “Well-designed agents with clear boundaries, policies and definitions managed the same way as an enterprise manages any worker can move fast on low-risk work inside clear guardrails, but escalate to humans when actions cross defined risk thresholds.”

“With detailed action logs, observability, and human gatekeeping for high-impact decisions, agents stop being mysterious bots and become systems you can inspect, audit, and trust.”

As Deloitte’s report suggests, AI agent adoption is set to accelerate in the coming years, and only the companies that deploy the technology with visibility and control will hold the upper hand over competitors, not those who deploy them quickest.

Why AI agents require robust guardrails

AI agents may perform well in controlled demos, but they struggle in real-world business settings where systems can be fragmented and data may be inconsistent.

Sarrafi commented on the unpredictable nature of AI agents in these scenarios. “When an agent is given too much context or scope at once, it becomes prone to hallucinations and unpredictable behaviour.”

“By contrast, production-grade systems limit the decision and context scope that models work with. They decompose operations into narrower, focused tasks for individual agents, making behaviour more predictable and easier to control. This structure also enables traceability and intervention, so failures can be detected early and escalated appropriately rather than causing cascading errors.”

Accountability for insurable AI

With agents taking real actions in business systems, such as keeping detailed action logs, risk and compliance are viewed differently. With every action recorded, agents’ activities become clear and evaluable, letting organisations inspect actions in detail.

Such transparency is crucial for insurers, who are reluctant to cover opaque AI systems. This level of detail helps insurers understand what agents have done, and the controls involved, thus making it easier to assess risk. With human oversight for risk-critical actions and auditable, replayable workflows, organisations can produce systems that are more manageable for risk assessment.

AAIF standards a good first step

Shared standards, like those being developed by the Agentic AI Foundation (AAIF), help businesses to integrate different agent systems, but current standardisation efforts focus on what is simplest to build, not what larger organisations need to operate agentic systems safely.

Sarrafi says enterprises require standards that support operation control, and which include, “access permissions, approval workflows for high-impact actions, and auditable logs and observability, so teams can monitor behaviour, investigate incidents, and prove compliance.”

Identity and permissions the first line of defence

Limiting what AI agents can access and the actions they can perform is important to ensure safety in real business environments. Sarrafi said, “When agents are given broad privileges or too much context, they become unpredictable and pose security or compliance risks.”

Visibility and monitoring are important to keep agents operating inside limits. Only then can stakeholders have confidence in the adoption of the technology. If every action is logged and manageable, teams can then see what has happened, identify issues, and better understand why events occurred.

Sarrafi continued, “This visibility, combined with human supervision where it matters, turns AI agents from inscrutable components into systems that can be inspected, replayed and audited. It also allows rapid investigation and correction when issues arise, which boosts trust among operators, risk teams and insurers alike.”

Deloitte’s blueprint

Deloitte’s strategy for safe AI agent governance sets out defined boundaries for the decisions agentic systems can make. For instance, they might operate with tiered autonomy, where agents can only view information or offer suggestions. From here, they can be allowed to take limited actions, but with human approval. Once they have proven to be reliable in low-risk areas, they can be allowed to act automatically.

Deloitte’s “Cyber AI Blueprints” suggest governance layers and embedding policies and compliance capability roadmaps into organisational controls. Ultimately, governance structures that track AI use and risk, and embedding oversight into daily operations are important for safe agentic AI use.

Readying workforces with training is another aspect of safe governance. Deloitte recommends training employees on what they shouldn’t share with AI systems, what to do if agents go off track, and how to spot unusual, potentially dangerous behaviour. If employees fail to understand how AI systems work and their potential risks, they may weaken security controls, albeit unintentionally.

Robust governance and control, alongside shared literacy are fundamental to the safe deployment and operation of AI agents, enabling secure, compliant, and accountable performance in real-world environments

(Image source: “Global Hawk, NASA’s New Remote-Controlled Plane” by NASA Goddard Photo and Video is licensed under CC BY 2.0. )

 

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The post Deloitte sounds alarm as AI agent deployment outruns safety frameworks appeared first on AI News.

Nature, Published online: 28 January 2026; doi:10.1038/d41586-026-00037-6

Co-optimized design and technology has resulted in cheap, flexible microchips that efficiently run neural-network tasks, as demonstrated in wearable health-care devices.

arXiv:2601.18799v1 Announce Type: cross Abstract: As digital twins (DTs) evolve to become more agentic through the integration of artificial intelligence (AI), they acquire capabilities that extend beyond dynamic representation of their target systems. This paper presents a taxonomy of agentic DTs organised around three fundamental dimensions: the locus of agency (external, internal, distributed), the tightness of coupling (loose, tight, constitutive), and model evolution (static, adaptive, reconstructive). From the resulting 27-configuration space, we identify nine illustrative configurations grouped into three clusters: "The Present" (existing tools and emerging steering systems), "The Threshold" (where emergent properties appear and coupling becomes constitutive), and "The Frontier" (where systems gain reconstructive capabilities). Our analysis explores how agentic DTs exercise performative power--not merely representing physical systems but actively participating in constituting them. Using traffic navigation systems as examples, we show how even passive tools can exhibit emergent performativity, while advanced configurations risk performative lock-in. Drawing on performative prediction theory, we trace a progression from passive tools through active steering to ontological reconstruction, examining how constitutive coupling enables systems to create self-validating realities. Understanding these configurations is essential for navigating the transformation from DTs as mirror worlds to DTs as architects of new ontologies.

arXiv:2601.18814v1 Announce Type: cross Abstract: Background: Coronary angiography (CAG) is the cornerstone imaging modality for evaluating coronary artery stenosis and guiding interventional decision-making. However, interpretation based on single-frame angiographic images remains highly operator-dependent, and conventional deep learning models still face challenges in modeling complex vascular morphology and fine-grained texture patterns.Methods: We propose a Lightweight Quantum-Enhanced ResNet (LQER) for binary classification of coronary angiography images. A pretrained ResNet18 is employed as a classical feature extractor, while a parameterized quantum circuit (PQC) is introduced at the high-level semantic feature space for quantum feature enhancement. The quantum module utilizes data re-uploading and entanglement structures, followed by residual fusion with classical features, enabling end-to-end hybrid optimization with a strictly controlled number of qubits.Results: On an independent test set, the proposed LQER outperformed the classical ResNet18 baseline in accuracy, AUC, and F1-score, achieving a test accuracy exceeding 90%. The results demonstrate that lightweight quantum feature enhancement improves discrimination of positive lesions, particularly under class-imbalanced conditions.Conclusion: This study validates a practical hybrid quantum--classical learning paradigm for coronary angiography analysis, providing a feasible pathway for deploying quantum machine learning in medical imaging applications.

arXiv:2601.19380v1 Announce Type: cross Abstract: Using multiple open-access models trained on public datasets, we developed Tri-Reader, a comprehensive, freely available pipeline that integrates lung segmentation, nodule detection, and malignancy classification into a unified tri-stage workflow. The pipeline is designed to prioritize sensitivity while reducing the candidate burden for annotators. To ensure accuracy and generalizability across diverse practices, we evaluated Tri-Reader on multiple internal and external datasets as compared with expert annotations and dataset-provided reference standards.