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…

© Adobe

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