arXiv:2503.01733v3 Announce Type: replace-cross Abstract: Smart homes equipped with ambient sensors offer a transformative approach to continuous health monitoring and assisted living. Traditional research in this domain primarily focuses on Human Activity Recognition (HAR), which relies on mapping sensor data to a closed set of predefined activity labels. However, the fixed granularity of these labels often constrains their practical utility, failing to capture the subtle, household-specific nuances essential, for example, for tracking individual health over time. To address this, we propose DISCOVER, a framework for discovering and annotating Patterns of Daily Living (PDL) - fine-grained, recurring sequences of sensor events that emerge directly from a resident's unique routines. DISCOVER utilizes a self-supervised feature extraction and representation-aware clustering pipeline, supported by a custom visualization interface that enables experts to interpret and label discovered patterns with minimal effort. Our evaluation across multiple smart-home environments demonstrates that DISCOVER identifies cohesive behavioral clusters with high inter-rater agreement while achieving classification performance comparable to fully-supervised baselines using only 0.01% of the labels. Beyond reducing annotation overhead, DISCOVER establishes a foundation for longitudinal analysis. By grounding behavior in a resident's specific environment rather than rigid semantic categories, our framework facilitates the observation of within-person habitual drift. This capability positions the system as a potential tool for identifying subtle behavioral indicators associated with early-stage cognitive decline in future longitudinal studies.

Nature, Published online: 04 February 2026; doi:10.1038/s41586-025-10087-x

A multiancestry phenome-wide analysis of copy number variants in the UK Biobank genomes increases power to detect genetic associations with complex traits across human populations.

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

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

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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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.

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:2601.12542v2 Announce Type: replace Abstract: Artificial intelligence systems for scientific discovery have demonstrated remarkable potential, yet existing approaches remain largely proprietary and operate in batch-processing modes requiring hours per research cycle, precluding real-time researcher guidance. This paper introduces Deep Research, a multi-agent system enabling interactive scientific investigation with turnaround times measured in minutes. The architecture comprises specialized agents for planning, data analysis, literature search, and novelty detection, unified through a persistent world state that maintains context across iterative research cycles. Two operational modes support different workflows: semi-autonomous mode with selective human checkpoints, and fully autonomous mode for extended investigations. Evaluation on the BixBench computational biology benchmark demonstrated state-of-the-art performance, achieving 48.8% accuracy on open response and 64.4% on multiple-choice evaluation, exceeding existing baselines by 14 to 26 percentage points. Analysis of architectural constraints, including open access literature limitations and challenges inherent to automated novelty assessment, informs practical deployment considerations for AI-assisted scientific workflows.

arXiv:2304.13894v2 Announce Type: replace-cross Abstract: The proliferation of the Internet of Things (IoT) has introduced a massive influx of devices into the market, bringing with them significant security vulnerabilities. In this diverse ecosystem, robust IoT device identification is a critical preventive measure for network security and vulnerability management. This study proposes a deep learning-based method to identify IoT devices using the Aalto dataset. We employ Convolutional Neural Networks (CNN) to classify devices by converting network packet payloads into pseudo-images. Furthermore, we compare the performance of this payload-based approach against a feature-based fingerprinting method. Our results indicate that while the fingerprint-based method is significantly faster (approximately 10x), the payload-based image classification achieves comparable accuracy, highlighting the trade-offs between computational efficiency and data granularity in IoT security.

arXiv:2601.12946v3 Announce Type: replace-cross Abstract: Generative artificial intelligence (AI) is rapidly populating medical records with synthetic content, creating a feedback loop where future models are increasingly at risk of training on uncurated AI-generated data. However, the clinical consequences of this AI-generated data contamination remain unexplored. Here, we show that in the absence of mandatory human verification, this self-referential cycle drives a rapid erosion of pathological variability and diagnostic reliability. By analysing more than 800,000 synthetic data points across clinical text generation, vision-language reporting, and medical image synthesis, we find that models progressively converge toward generic phenotypes regardless of the model architecture. Specifically, rare but critical findings, including pneumothorax and effusions, vanish from the synthetic content generated by AI models, while demographic representations skew heavily toward middle-aged male phenotypes. Crucially, this degradation is masked by false diagnostic confidence; models continue to issue reassuring reports while failing to detect life-threatening pathology, with false reassurance rates tripling to 40%. Blinded physician evaluation confirms that this decoupling of confidence and accuracy renders AI-generated documentation clinically useless after just two generations. We systematically evaluate three mitigation strategies, finding that while synthetic volume scaling fails to prevent collapse, mixing real data with quality-aware filtering effectively preserves diversity. Ultimately, our results suggest that without policy-mandated human oversight, the deployment of generative AI threatens to degrade the very healthcare data ecosystems it relies upon.

Background: Ambulatory oxygen therapy is prescribed for patients with chronic lung diseases who experience exertional hypoxemia. However, available devices may not adequately meet user requirements, and their performance characteristics are heterogeneous. Objective: This study aims to identify devices available for delivery of ambulatory oxygen therapy, the technologies that they use to generate oxygen, the performance characteristics of each device, and the development status. Methods: We used medical and engineering databases to identify peer-reviewed papers (eg, MEDLINE, IEEE). Gray literature was used to identify additional descriptions of ambulatory oxygen devices in military medicine, space exploration, or patents. The last search was conducted in September 2025. Documents that described a device that can deliver oxygen in an ambulatory context (defined as weighing less than 10 kg) and were written in English were included. Search results were screened for inclusion by 2 independent reviewers. Data were synthesized by descriptively mapping the performance of each product, the technology used, and the development status of emerging technologies. Results: From 9702 records identified, a total of 166 met eligibility criteria (106 scientific publications and 60 gray literature). We identified 33 portable oxygen concentrators (POCs; 29 commercially available), 10 oxygen cylinders, and 6 portable liquid oxygen (LOX) devices. The POC products showed a trade-off between portability and oxygen delivery capacity (maximum flow rate ranging from 2.0 to 6.0 L/min; device weight ranging from 1.0 to 9.1 kg). Pressure swing adsorption with zeolite was the most common oxygen generation technology in POCs on the market. The mean maximum continuous operating time of POCs was 3.8 hours. Two prototype POCs (maximum flow rate of 4-6 L/min and device weight of 8-9 kg) were developed for space exploration using modified adsorbents. LOX devices were the lightest and had the longest continuous operating time. Innovations in delivery included the downsizing of a POC by using nanozeolite as an adsorbent and pulse oximeter oxygen saturation (SpO₂)–targeted automatic titration of oxygen delivery based on the user’s SpO₂. Conclusions: This scoping review is the first study to integrate medical, engineering, and gray literature on ambulatory oxygen devices and their development. Although prior literature has narratively explained the products and technologies, no previous research has systematically investigated them. This review showed that POCs available to consumers may not meet the needs of patients in terms of flow rate, portability, and operating time. LOX devices offered superior performance but are limited by high costs. Limitations of this review include the difficulty of comparing product performance across oxygen delivery settings and that the records were largely obtained from English-language sources. Innovation in ambulatory oxygen technology has been limited over the past decade, highlighting urgent need for research and development of new lightweight devices with higher oxygen delivery. Clinical Trial: OSF Registries 10.17605/OSF.IO/QS7FX; https://osf.io/qs7fx

npj Digital Medicine, Published online: 27 January 2026; doi:10.1038/s41746-026-02396-w

Publisher Correction: Best practice recommendations and considerations for designing and electronically implementing event-driven diaries in clinical trials

arXiv:2601.17310v1 Announce Type: new Abstract: Simulation is a powerful tool for exploring uncertainty. Its potential in clinical medicine is transformative and includes personalized treatment planning and virtual clinical trials. However, simulating patient trajectories is challenging because of complex biological and sociocultural influences. Here, we show that real-world clinical records can be leveraged to empirically model patient timelines. We developed a generative simulator model that takes a patient's history as input and synthesizes fine-grained, realistic future trajectories. The model was pretrained on more than 200 million clinical records. It produced high-fidelity future timelines, closely matching event occurrence rates, laboratory test results, and temporal dynamics in real patient future data. It also accurately estimated future event probabilities, with observed-to-expected ratios consistently near 1.0 across diverse outcomes and time horizons. Our results reveal the untapped value of real-world data in electronic health records and introduce a scalable framework for in silico modeling of clinical care.

arXiv:2601.17183v1 Announce Type: cross Abstract: Healthcare institutions have access to valuable patient data that could be of great help in the development of improved diagnostic models, but privacy regulations like HIPAA and GDPR prevent hospitals from directly sharing data with one another. Federated Learning offers a way out to this problem by facilitating collaborative model training without having the raw patient data centralized. However, clinical datasets intrinsically have non-IID (non-independent and identically distributed) features brought about by demographic disparity and diversity in disease prevalence and institutional practices. This paper presents a comprehensive simulation research of Federated Proximal Optimization (FedProx) for Heart Disease prediction based on UCI Heart Disease dataset. We generate realistic non-IID data partitions by simulating four heterogeneous hospital clients from the Cleveland Clinic dataset (303 patients), by inducing statistical heterogeneity by demographic-based stratification. Our experimental results show that FedProx with proximal parameter mu=0.05 achieves 85.00% accuracy, which is better than both centralized learning (83.33%) and isolated local models (78.45% average) without revealing patient privacy. Through generous sheer ablation studies with statistical validation on 50 independent runs we demonstrate that proximal regularization is effective in curbing client drift in heterogeneous environments. This proof-of-concept research offers algorithmic insights and practical deployment guidelines for real-world federated healthcare systems, and thus, our results are directly transferable to hospital IT-administrators, implementing privacy-preserving collaborative learning.

arXiv:2601.17303v1 Announce Type: cross Abstract: As Industrial Internet of Things (IIoT) environments expand to include tens of thousands of connected devices. The centralization of security monitoring architectures creates serious latency issues that savvy attackers can exploit to compromise an entire manufacturing ecosystem. This paper outlines a new, decentralized multi-agent swarm (DMAS) architecture that includes autonomous artificial intelligence (AI) agents at each edge gateway, functioning as a distributed digital "immune system" for IIoT networks. Instead of using a traditional static firewall approach, the DMAS agents communicate via a lightweight peer-to-peer protocol to cooperatively detect anomalous behavior across the IIoT network without sending data to a cloud infrastructure. The authors also outline a consensus-based threat validation (CVT) process in which agents vote on the threat level of an identified threat, enabling instant quarantine of a compromised node or nodes. The authors conducted experiments on a testbed that simulated an innovative factory environment with 2000 IIoT devices and found that the DMAS demonstrated sub-millisecond response times (average of 0.85ms), 97.3% accuracy in detecting malicious activity under high load, and 87% accuracy in detecting zero-day attacks. All significantly higher than baseline values for both centralized and edge computing. Additionally, the proposed architecture can prevent real-time cascading failures in industrial control systems and reduce network bandwidth use by 89% compared to cloud-based solutions.

arXiv:2601.17429v1 Announce Type: cross Abstract: X-ray coronary angiography (XCA) is the clinical reference standard for assessing coronary artery disease, yet quantitative analysis is limited by the difficulty of robust vessel segmentation in routine data. Low contrast, motion, foreshortening, overlap, and catheter confounding degrade segmentation and contribute to domain shift across centers. Reliable segmentation, together with vessel-type labeling, enables vessel-specific coronary analytics and downstream measurements that depend on anatomical localization. From 670 cine sequences (407 subjects), we select a best frame near peak opacification using a low-intensity histogram criterion and apply joint super-resolution and enhancement. We benchmark classical Meijering, Frangi, and Sato vesselness filters under per-image oracle tuning, a single global mean setting, and per-image parameter prediction via Support Vector Regression (SVR). Neural baselines include U-Net, FPN, and a Swin Transformer, trained with coronary-only and merged coronary+catheter supervision. A second stage assigns vessel identity (LAD, LCX, RCA). External evaluation uses the public DCA1 cohort. SVR per-image tuning improves Dice over global means for all classical filters (e.g., Frangi: 0.759 vs. 0.741). Among deep models, FPN attains 0.914+/-0.007 Dice (coronary-only), and merged coronary+catheter labels further improve to 0.931+/-0.006. On DCA1 as a strict external test, Dice drops to 0.798 (coronary-only) and 0.814 (merged), while light in-domain fine-tuning recovers to 0.881+/-0.014 and 0.882+/-0.015. Vessel-type labeling achieves 98.5% accuracy (Dice 0.844) for RCA, 95.4% (0.786) for LAD, and 96.2% (0.794) for LCX. Learned per-image tuning strengthens classical pipelines, while high-resolution FPN models and merged-label supervision improve stability and external transfer with modest adaptation.