arXiv:2511.22619v2 Announce Type: replace Abstract: As intelligence increases, so does its shadow. AI deception, in which systems induce false beliefs to secure self-beneficial outcomes, has evolved from a speculative concern to an empirically demonstrated risk across language models, AI agents, and emerging frontier systems. This project provides a comprehensive and up-to-date overview of the AI deception field, covering its core concepts, methodologies, genesis, and potential mitigations. First, we identify a formal definition of AI deception, grounded in signaling theory from studies of animal deception. We then review existing empirical studies and associated risks, highlighting deception as a sociotechnical safety challenge. We organize the landscape of AI deception research as a deception cycle, consisting of two key components: deception emergence and deception treatment. Deception emergence reveals the mechanisms underlying AI deception: systems with sufficient capability and incentive potential inevitably engage in deceptive behaviors when triggered by external conditions. Deception treatment, in turn, focuses on detecting and addressing such behaviors. On deception emergence, we analyze incentive foundations across three hierarchical levels and identify three essential capability preconditions required for deception. We further examine contextual triggers, including supervision gaps, distributional shifts, and environmental pressures. On deception treatment, we conclude detection methods covering benchmarks and evaluation protocols in static and interactive settings. Building on the three core factors of deception emergence, we outline potential mitigation strategies and propose auditing approaches that integrate technical, community, and governance efforts to address sociotechnical challenges and future AI risks. To support ongoing work in this area, we release a living resource at www.deceptionsurvey.com.

Nature Medicine, Published online: 27 October 2025; doi:10.1038/s41591-025-04006-w

The biological clock model LifeClock predicts biological age across all life stages from routine clinical data, revealing distinct pediatric and adult disease risk patterns.

Background: End-stage kidney disease (ESKD) imposes a significant global health burden, with patients often experiencing poor quality of life (QoL) due to psychological distress and low self-efficacy. Digital health interventions (DHIs) offer potential to address these challenges. However, their effects in this population remain inconsistent, and a comprehensive synthesis of the evidence is lacking. Objective: To assess the impact of DHIs on the psychological health, self-efficacy, and QoL of ESKD patients, and to evaluate engagement, adherence and satisfaction with these interventions. Methods: A comprehensive search was conducted across six electronic databases (PubMed, Web of Science, Cochrane Library, PsycINFO, Embase, and CINAHL) up to January 21, 2025. Randomized controlled trials (RCTs) examining DHIs effects on psychological health, self-efficacy, or QoL in ESKD patients were included. Two reviewers independently screened studies, extracted data, and assessed the risk of bias using the RoB 2 tool. Meta-analysis was performed using Review Manager 5.4, with subgroup analyses by treatment modality, intervention type, and duration. Evidence quality was assessed using the GRADE approach. Results: 23 RCTs involving 2407 ESKD patients from 12 countries were included. DHIs significantly improved depression (SMD: -0.41, 95% CI: [-0.63, -0.19], P=.003) and overall QoL (SMD: 0.55, 95% CI: [0.07, 1.03], P=.03). While DHIs did not significantly improve overall self-efficacy (SMD: 0.56, 95% CI: [-0.06, 1.18], P=.08), a benefit was observed in hemodialysis patients (SMD: 0.59, 95% CI: [0.34, 0.83], P<.001 engagement was favorable with completion rates above adherence of and generally positive patient feedback on dhis. application-based interventions improved self-efficacy ci: p overall qol telemedicine depression video-based due to high heterogeneity risk bias evidence quality rated as low for moderate general anxiety very stress self-efficacy. conclusions: dhis can significantly improve the psychological health eskd patients particularly when tailored needs delivered through interactive platforms such applications telemedicine. suggest good acceptability in clinical practice. however warrants cautious interpretation. future research should involve more high-quality rcts design that address unique elderly peritoneal dialysis kidney transplant recipients. trial: prospero crd42024629357 https:>

Cell Death Discovery, Published online: 10 March 2023; doi:10.1038/s41420-023-01369-2

Ferroptosis, pyroptosis and necroptosis in acute respiratory distress syndrome