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Problems and Barriers Regarding the Admission, Financing, and Service Provision of Digital Health Apps: Qualitative Stakeholder Survey

✇Journal of Medical Internet Research
By: Felix Plescher · Godwin Denk Giebel · Carina Abels · Silke Neusser · Bonnie Kampka · Kirstin Börchers · Jürgen Wasem · Nikola Blase
Background: Since their introduction with the Digital Care Act in 2019, DiGA are a part of the German statutory healthcare system. In order to become a DiGA, mHealth apps have to complete a certification process covering both technical and evidence related aspects. After completion, DiGA are added to the DiGA-directory, containing a list of all reimbursable DiGA within German statutory health insurance (SHI). The first apps were added at the end of 2020 with the number steadily increasing. The novelty of the introduction leads to problems and barriers to optimal use along the way, which is studied from different stakeholder perspectives in this research article. Objective: The aim of the survey was to identify problems and barriers in the context of certification, financing and use of DiGA in Germany. Methods: We used semi-structured expert interviews to evaluate the perspective of stakeholders of the German healthcare system on DiGA. The interview guide was developed according to Helfferich, the interviews were transcribed and analyzed using the qualitative content approach by Mayring and Kuckartz. Results: We identified problems from stakeholder perspectives regarding the certification/admission, financing and service distribution regarding DiGA. The interviewed stakeholders reported problems with authorization of DiGA and the corresponding process. DiGA prices and the different negotiation positions were criticized, as well as financial challenges for smaller DiGA-manufacturers. Within service provision, technical problems, e. g., with activation codes or software surrounding DiGA-prescription were mentioned. Problems were also seen in insufficient knowledge and skills on the side of the patients as well as the medical providers. Conclusions: mHealth applications provide potentially disruptive innovations within the healthcare sector. Nevertheless, since the evidence-based and regulated use of this technology is relatively new there are still problems and barriers limiting the optimized, patient-centered use. This study provides an overview of problems in the context of DiGA in Germany from the stakeholder perspective. Since other countries showed interest in potentially adopting the German system, valuable implications can be drawn from this survey.
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Quantifying Individual Health Status from Multi-omics Data by Health State Manifold

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Xinyan Zhang · Chengming Zhang · Yanpu Wu · Xu Lin · Xiaoping Liu · Luonan Chen

Phenomics. 2025 Dec 15;5(5):469-486. doi: 10.1007/s43657-024-00188-4. eCollection 2025 Oct.

ABSTRACT

Quantifying individual health status from increasingly accumulated omics data is essential for both early prevention and intervention of diseases, which attracts great attention from communities of biology and medicine. Most of the existing approaches mainly classify individuals into different catalogues or classes based on phenotypes and biomarkers. However, an individual's health status from a dynamical systems viewpoint can be viewed as a non-equilibrium steady state, which can generally be characterized by two key features, i.e. (1) homeostatic potential that represents the ability of homeostatic resilience to withstand perturbations or maintain functions at the current state/phenotype of this individual and (2) phenotypic potential that represents the state/phenotype of the individual on the whole process from health to disease. Here, we proposed a health state manifold (HSM) method derived from dynamic network biomarker method and diffusion map theory to quantify individual health status with the characterization of such two features in a robust and accurate manner based on multi-omics data. To verify our method, HSM method was applied to the quantification of diabetes mellitus (rat subjects) and the Roux-en-Y Gastric Bypass (human subjects) for both disease progression process and recovery process, which demonstrated its effectiveness and potential for personalized medicine and preventive medicine.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s43657-024-00188-4.

PMID:41659741 | PMC:PMC12881232 | DOI:10.1007/s43657-024-00188-4

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Spatial Multi-omics Analyses Reveal Diabetes Promotes Pancreatic Cancer Progression by Stimulating Cholesterol-Induced Neutrophil Extracellular Trap Formation

✇(Multiomics OR Omics) AND (Pancreatic)
By: Guanqun Li · Can Zhang · Tianqi Lu · Ziwei Zhang · Jiafu Wu · Rui Bai · Yan Luo · Fengyi Wang · Yiqin Song · Liwei Liu · Jisheng Hu · Yongwei Wang · Gang Wang · Hongtao Tan · Hua Chen · Rui Kong · Le Li · Bei Sun

Cancer Res. 2026 Feb 9. doi: 10.1158/0008-5472.CAN-25-2854. Online ahead of print.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) patients with diabetes mellitus (DM) exhibit poor clinical outcomes. Metabolic reprogramming of both cancer cells and immune compartments plays a crucial role in shaping the anti-tumor immune response in PDAC. DM-induced metabolic alteration may disrupt the intricate crosstalk between immune cells and tumor-associated immune factors, profoundly influencing PDAC progression. Here, we performed an integrated, spatially resolved multi-omics study to investigate DM-associated, cell-specific metabolic remodeling within the PDAC tumor microenvironment. DM influenced interactions between tumor cells and immune cells, which accelerated PDAC growth in both humans and mice. PDAC patients with DM exhibited higher tumor-stage, poorer differentiation, and worse outcomes. Spatial metabolic and transcriptional profiling revealed that SREBP2-dependent cholesterol biosynthesis exacerbated PDAC progression. Increased cholesterol biosynthesis promoted neutrophil recruitment and accelerated formation of neutrophil extracellular traps (NETs) by stimulating the CXCL1-CXCR1/CXCR2 signaling axis, ultimately promoting PDAC growth. Inhibition of SREBP2, pharmacological blockade of CXCL1, or perturbation of NETs markedly reduced PDAC growth in diabetic mouse models. Together, these multi-omics analyses and follow-up mechanistic studies constitute an integrated approach that elucidates a metabolic mechanism by which diabetes promotes PDAC development by remodeling the tumor immune microenvironment and highlights a potential therapeutic strategy for PDAC with DM.

PMID:41661642 | DOI:10.1158/0008-5472.CAN-25-2854

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The challenge of generating and evolving real-life like synthetic test data without accessing real-world raw data -- a Systematic Review

✇cs.AI, q-bio.NC updates on arXiv.org
By: Maj-Annika Tammisto · Faiz Ali Shah · Daniel Rodriguez · Dietmar Pfahl
arXiv:2602.06609v1 Announce Type: cross Abstract: Background: High-level system testing of applications that use data from e-Government services as input requires test data that is real-life-like but where the privacy of personal information is guaranteed. Applications with such strong requirement include information exchange between countries, medicine, banking, etc. This review aims to synthesize the current state-of-the-practice in this domain. Objectives: The objective of this Systematic Review is to identify existing approaches for creating and evolving synthetic test data without using real-life raw data. Methods: We followed well-known methodologies for conducting systematic literature reviews, including the ones from Kitchenham as well as guidelines for analysing the limitations of our review and its threats to validity. Results: A variety of methods and tools exist for creating privacy-preserving test data. Our search found 1,013 publications in IEEE Xplore, ACM Digital Library, and SCOPUS. We extracted data from 75 of those publications and identified 37 approaches that answer our research question partly. A common prerequisite for using these methods and tools is direct access to real-life data for data anonymization or synthetic test data generation. Nine existing synthetic test data generation approaches were identified that were closest to answering our research question. Nevertheless, further work would be needed to add the ability to evolve synthetic test data to the existing approaches. Conclusions: None of the publications really covered our requirements completely, only partially. Synthetic test data evolution is a field that has not received much attention from researchers but needs to be explored in Digital Government Solutions, especially since new legal regulations are being placed in force in many countries.
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Yunjue Agent Tech Report: A Fully Reproducible, Zero-Start In-Situ Self-Evolving Agent System for Open-Ended Tasks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haotian Li · Shijun Yang · Weizhen Qi · Silei Zhao · Rui Hua · Mingzhu Song · Xiaojian Yang · Chao Peng
arXiv:2601.18226v2 Announce Type: replace Abstract: Conventional agent systems often struggle in open-ended environments where task distributions continuously drift and external supervision is scarce. Their reliance on static toolsets or offline training lags behind these dynamics, leaving the system's capability boundaries rigid and unknown. To address this, we propose the In-Situ Self-Evolving paradigm. This approach treats sequential task interactions as a continuous stream of experience, enabling the system to distill short-term execution feedback into long-term, reusable capabilities without access to ground-truth labels. Within this framework, we identify tool evolution as the critical pathway for capability expansion, which provides verifiable, binary feedback signals. Within this framework, we develop Yunjue Agent, a system that iteratively synthesizes, optimizes, and reuses tools to navigate emerging challenges. To optimize evolutionary efficiency, we further introduce a Parallel Batch Evolution strategy. Empirical evaluations across five diverse benchmarks under a zero-start setting demonstrate significant performance gains over proprietary baselines. Additionally, complementary warm-start evaluations confirm that the accumulated general knowledge can be seamlessly transferred to novel domains. Finally, we propose a novel metric to monitor evolution convergence, serving as a function analogous to training loss in conventional optimization. We open-source our codebase, system traces, and evolved tools to facilitate future research in resilient, self-evolving intelligence.
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Data-Centric Interpretability for LLM-based Multi-Agent Reinforcement Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: John Yan · Michael Yu · Yuqi Sun · Alexander Duffy · Tyler Marques · Matthew Lyle Olson
arXiv:2602.05183v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly trained in complex Reinforcement Learning, multi-agent environments, making it difficult to understand how behavior changes over training. Sparse Autoencoders (SAEs) have recently shown to be useful for data-centric interpretability. In this work, we analyze large-scale reinforcement learning training runs from the sophisticated environment of Full-Press Diplomacy by applying pretrained SAEs, alongside LLM-summarizer methods. We introduce Meta-Autointerp, a method for grouping SAE features into interpretable hypotheses about training dynamics. We discover fine-grained behaviors including role-playing patterns, degenerate outputs, language switching, alongside high-level strategic behaviors and environment-specific bugs. Through automated evaluation, we validate that 90% of discovered SAE Meta-Features are significant, and find a surprising reward hacking behavior. However, through two user studies, we find that even subjectively interesting and seemingly helpful SAE features may be worse than useless to humans, along with most LLM generated hypotheses. However, a subset of SAE-derived hypotheses are predictively useful for downstream tasks. We further provide validation by augmenting an untrained agent's system prompt, improving the score by +14.2%. Overall, we show that SAEs and LLM-summarizer provide complementary views into agent behavior, and together our framework forms a practical starting point for future data-centric interpretability work on ensuring trustworthy LLM behavior throughout training.
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Exploring AI-Augmented Sensemaking of Patient-Generated Health Data: A Mixed-Method Study with Healthcare Professionals in Cardiac Risk Reduction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Pavithren V S Pakianathan · Rania Islambouli · Diogo Branco · Albrecht Schmidt · Tiago Guerreiro · Jan David Smeddinck
arXiv:2602.05687v2 Announce Type: replace-cross Abstract: Individuals are increasingly generating substantial personal health and lifestyle data, e.g. through wearables and smartphones. While such data could transform preventative care, its integration into clinical practice is hindered by its scale, heterogeneity and the time pressure and data literacy of healthcare professionals (HCPs). We explore how large language models (LLMs) can support sensemaking of patient-generated health data (PGHD) with automated summaries and natural language data exploration. Using cardiovascular disease (CVD) risk reduction as a use case, 16 HCPs reviewed multimodal PGHD in a mixed-methods study with a prototype that integrated common charts, LLM-generated summaries, and a conversational interface. Findings show that AI summaries provided quick overviews that anchored exploration, while conversational interaction supported flexible analysis and bridged data-literacy gaps. However, HCPs raised concerns about transparency, privacy, and overreliance. We contribute empirical insights and sociotechnical design implications for integrating AI-driven summarization and conversation into clinical workflows to support PGHD sensemaking.
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Reliability of LLMs as medical assistants for the general public: a randomized preregistered study

✇Nature Medicine
By: Andrew M. Bean · Rebecca Elizabeth Payne · Guy Parsons · Hannah Rose Kirk · Juan Ciro · Rafael Mosquera-Gómez · Sara Hincapié M · Aruna S. Ekanayaka · Lionel Tarassenko · Luc Rocher · Adam Mahdi

Nature Medicine, Published online: 09 February 2026; doi:10.1038/s41591-025-04074-y

In a randomized controlled study involving 1,298 participants from a general sample, performance of humans when assisted by a large language model (LLM) was sensibly inferior to that of the LLM alone when assessing ten medical scenarios leading to disease identification and recommendations for treatment.
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Integrating liquid biopsies in non-small cell lung cancer diagnosis and management: opportunities and challenges

✇MRD
By: Jeong Uk Lim · Yuko Oya · Rui Fu · Kenneth Sooi · Yvonne L E Ang · Ross A Soo

Expert Rev Anticancer Ther. 2026 Feb 15:1-12. doi: 10.1080/14737140.2026.2630026. Online ahead of print.

ABSTRACT

INTRODUCTION: Liquid biopsy has emerged as an important approach to capture tumor-derived material from blood and other body fluids, offering a minimally invasive window into cancer biology. In non - small cell lung cancer (NSCLC), it enables comprehensive molecular profiling that informs patient management, from guiding therapy choices to monitoring disease status and assessing minimal residual disease (MRD).

AREAS COVERED: Its main advantages over tissue biopsy lie in being noninvasive, capable of reflecting tumor heterogeneity and real-time biological changes. These strengths allow liquid biopsy to be applied at different clinical timepoints, including diagnosis, treatment decision-making, evaluation during therapy, detection of resistance, and surveillance for recurrence. Although circulating tumor DNA (ctDNA) remains the most established analyte, the scope is broadening to include circulating RNAs, circulating tumor cells, exosomes, DNA methylation signatures, and tumor-educated platelets, each providing complementary insights. A literature search of PubMed, EMBASE, and Web of Science was conducted without restrictions, supplemented by screening reference lists and major oncology conference abstracts.

EXPERT OPINION: While significant progress has been made integrating liquid biopsies in NSCLC, challenges persist, encompassing issues of standardization, cost, and clinical integration.

PMID:41656166 | DOI:10.1080/14737140.2026.2630026

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From Amphiphiles to mRNA platforms: emerging vaccination strategies for pancreatic cancer

✇(Multiomics OR Omics) AND (Pancreatic)
By: Dong Gun Lee · Kyunghee Noh

Exp Hematol Oncol. 2026 Feb 7. doi: 10.1186/s40164-026-00755-7. Online ahead of print.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) remains among the deadliest cancers, with limited surgical eligibility, modest chemotherapy benefit, and resistance to immune checkpoint blockade. Two recent vaccine platforms have shown encouraging results. Wainberg et al. demonstrated that the amphiphile vaccine ELI-002 efficiently traffics to lymph nodes via albumin binding and induced KRAS-specific T-cell responses in most patients, correlating with survival. In parallel, Sethna et al. reported that an individualized uridine-modified mRNA vaccine elicited durable, polyfunctional CD8⁺ T cells with long-term persistence, especially when combined with PD-1 blockade. Amphiphiles provide rapid and efficient priming, whereas mRNA vaccines broaden and sustain clonotypic diversity. A hybrid prime-boost strategy may synergize these complementary mechanisms, while advances in multi-omics and AI-driven neoantigen prediction pave the way for personalized designs. Together, these developments suggest that PDAC, long regarded as immunologically "cold," may become tractable to vaccination strategies. Importantly, these findings are based on early-phase clinical studies with limited patient numbers and should therefore be interpreted as preliminary clinical evidence requiring further studies.

PMID:41654971 | DOI:10.1186/s40164-026-00755-7

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The Feasibility of Smartwatch Micro–Ecological Momentary Assessment for Tracking Eating Patterns of Malaysian Children and Adolescents in the South-East Asian Community Observatory Child Health Update 2020: Cross-Sectional Study

✇Journal of Medical Internet Research
By: Richard Lane · Louise A C Millard · Ruth Salway · Chris J Stone · Andy L Skinner · Sophia M Brady · Jeevitha Mariapun · Sutha Rajakumar · Amutha Ramadas · Hussein Rizal · Laura Johnson · Tin Tin Su · Miranda Elaine Glynis Armstrong
Background: Mobile phone ecological momentary assessment (EMA) methods are a well-established measure of eating and drinking behaviors, but compliance can be poor. Micro-EMA (μEMA), which collects information with a single tap response to brief questions on smartwatches, offers a novel application that may improve response rates. To our knowledge, there is no data evaluating μEMA to measure eating habits in children or in low-to-middle-income countries. Objective: In this study, we investigated the feasibility of micro-EMA to measure eating patterns in Malaysian children and adolescents. Methods: We invited 100 children and adolescents aged 7-18 years in Segamat, Malaysia, to participate in 2021-2022. Smartwatches were distributed to 83 children and adolescents who agreed to participate. Participants were asked to wear the smartwatch for 8 days and respond to 12 prompts per day, hourly, from 9AM to 8PM, asking for information on their meals, snacks, and drinks consumed. A questionnaire captured their experiences using the smartwatch and μEMA interface. Response rate (proportion of prompts responded to) assessed participants’ adherence. We explored associations between response rate with time of day, across days, age, and sex using multilevel binomial logistic regression modeling. Results: Eighty-two participants provided usable smartwatch data. The median number (IQR) of meals, drinks, and snacks per day was 2 (2-4), 3 (1-5), and 1 (0-2), respectively, on the first day of the study. The median response rate across the study was 68% (IQR 50-83). The response rate decreased across study days from 74% (68-78) on Day 1 to 40% (30-50) on Day 7 (odds ratio [OR] per study day 0.73, 95% CI 0.64-0.83). Response rate was lowest at the start of the day and highest between the hours of 12 PM and 2 PM. Female participants responded to more prompts than male participants (OR 1.72, 95% CI 1.03-2.86). There was no evidence of differential response by age (OR 0.73, 95% CI 0.41-1.28). Most participants (65%) rated their experience using the smartwatch positively, with 33% saying they were happy to participate in future studies using the smartwatch. For children that did not wear the smartwatch for the full study duration (n=22), discomfort was the most common complaint (41%). Conclusions: In this study of the feasibility of μEMA on smartwatches to measure eating in Malaysian children, we found the method was acceptable. However, response rates declined across study days, resulting in substantial missingness. Future studies (eg, through focus groups) should explore approaches to improving response to event prompts, trial alternative devices to increase children’s comfort, and evaluate revised protocols for reporting of intake events.
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Digital Biomarkers for Precision Early Detection of Lung Cancer: Integrating AI-Driven Multi-Omics Into Clinical Pathways

✇Omics In Lung
By: Fan Bu · Zhi-Qiang Ling

Cancer Med. 2026 Feb;15(2):e71578. doi: 10.1002/cam4.71578.

ABSTRACT

BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide, highlighting the urgent need for earlier detection within real-world screening and patient management pathways. Recent advances in multi-omics technologies have created new opportunities for identifying biomarkers associated with early-stage lung cancer, particularly in high-risk populations under clinical surveillance.

METHODS: This review systematically evaluates early diagnostic biomarkers across multiple omics layers, including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics. It also summarises the application of artificial intelligence (AI), particularly machine learning and deep learning approaches, for integrating and analysing complex multi-omics datasets to support biomarker discovery and clinical decision-making.

RESULTS: Multi-omics strategies are accelerating the identification of molecular signatures relevant to early lung cancer detection. AI-driven methods enable the extraction of latent patterns from high-dimensional data, facilitating risk stratification, diagnostic refinement, histological subtyping and treatment planning. The review highlights the clinical utility of these biomarkers and their potential incorporation into screening algorithms, as well as the development of AI-based clinical decision support systems (CDSS) aligned with real-world clinical workflows. However, major barriers to clinical translation remain, including multi-centre data heterogeneity, limited model interpretability affecting clinical trust, regulatory and cost-effectiveness challenges and insufficient validation in prospective cohorts.

CONCLUSIONS: Emerging technologies, such as single-cell and spatial multi-omics, along with federated learning frameworks, offer promising solutions to bridge the gap between computational discovery and clinical implementation. The integration of AI and multi-omics approaches has the potential to advance risk-adapted and personalised early detection strategies for lung cancer.

PMID:41645653 | PMC:PMC12877424 | DOI:10.1002/cam4.71578

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Tumor microbiome differences in early-onset versus average-onset pancreatic adenocarcinoma

✇(Multiomics OR Omics) AND (Pancreatic)
By: T Jayakrishnan · N Sangwan · K G Nair · S D Kamath · M H Patel · D Joyce · M Walsh · R Simon · D Vadehra · R V Iyer · C Fountzilas · A A Khorana

ESMO Gastrointest Oncol. 2025 Jul 7;9:100194. doi: 10.1016/j.esmogo.2025.100194. eCollection 2025 Sep.

ABSTRACT

BACKGROUND: Compelling evidence supports the biomarker potential of microbiome in pancreatic adenocarcinoma. Given the knowledge gap on the characteristics and significance of microbiome in early-onset pancreatic ductal adenocarcinoma (eoPDAC, age <50 years), we aimed to evaluate microbiome profiles in resected specimens from individuals with eoPDAC and average-onset PDAC (aoPDAC, age >50 years).

MATERIALS AND METHODS: We carried out shotgun metagenomic sequencing in resected specimens from individuals with eoPDAC (n = 24) and aoPDAC (n = 20). Statistical tests included Wilcoxon test, permutational analysis of variance, multiomic classifier modeling, differential abundance analysis, and linear regression. All P values were adjusted for multiple testing and P < 0.05 was considered statistically significant.

RESULTS: We successfully sequenced several bacteria and fungi in the tumor specimens from 44 individuals with resected PDAC (24 eoPDAC and 20 aoPDAC). The alpha diversity of the bacterial microbiome was higher in eoPDAC tumor tissue compared with aoPDAC (P = 0.04). In contrast, the fungal mycobiome's alpha diversity was higher for aoPDAC tumor tissue (P = 0.02). Key organisms with differential abundance between tumor tissue from individuals with eoPDAC and aoPDAC included Bacillus, Candida, Collimonas, Cupriavidus, Enterobacter, Escherichia, Klebsiella, Malasseiza, Mucilaginibacter, Neisseria, and Sphingomonas. Higher bacterial diversity in tumor tissue was associated with better overall survival for individuals with eoPDAC (R = 0.26, P = 0.02).

CONCLUSIONS: Shotgun metagenomic sequencing identified bacterial microbiome and fungal mycobiome in tumors from individuals with eoPDAC and aoPDAC. We observed significant differences in alpha and beta diversity and relative abundances of organisms suggesting distinct microbiome signatures. Microbiome associations with survival were observed in eoPDAC indicating unique potential as prognostic biomarker.

PMID:41647993 | PMC:PMC12836659 | DOI:10.1016/j.esmogo.2025.100194

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EcDNA-borne structural variants drive oncogenic fusion transcript amplification

✇Cell
By: Hyerim Yi · Shu Zhang · Jason Swinderman · Yanbo Wang · Vishnupriya Kanakaveti · King L. Hung · Ivy Tsz-Lo Wong · Suhas Srinivasan · Ellis J. Curtis · Aarohi Bhargava-Shah · Rui Li · Matthew G. Jones · Jens Luebeck · Chris Bailey · Yanding Zhao · Julia A. Belk · Katerina Kraft · Quanming Shi · Xiaowei Yan · Simon K. Pritchard · Kabir S. Mahajan · Frances Liang · Mariam Jamal-Hanjani · Dean W. Felsher · Luke A. Gilbert · Vineet Bafna · Paul S. Mischel · Howard Y. Chang
Extrachromosomal DNA (ecDNA) is a major source of oncogenic fusions across cancer types, generating tissue-specific fusion landscapes with diagnostic potential. EcDNA-borne PVT1 5′-end fusions stabilize partner RNAs and boost oncogene output.
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A Human-Centered Privacy Approach (HCP) to AI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Luyi Sun · Wei Xu · Zaifeng Gao
arXiv:2602.04616v1 Announce Type: cross Abstract: As the paradigm of Human-Centered AI (HCAI) gains prominence, its benefits to society are accompanied by significant ethical concerns, one of which is the protection of individual privacy. This chapter provides a comprehensive overview of privacy within HCAI, proposing a human-centered privacy (HCP) framework, providing integrated solution from technology, ethics, and human factors perspectives. The chapter begins by mapping privacy risks across each stage of AI development lifecycle, from data collection to deployment and reuse, highlighting the impact of privacy risks on the entire system. The chapter then introduces privacy-preserving techniques such as federated learning and dif erential privacy. Subsequent chapters integrate the crucial user perspective by examining mental models, alongside the evolving regulatory and ethical landscapes as well as privacy governance. Next, advice on design guidelines is provided based on the human-centered privacy framework. After that, we introduce practical case studies across diverse fields. Finally, the chapter discusses persistent open challenges and future research directions, concluding that a multidisciplinary approach, merging technical, design, policy, and ethical expertise, is essential to successfully embed privacy into the core of HCAI, thereby ensuring these technologies advance in a manner that respects and ensures human autonomy, trust and dignity.
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Let Experts Feel Uncertainty: A Multi-Expert Label Distribution Approach to Probabilistic Time Series Forecasting

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhen Zhou · Zhirui Wang · Qi Hong · Yunyang Shi · Ziyuan Gu · Zhiyuan Liu
arXiv:2602.04678v1 Announce Type: cross Abstract: Time series forecasting in real-world applications requires both high predictive accuracy and interpretable uncertainty quantification. Traditional point prediction methods often fail to capture the inherent uncertainty in time series data, while existing probabilistic approaches struggle to balance computational efficiency with interpretability. We propose a novel Multi-Expert Learning Distributional Labels (LDL) framework that addresses these challenges through mixture-of-experts architectures with distributional learning capabilities. Our approach introduces two complementary methods: (1) Multi-Expert LDL, which employs multiple experts with different learned parameters to capture diverse temporal patterns, and (2) Pattern-Aware LDL-MoE, which explicitly decomposes time series into interpretable components (trend, seasonality, changepoints, volatility) through specialized sub-experts. Both frameworks extend traditional point prediction to distributional learning, enabling rich uncertainty quantification through Maximum Mean Discrepancy (MMD). We evaluate our methods on aggregated sales data derived from the M5 dataset, demonstrating superior performance compared to baseline approaches. The continuous Multi-Expert LDL achieves the best overall performance, while the Pattern-Aware LDL-MoE provides enhanced interpretability through component-wise analysis. Our frameworks successfully balance predictive accuracy with interpretability, making them suitable for real-world forecasting applications where both performance and actionable insights are crucial.
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DISCOVER: Identifying Patterns of Daily Living in Human Activities from Smart Home Data

✇cs.AI, q-bio.NC updates on arXiv.org
By: Alexander Karpekov, Archith Iyer, Sourish Gunesh Dhekane, Sonia Chernova, Thomas Pl\"otz
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.
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Phenome-wide analysis of copy number variants in 470,727 UK Biobank genomes

✇Nature - Issue - nature.com science feeds
By: Xueqing Zoe Zou · Fengyuan Hu · Haiyi Lou · Oliver S. Burren · Xiaoyin Li · Karyn Megy · Eleanor Wheeler · Qiang Wu · Santosh S. Atanur · Marcin Karpinski · Douglas Loesch · Zammy Fairhurst-Hunter · Sri V. V. Deevi · Erin Oerton · Sean Wen · Xiao Jiang · Cecilia Salvoro · Jonathan Mitchell · Abhishek Nag · Ben Hollis · Amanda O’Neill · Lauren Anderson-Dring · Mohammad Bohlooly-Y · Lisa Buvall · Sophia Cameron-Christie · Bram Prins · Suzanne Cohen · Regina F. Danielson · Andrew Davis · Wei Ding · Brian Dougherty · Manik Garg · Benjamin Georgi · Andrew Harper · Carolina Haefliger · Mårten Hammar · Richard N. Hanna · Ian Henry · Kousik Kundu · Zhongwu Lai · Mark Lal · Glenda Lassi · Yupu Liang · Margarida Lopes · Kieren Lythgow · Meeta Maisuria-Armer · Ruth March · Dorota Matelska · Rob Menzies · Erik Michaëlsson · Bill Mowrey · Daniel Muthas · Yoichiro Ohne · Benjamin Pullman · Sonja Hess · Arwa Raies · Anna Reznichenko · Xavier Romero Ros · Helen Stevens · Ioanna Tachmazidou · Coralie Viollet · Anna Walentinsson · Lily Wang · Qing-Dong Wang · Anna Cuomo · Daniel Elias

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.
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