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Validation of the Updated Digital Health Literacy Instrument and Development of a Short Form: Online Survey Study of the General Population

✇Journal of Medical Internet Research
By: Rosalie van der Vaart · Inge Tuitert · Job van 't Veer · Constance Drossaert
Background: The digital health literacy instrument (DHLI) was developed in 2017 to measure individuals’ ability to access, understand, evaluate, and apply online health information. Since that time, digital health has shifted from desktop-based internet use to mobile devices, and there has been a rapidly expanding range of health apps. Additionally, heightened privacy and data security requirements have increased the complexity of user competencies needed to engage with digital health tools. These developments underscore the need to update the original DHLI. Objective: This study aimed to create an updated version of the DHLI (DHLI 2.0) that reflects current digital health practices and to examine its reliability and validity by exploring associations with user characteristics. Additionally, we aimed to develop a short-form version to facilitate broader use in research and practice. Methods: The instrument was iteratively updated and pilot-tested to retain the original theoretical framework while reflecting current digital health practices, devices, and emerging challenges such as mobile use and data security. Several items were reworded and a new 2-item subscale on digital safety was added. The full DHLI 2.0 comprises 24 items across 8 skill domains. A 16-item short form was developed by iteratively removing 1 or 2 items per subscale based on the “α if item deleted” criterion, while retaining the same subscale structure as the full form. Data to validate the new version of the instrument were collected in June 2024 through an online survey among members of a representative citizen panel in Friesland, a province in the Netherlands (N=2728). Sociodemographics, internet and health-related internet use, general health literacy (measured with the Single Item Literacy Screener), self-reported health, and health care use were assessed. Internal consistency was evaluated using Cronbach α, and construct validity was assessed via Spearman ρ correlations with related constructs. Results: Internal consistency was high for both the full (α=0.94) and short-form (α=0.90) scales. Most subscales showed satisfactory to excellent reliability (α=0.71–0.93), while “Securing privacy” and “Using security measures” demonstrated moderate reliability (α=0.65-0.66). The DHLI 2.0 total scores were approximately normally distributed (skewness –0.5; kurtosis 0.4). As expected, digital health literacy was negatively correlated with age (ρ=−0.39,
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A viable human lung cancer tissue collection (LCTC) to accelerate translational research

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Virginia Guzzeloni · Alessandra Bartolucci · Samuele Valci · Federica Pedica · Pierluigi Novellis · Piergiorgio Muriana · Francesca Rita Ogliari · Silvia Teresa Riva · Nerina Shahi · Arianna Limongelli · Laura Bettini · Francesca Rossetti · Stefano Viscardi · Carlo Di Giovannantonio · Francesca Misceo · Cinzia Scala · Riccardo Di Fonzo · Alberto Antonicelli · Alessandro Bandiera · Martina Bellantoni · Simona De Santis · Elisa Dieci · Angelo Carretta · Paola Ciriaco · Pau Mateo-Ramos · Maria Giulia Cangi · Emanuela Brunetto · Lorenza Pecciarini · Gilda Magliacane · Gianluigi Arrigoni · Claudio Doglioni · Roberto Ferrara · Sara Oresti · Maria Grazia Viganò · Antonio Nuccio · Francesco Maria Venanzi · Ludovica Mollica · Giuseppe Damiano · Elena Brioschi · Michele Ferrara · Sara Guarnieri · Giacomo Lampertico · Nicola Fogale · Michele Colombo · Diego Palumbo · Domenico Ghio · Francesco De Cobelli · Maurilio Ponzoni · Giampiero Negri · Michele Reni · Alessandra Bulotta · Giulia Veronesi · Giorgia Foggetti

Cancer Treat Res Commun. 2026 Mar 2;47:101162. doi: 10.1016/j.ctarc.2026.101162. Online ahead of print.

ABSTRACT

BACKGROUND: The collection of clinical data and patient tumor specimens in institutional repositories is essential to accelerate translational research in lung cancer, linking laboratory findings with patient outcomes. These resources allow investigators to explore tumor heterogeneity, analyze therapeutic profiles and, more recently, generate patient-derived models of cancer. Given the plethora of therapies in clinical use or under investigation, it is critical to establish tissue collection programs that support the identification of predictive biomarkers of drug sensitivity to define patient subgroups that may benefit from tailored therapeutic strategies. However, access to high-quality viable specimens remains limited.

METHODS: We established a multidisciplinary program -the Lung Cancer Tissue Collection (LCTC) study- to prospectively collect viable human specimens and clinical data. Samples can be collected post-diagnosis and at multiple treatment time points, preserving material for future studies.

RESULTS: In the first 24 months of the LCTC study, we enrolled 158 patients and collected over 700 specimens from patients with lung cancer. EGFR and KRAS mutations were the most frequently identified oncogenic drivers, mirroring frequencies reported in public datasets. We achieved a 60 % success rate in cryopreservation -measured by the proportion of patient-derived organoids growing after tissue thawing and processing- highlighting the feasibility of our program.

CONCLUSIONS: The LCTC biobank captures the molecular and clinical diversity of lung cancer, providing a clinically annotated resource of viable tissue and longitudinal blood specimens. This platform enables patient-derived modeling and multi-omic and functional studies to investigate tumor biology, treatment response, and resistance, supporting biomarker discovery and precision medicine.

PMID:41797251 | DOI:10.1016/j.ctarc.2026.101162

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Clinical development of molecular residual disease (MRD) and multi-cancer early detection (MCED) using liquid biopsy multiomics with artificial intelligence (AI)

✇(Multiomics OR Omics) AND (Pancreatic)
By: Taro Shibuki · Riu Yamashita · Tadayoshi Hashimoto · Takao Fujisawa · Mitsuho Imai · Junichiro Yuda · Takeshi Kuwata · Toshihiro Misumi · Yoshiaki Nakamura · Hideaki Bando · Kaname Kojima · Sayuri Tokioka · Ippei Chiba · Naoki Nakaya · Atsushi Hozawa · Seizo Koshiba · Nobuo Fuse · Sakae Saito · Ritsuko Shimizu · Woong-Yang Park · Kengo Kinoshita · Takayuki Yoshino

Int J Clin Oncol. 2026 Mar 6. doi: 10.1007/s10147-026-03001-6. Online ahead of print.

ABSTRACT

BACKGROUND: Early detection of cancer and precise recurrence monitoring remain major unmet needs in oncology. Conventional screening is limited to a few cancer types, leaving nearly half of cancers without established programs. Multi-cancer early detection (MCED) tests based on circulating tumor biomarkers have shown promise, but sensitivity for early-stage remains a challenge. In parallel, detection of molecular residual disease (MRD) using circulating tumor DNA (ctDNA) has emerged as a powerful prognostic and predictive tool, though current assays remain limited in sensitivity and specificity. This study aims to integrate multi-omics data to develop more refined and highly sensitive MCED and MRD assays.

METHODS: This study leverages clinical information and biospecimens from patients with cancer and cancer-naïve individuals. Samples from patients with cancers will be derived from the MONSTAR-SCREEN-3 study, while those from cancer-naïve individuals will be obtained from the Tohoku Medical Megabank Project. Comprehensive analyses will include whole-genome sequencing (WGS), whole-exome sequencing (WES), whole-transcriptome sequencing (WTS), proteomics, metabolomics, and microbiome profiling using stool and saliva. Artificial intelligence (AI)-based multi-omics integration will be performed to develop novel MCED and MRD assays and to evaluate their clinical performance. The primary endpoints are the sensitivity and specificity of MCED and MRD assays.

DISCUSSION: This is the first large-scale study to integrate comprehensive multi-omics profiling with AI for MCED and MRD assay development. The findings are expected to advance precision oncology by improving early diagnosis and recurrence monitoring.

TRIAL REGISTRATION: UMIN000053815, approved by the Institutional Review Board of the National Cancer Center Hospital East.

PMID:41790338 | DOI:10.1007/s10147-026-03001-6

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Interorganizational Mechanisms for Developing and Implementing Clinical Decision Support Systems in Primary Care: Exploratory, Qualitative Case Study

✇Journal of Medical Internet Research
By: Jesse J M M Santema · Jeroen D H van Wijngaarden · Eric G Hiddink · Fleur Deken · Maaike Kleinsmann · Hendrikus J A van Os
Background: Clinical decision support systems (CDSS) have the potential to improve patient safety and reduce costs in primary care. However, CDSS adoption remains limited due to development and implementation challenges. CDSSs are complex interventions involving multiple interacting components that require technological innovation and behavioral and organizational change. Additionally, the primary care context is considered a complex system with high care demand, fragmented structures, and many independent yet interdependent organizations. Established determinant frameworks for implementing and scaling up complex health care interventions support the identification of implementation determinants. However, they offer limited guidance on the underlying processes of these determinants, such as the implementation processes involved in complex interorganizational collaboration in primary care. Objective: This study examined how an interorganizational collaboration in Dutch primary care ( []) achieved an iterative CDSS development and implementation. We aimed to identify the mechanisms that supported the collaboration in overcoming challenges. Methods: We performed an exploratory process-level case study. Data were collected through 15 semistructured interviews. The nonadoption, abandonment, scale-up, spread, and sustainability framework was used to ensure comprehensive topic coverage during the interviews, but not as an analytical framework. We triangulated the interviews with internal and external documents and expert input. Using a thematic, inductive approach, we developed a chronological overview of the collaboration and identified mechanisms offering insights into how GzGr navigated complexity in the development and implementation of CDSS. Results: We identified two mechanisms: (1) enacting an interorganizational value model and (2) iterative, co-creative experimentation. First, GzGr was driven by a coalition of the willing (ie, individuals willing to take an extra step), with shared goals that prioritized collective benefit while respecting organizational values. They established shared principles that translated the broad GzGr mission into concrete CDSS development choices, while also guiding strategic expansion by involving mission-aligned, innovative organizations. Second, after initial prototypes, GzGr established an iterative learning and improvement experimentation for both the technology and the collaboration. This process allowed for rapid feedback, validation of added value, and ongoing refinement. Additionally, this experimentation approached the development and implementation phase as a continuous process involving multistakeholders, supporting both the technology and the collaboration. Conclusions: This study identified 2 mechanisms that sustained interorganizational collaboration and CDSS development. These mechanisms connected collaborative and technical changes across people, technology, and organizational levels, enabling technological viability, stakeholder value, and multilevel support. The mechanisms operated both within and between organizations through iterative cycles of development and implementation. Practical implications include involving multilevel, innovative, and influential stakeholders; maintaining alignment through an orchestrating actor; and adopting an iterative approach between development and implementation. Our findings extend existing determinant frameworks by offering process-level insights into how such mechanisms help overcome challenges in the development and implementation of CDSS within interorganizational collaborations.
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AI Triage in Primary Care: Building Safer and More Equitable Real-World Evidence

✇Journal of Medical Internet Research
By: Aymn Alamoudi · Evangelos Kontopantelis · Salwa S Zghebi · Benjamin Brown
Artificial intelligence triage in general practice is developing rapidly within the primary care digital transformation, promising efficiency gains and safety standardization in overwhelmed primary care systems. However, current evidence is drawn from retrospective validations, emergency settings, or vignettes, with scant evaluation of real-world outcomes and almost no equity-stratified safety data, despite known disparities across age, ethnicity, language, and deprivation. From a sociotechnical standpoint, which considers the fit between people, tasks, technology, and organizational context, risks arise not only from algorithmic bias and undertriage but also from human factors, workflow misalignment, governance gaps, and inadequate postdeployment monitoring. We argue that ensuring artificial intelligence triage is safe and equitable requires real-world evaluations in primary care settings, equity-focused performance reporting using theoretically informed frameworks, and rigorous postmarket surveillance. Without these, deployment may widen existing health inequalities rather than moderate them.
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Mental Health Professionals’ Perceptions of Benefits and Disadvantages of Telehealth: International Mixed Methods Study

✇Journal of Medical Internet Research
By: Madeline I Montoya · Cary S Kogan · Brigitte Khoury · José A García-Pacheco · Rebeca Robles · Tahilia J Rebello · Maya Kulygina · Geoffrey M Reed
Background: Telehealth has become an integral component of mental health care delivery worldwide. Understanding provider perceptions is essential to guiding its continued implementation. Objective: This international study used quantitative and qualitative methodologies to examine and broaden our understanding of the benefits and concerns related to telehealth for mental health care. Methods: An internet-based survey was conducted during the COVID-19 pandemic between November 11 and December 18, 2020, among mental health professionals, primarily psychiatrists and psychologists, registered with the World Health Organization’s Global Clinical Practice Network. Clinicians completed the survey in 1 of 6 languages (Chinese, English, French, Japanese, Russian, or Spanish). Descriptive statistics and logistic regressions were used to analyze quantitative survey data on concerns and implementation of telehealth. Responses to an open-ended question about providers’ perspectives on the benefits of telehealth were analyzed qualitatively. Results: In total, 847 participants completed the telehealth section of the survey, and 496 provided a response to the open-ended question. Quantitative data on telehealth use and concerns revealed that clinicians’ primary concerns focused on technical issues and clinical effectiveness relative to in-person services, specifically, loss of clinical information (eg, nonverbal behavior) and challenges with establishing a therapeutic alliance. Findings varied by profession, World Health Organization region, and telehealth training and experience. Qualitative data examining benefits fell into 3 major areas: accessibility and reach of mental health services, efficiency and flexibility for clinicians, and enhancement of clinical processes and outcomes. Taken together, findings revealed a trade-off between telehealth benefits and disadvantages. Conclusions: From the perspective of mental health professionals, telehealth practice comes with key challenges and valuable benefits. Findings offer important considerations for the implementation of telehealth systems, including the importance of training and education and balancing trade-offs to optimize care.
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Architecting Trust in Artificial Epistemic Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Nahema Marchal · Stephanie Chan · Matija Franklin · Manon Revel · Geoff Keeling · Roberta Fischli · Bilva Chandra · Iason Gabriel
arXiv:2603.02960v1 Announce Type: new Abstract: Large language models increasingly function as epistemic agents -- entities that can 1) autonomously pursue epistemic goals and 2) actively shape our shared knowledge environment. They curate the information we receive, often supplanting traditional search-based methods, and are frequently used to generate both personal and deeply specialized advice. How they perform these functions, including whether they are reliable and properly calibrated to both individual and collective epistemic norms, is therefore highly consequential for the choices we make. We argue that the potential impact of epistemic AI agents on practices of knowledge creation, curation and synthesis, particularly in the context of complex multi-agent interactions, creates new informational interdependencies that necessitate a fundamental shift in evaluation and governance of AI. While a well-calibrated ecosystem could augment human judgment and collective decision-making, poorly aligned agents risk causing cognitive deskilling and epistemic drift, making the calibration of these models to human norms a high-stakes necessity. To ensure a beneficial human-AI knowledge ecosystem, we propose a framework centered on building and cultivating the trustworthiness of epistemic AI agents; aligning AI these agents with human epistemic goals; and reinforcing the surrounding socio-epistemic infrastructure. In this context, trustworthy AI agents must demonstrate epistemic competence, robust falsifiability, and epistemically virtuous behaviors, supported by technical provenance systems and "knowledge sanctuaries" designed to protect human resilience. This normative roadmap provides a path toward ensuring that future AI systems act as reliable partners in a robust and inclusive knowledge ecosystem.
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The Transformative Potential of Liquid Biopsies and Circulating Tumor DNA (ctDNA) in Modern Oncology

✇MRD
By: Keren Rouvinov · Rashad Naamneh · Alexander Yakobson · Wenad Najjar · Mahmoud Abu Amna · Arina Soklakova · Ez El Din Abu Zeid · Ronen Brenner · Mohnnad Asla · Fahmi Abu Ghalion · Ali Abu Juma'a · Amichay Meirovitz · Walid Shalata

Diagnostics (Basel). 2026 Feb 9;16(4):523. doi: 10.3390/diagnostics16040523.

ABSTRACT

Background: Liquid biopsy, particularly through the analysis of circulating tumor DNA (ctDNA), represents a significant advancement in oncology. Unlike traditional tissue biopsies, ctDNA offers a minimally invasive, real-time approach to cancer management. It has demonstrated considerable potential in early cancer detection, monitoring of therapeutic responses, and assessing minimal residual disease (MRD) to predict recurrence. By enabling comprehensive molecular profiling through a simple blood test, ctDNA supports the core principles of precision oncology, facilitating more personalized and adaptive treatment strategies. Methods: In the following article we describe the recent developments focused on refining ctDNA detection assays to improve sensitivity and specificity. Advanced technologies, including next-generation sequencing (NGS) and digital PCR, are commonly employed. The integration of artificial intelligence (AI) and multi-omics approaches-such as combining genomic, epigenomic, and transcriptomic data-has further enhanced the analytical power of ctDNA assays. Results: Emerging evidence shows that ctDNA-based liquid biopsy enables dynamic, real-time tracking of tumor evolution and therapeutic resistance. Clinical studies have demonstrated its efficacy in detecting early-stage cancers, guiding treatment selection, and predicting relapse with higher accuracy than some conventional methods. Moreover, AI-enhanced algorithms have improved signal detection, allowing for more precise and earlier identification of actionable mutations and MRD. Conclusions: ctDNA analysis via liquid biopsy is poised to revolutionize cancer care by offering a non-invasive, precise, and adaptive tool for tumor characterization and monitoring. Although obstacles remain-particularly regarding assay sensitivity, standardization, and economic feasibility-ongoing technological innovations and multi-omics integration are rapidly advancing its clinical viability. With continued progress, ctDNA-based liquid biopsy is likely to become a cornerstone of routine oncology practice.

PMID:41750672 | PMC:PMC12938931 | DOI:10.3390/diagnostics16040523

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3D, multi-omic imaging reveals molecular biomarkers of the pre-metastatic niche in lung cancer

✇Omics In Lung
By: John Michel · André Forjaz · Vasco Queiroga · Kelly Casella · Kaitlin Stivers · Helen Nguyen · Maria Browne · Feiyu Chen · Ada Tam · Omkar Dhaygude · Hongni Fan · Hitoaki Maehira · Cheng Ting Lin · Elise Gray-Gaillard · Talia L Benducci · Suguru Yamauchi · Ife Shoyombo · Ziying Xu · Eugene Shenderov · Peng Huang · Denis Wirtz · Jennifer H Elisseeff · Yun Chen · Malcolm V Brock · Ashley L Kiemen · Franck Housseau

bioRxiv [Preprint]. 2026 Feb 18:2026.02.18.706515. doi: 10.64898/2026.02.18.706515.

ABSTRACT

The recurrence rate following complete surgical resection of primary non-small cell lung cancer is as high as 55%, yet no approach currently exists to evaluate the risk of local recurrence. The premetastatic paradigm is the recognition that metastasis is preceded by reprogramming naïve tissues to prime a microenvironment for tumor cell survival and subsequent reactivation. Identification of biomarkers of the pre-metastatic niche would allow us to evaluate a patient's risk of local relapse in the normal lung parenchyma surrounding the resected tumor. We designed a workflow incorporating in vivo modelling, radiology, and deep learning-guided three-dimensional (3D) imaging, spatial proteomics, and transcriptomics to identify previously unreported signals associated with the early transformation of the lung parenchyma announcing regional metastasis. We curated biorepository spanning timepoints before and after resection of primary Lewis Lung Carcinoma (LLC) tumors. Using radiology and cellular resolution 3D histology, we calculated the number and distribution of metastases in mouse lungs and developed an algorithm to guide placement of spatial proteomics and transcriptomics to regions containing early micro-metastases and the pre-metastatic microenvironment. Molecular and tissue features associated with presence, size, and location of metastases guided the identification of both myeloid (F4/80) and senescent (p16/p21) cell signatures in the premetastatic and metastatic environments. Finally, multiparametric flow cytometry of metastatic lungs in a senescence reporter GEMM (tdTomato-p16 INKA mice) resolved senescent cells including alveolar macrophages as the cellular phenotypes associated with these early premetastatic signatures. Altogether, this work highlights a novel AI-assisted approach for detection of biomarkers of tissue remodeling during lung cancer invasion.

PMID:41756853 | PMC:PMC12934922 | DOI:10.64898/2026.02.18.706515

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Exploration of multi-omics liquid biopsy approaches for multi-cancer early detection: The PROMISE study

✇(Multiomics OR Omics) AND (Pancreatic)
By: Jianchun Duan · Qiang Gao · Zhijie Wang · Jiachen Xu · Yuzi Zhang · Yanan Wang · Xi Yang · Lei Zhang · Yu Xu · Qiaoxia Zhou · Bo Yang · Xingyu Yang · Guoqiang Wang · Jing Zhao · Xuefei Wang · Di Ge · Lijie Tan · Wenju Chang · Jianmin Xu · Pinghong Zhou · Mengjiang He · Yuanlin Song · Jie Hu · Wenhui Lou · Wenchuan Wu · Tingyan Shi · Rongyu Zang · Yuan Ji · Sunfang Jiang · Youpei Lin · Liangqing Dong · Shangli Cai · Jia Fan · Jie Wang

Innovation (Camb). 2025 Aug 6;7(1):101076. doi: 10.1016/j.xinn.2025.101076. eCollection 2026 Jan 5.

ABSTRACT

Although circulating cell-free DNA (cfDNA) methylation has emerged as the mainstream approach in multi-cancer detection blood tests (MCDBTs), the potential of integrating proteins and mutations, to enhance its performance remains unclear. The PROMISE study (NCT04972201) was conducted to investigate the feasibility of a multi-omics integration strategy in MCDBTs across nine types of cancers in head and neck (excluding nasopharynx), esophagus, lung, stomach, liver, biliary tract, pancreas, colorectum, and ovary. Blood samples were prospectively collected from 1,706 participants (840 non-cancer; 866 cancer) and then randomly divided into training and validation sets. The complementarity between various omics were investigated, and specific omics features were carefully selected for further multimodal model construction. The methylation-based classifier outperformed both the mutation-based and protein-based classifiers. As 95.0% of cancer cases detected by the mutation-based classifier were simultaneously identified by the methylation-based classifier, while 14.0% of the protein-positive samples were missed, protein markers may provide complementary value to the methylation-based classifier. Compared with the methylation-based classifier, the multimodal classifier combining methylation and protein features exhibited an improved sensitivity of 75.1% (95% confidence interval [CI], 69.3%-80.3%) at the same specificity of 98.8% with the accuracy of top predicted origin (TPO1) of 73.1% (95% CI, 66.2%-79.2%). Notably, the TPO1 accuracy reached 100% in liver and ovarian cancers with negative results of the methylation-based classifier. Collectively, these data suggest that the integration of protein markers in the multimodal classifier can offer additional benefits to the methylation-based classifier, particularly in identifying liver and ovarian cancers.

PMID:41737326 | PMC:PMC12925926 | DOI:10.1016/j.xinn.2025.101076

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AI and Wearables for Early Detection of Cognitive Impairment and Dementia: Systematic Review

✇Journal of Medical Internet Research
By: Ander Cejudo · Markel Arrojo · Cristina Martín · Aitor Almeida
Background: Traditional cognitive screening relies on episodic clinical assessments and may miss early changes preceding cognitive impairment and dementia. Wearable and mobile health technologies enable continuous monitoring of sleep, physical activity, and circadian rhythms, generating digital biomarkers that may support scalable early detection and prevention. However, current evidence remains fragmented across devices, analytic approaches, and cognitive outcomes. Objective: This study synthesizes and critically evaluates recent evidence on wearable devices for early detection and prevention of cognitive impairment and dementia, focusing on device categories, cognitive outcomes, analytic approaches, and prevention relevance. Methods: We searched PubMed, Scopus, ACM Digital Library, and SpringerLink for peer-reviewed studies published between January 2020 and December 1, 2025. Eligible studies included human participants with a mean age ≥50 years, continuous wearable-derived data collected for ≥24 hours, and validated cognitive outcomes; reviews, protocols, smartphone-only studies, and pharmacological interventions were excluded. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the Appraisal Tool for Cross-Sectional Studies, Newcastle-Ottawa Scale, Cochrane Risk of Bias tool, and Quality Assessment of Diagnostic Accuracy Studies-2. Owing to substantial heterogeneity in devices, outcomes, and analytic methods, quantitative meta-analysis was not feasible; a structured narrative synthesis was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidance. This study was not prospectively registered. Results: We included 49 studies, with sample sizes ranging from 14 to 91,948 participants (>200,000 total) and a median sample size of 145. Most used research-grade actigraphy (43/49, 87.8%), while fewer used commercial wearables (7/49, 14.3%). Cognitive outcomes most frequently relied on global screening instruments, including the Mini-Mental State Examination (18/49, 36.7%), followed by ICD-10 (International Statistical Classification of Diseases, Tenth Revision)–based clinical diagnoses (7/49, 14.3%) and the Montreal Cognitive Assessment (7/49, 14.3%). Analytic approaches were predominantly statistical (36/49, 73.5%), with fewer studies applying machine learning (7/49, 14.3%) or deep learning methods (6/49, 12.2%). Statistical analyses linked disrupted sleep, circadian rhythm fragmentation, and irregular activity patterns to worse cognitive outcomes, with modest-to-moderate effect sizes. Machine learning and deep learning approaches reported classification performance with area under the curve values between approximately 0.70 and 0.95. Approximately one-quarter of the studies (13/49, 26.5%) addressed early detection or prevention through longitudinal risk estimation or predictive modeling. Key limitations included small sample sizes, short monitoring durations, and limited external validation. Conclusions: Wearable-derived behavioral markers show promise for early risk stratification. This review advances the field by shifting from descriptive associations toward a digital phenotyping framework evaluating artificial intelligence–driven prediction in the preclinical window. Unlike prior reviews focused on established dementia, it differentiates direct predictive evidence from indirect correlational findings and critically assesses methodological maturity. Continuous, passive monitoring may enable scalable detection of subtle behavioral changes, supporting earlier and more personalized risk reduction strategies. Trial Registration:
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Evolving roles of liquid biopsy in precision medicine for colorectal cancer: from single-gene analysis to broad genomic profiling

✇MRD
By: Giulia Martini · Stefania Napolitano · Davide Ciardiello · Luca Boscolo Bielo · Erika Martinelli · Teresa Troiani · Maria Giulia Zampino · Nicola Fazio · Giuseppe Curigliano · Fortunato Ciardiello

Nat Rev Clin Oncol. 2026 Feb 20. doi: 10.1038/s41571-026-01126-1. Online ahead of print.

ABSTRACT

Colorectal cancer (CRC) is a heterogeneous malignancy, with various alterations in molecular signalling pathways driving disease progression and resistance to therapy. Liquid biopsy, as a source of circulating tumour DNA (ctDNA), has been utilized to characterize tumour molecular heterogeneity, facilitating the identification of actionable targets for precision medicine-guided therapies and the detection of emerging genomic drivers of drug resistance in patients with metastatic CRC. In addition, liquid biopsy-based analysis of ctDNA has been validated as a tool for detecting minimal residual disease (MRD) following locoregional treatment in patients with localized colon or rectal cancer, offering improved prognostic stratification and supporting the tailoring of adjuvant systemic therapy. Methodological evolution from PCR analysis of a few known mutations in one gene or a small panel of genes to the assessment of hundreds of genes and pathogenic variants by next-generation sequencing has enabled comprehensive genomic profiling (CGP), thereby improving knowledge of cancer molecular complexity at the individual patient level. In this respect, liquid biopsy-based CGP is an easily repeatable and minimally invasive approach that can provide a dynamic portrait of CRC molecular heterogeneity to guide personalized and adaptive treatment based on biomarkers of response and resistance. In this Review, we discuss current and potential roles of liquid biopsy-based ctDNA analysis in the clinical management of metastatic CRC. We also discuss the evidence supporting implementation of liquid biopsy-based assessment of MRD to refine the management of locoregional CRC and potentially improve cure rates while reducing overtreatment of many patients.

PMID:41720942 | DOI:10.1038/s41571-026-01126-1

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Hunt Globally: Deep Research AI Agents for Drug Asset Scouting in Investing, Business Development, and Search & Evaluation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Alisa Vinogradova · Vlad Vinogradov · Luba Greenwood · Ilya Yasny · Dmitry Kobyzev · Shoman Kasbekar · Kong Nguyen · Dmitrii Radkevich · Roman Doronin · Andrey Doronichev
arXiv:2602.15019v1 Announce Type: new Abstract: Bio-pharmaceutical innovation has shifted: many new drug assets now originate outside the United States and are disclosed primarily via regional, non-English channels. Recent data suggests >85% of patent filings originate outside the U.S., with China accounting for nearly half of the global total; a growing share of scholarly output is also non-U.S. Industry estimates put China at ~30% of global drug development, spanning 1,200+ novel candidates. In this high-stakes environment, failing to surface "under-the-radar" assets creates multi-billion-dollar risk for investors and business development teams, making asset scouting a coverage-critical competition where speed and completeness drive value. Yet today's Deep Research AI agents still lag human experts in achieving high-recall discovery across heterogeneous, multilingual sources without hallucinations. We propose a benchmarking methodology for drug asset scouting and a tuned, tree-based self-learning Bioptic Agent aimed at complete, non-hallucinated scouting. We construct a challenging completeness benchmark using a multilingual multi-agent pipeline: complex user queries paired with ground-truth assets that are largely outside U.S.-centric radar. To reflect real deal complexity, we collected screening queries from expert investors, BD, and VC professionals and used them as priors to conditionally generate benchmark queries. For grading, we use LLM-as-judge evaluation calibrated to expert opinions. We compare Bioptic Agent against Claude Opus 4.6, OpenAI GPT-5.2 Pro, Perplexity Deep Research, Gemini 3 Pro + Deep Research, and Exa Websets. Bioptic Agent achieves 79.7% F1 versus 56.2% (Claude Opus 4.6), 50.6% (Gemini 3 Pro + Deep Research), 46.6% (GPT-5.2 Pro), 44.2% (Perplexity Deep Research), and 26.9% (Exa Websets). Performance improves steeply with additional compute, supporting the view that more compute yields better results.
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Randomized-controlled trial of skills-based vr vs. distraction vr vs. sham VR for chronic low back pain

✇npj Digital Medicine
By: Brennan M. R. Spiegel · Samuel A. Eberlein · Susan Persky · Mariko L. Ishimori · Swamy Venuturupalli · Joseph Tu · Fadi Alhatem · Mary A. Vijjeswarapu · Zoe Krut · Tom Norris · So Yung Choi · Mourad Tighiouart · Lindsey Ross · Mark Vrahas · Omer Liran · Itai Danovitch · Tinh Vuong · Joshua Fouladian

npj Digital Medicine, Published online: 16 February 2026; doi:10.1038/s41746-026-02437-4

Randomized-controlled trial of skills-based vr vs. distraction vr vs. sham VR for chronic low back pain
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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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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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