Frontiers in digital health最新文献

Background: The digital divide in health has rapidly expanded during and after the COVID-19 pandemic, with fragmented understanding and an unclear implementation process, for the formal integration of digital health into the healthcare system, which challenges actionable policy development.

Methods: This critical interpretive synthesis (CIS) of the literature aimed to capture the complexity of the digital divide in health. This began with a scoping review of literature published between 2013 and 2023 describing the digital divide in health within the WHO's European Region, in Web of Science, Medline (via Ovid), PsycInfo (via Ovid), and Sociological Abstract (via ProQuest). Three sets of two reviewers independently conducted the selection, and all contributed to the synthesis process.

Results: Of 4,967 original articles identified, 49 articles were included for review. Results revealed a synthesizing argument that the digital divide should be considered as more of a dynamic, entangled, and reciprocal collection of "areas" of phenomenon affecting service users, rather than "levels". Results describe the three synthetic constructs that describe this synthesizing argument.

Conclusion: Findings suggest that digital health solutions should respectfully consider the pace of human healing, long-term user engagement and adaptability. We call for the importance of inter- and multidisciplinary collaboration to ensure effective and context-sensitive implementation in future studies.

Purpose: The social acceptance of health technologies is crucial for the effectiveness and sustainability of healthcare systems amid the demographic change. However, patients' acceptance, which shapes technology use and compliance, is still insufficiently understood.

Methods: In this study, we explore how perceived risks and perceived benefits relate to attributed value as a proxy for social acceptance. Unlike most studies that focus on individual technologies, we measure public perception of 20 very different types of health technologies-ranging from plaster cast and x-Ray to insulin pumps, bionic limbs, and mRNA vaccines. Through an online survey utilizing a convenience sample of 193 participants from Germany and Bulgaria, we assessed perceived risks, benefits, and overall value attributed to these technologies. The study presents a visual mapping of the technologies and investigates the individual and technology-related factors shaping these perceptions.

Results: The findings suggest that perceived benefit is the strongest predictor for overall value (β = +0.886), while perceived risk plays a significant, but much smaller role (β = -0.133). Together, both factors explain 95% of the variance in overall attributed value (95%, R ²  = .959). Further, individual differences, such as prior care experience and trust in physicians, significantly influences the perceptions of health technologies.

Conclusion: We conclude with recommendations for effectively communicating the benefits and risks of health technologies to the public, mitigating biases, and enhancing social acceptance and integration into healthcare systems.

Objectives: Artificial intelligence (AI) has shown increasing promise is orthopedic medicine. However, its role in postoperative rehabilitation remains insufficiently synthesized, particularly when rehabilitation is viewed as a continuous and dynamic care process. This scoping review aims to systematically map current AI applications in postoperative orthopedic rehabilitation, indentify prevailing application patterns and evidence gaps, and clarify their clinical and nursing implications.

Methods: This scoping review was conducted following the methodological framework by Arksey and O'Malley. A comprehensive literature search was conducted in PubMed, CINAHL Complete, The Cochrane Library, Web of Science, Embase, Scopus, IEEE Xplore, SinoMed, China National Knowledge Infrastructure (CNKI), and the WanFang Database for studies published between March 2020 and March 2025. Data extraction and descriptive synthesis were performed on all included studies.

Results: A total of 38 articles were included in this review, encompassing 3 core AI technologies, namely machine learning (ML), natural language processing (NLP), and expert systems (ES). These technologies were mainly applied in patients undergoing joint replacement, fracture repair, and spinal surgery, with the main application scenarios focusing on risk prediction, dynamic feedback, and rehabilitation monitoring. Notably, most studies focused on short-term predictive outcomes, while limited evidence addressed AI-supported intervention adjustment, nursing decision support, or long-term functional recovery.

Conclusion: This review highlights that, despite rapid technological progress, AI in postoperative orthopedic rehabilitation remains largely predictive rather than interventional. The novelty of this review lies in its stage-oriented synthesis of AI applications across the rehabilitation continuum, revealing a critical gap between data-driven prediction and clinically actionable rehabilitation support. Future research should prioritize high-quality, longitudinal studies and shift toward AI-enabled preventive and adaptive rehabilitation strategies to facilitate meaningful clinical translation.

Introduction: Dementia is a progressive condition that impacts cognitive and motor functions, with early symptoms often subtle and difficult to detect. Early detection is crucial for effective intervention and improved care. Recent advances in sensor technology enable continuous monitoring of human motion, providing valuable indicators of dementia and cognitive decline.

Methods: This systematic review is the first to focus exclusively on motion-based dementia detection, excluding other neurological conditions. The study aimed to address gaps in the literature by analysing evidence for motion assessment as a tool for dementia detection and by identifying and comparing sensor types, sensor placements, motion assessment tasks, extracted motion features, and analytical methods. Electronic databases (PubMed, Web of Science, IEEE Xplore and Scopus) were searched for articles published between January 2015 to May 2025.

Results: A total of 23 published articles were included. Sensors used across studies included inertial measurement units, pressure mats, cameras, and passive infrared sensors, with placements on body parts, wall-mounted, or floor-based. Motion assessment tasks were grouped into three categories: gait, activities of daily living, and standing postural control. Regarding analytical approaches, 11 studies employed machine learning techniques, while 12 studies utilised statistical analysis. The findings indicate that motion-based assessments demonstrate strong potential for dementia detection, as motion-related features extracted from specific tasks can serve as sensitive indicators of dementia-related cognitive decline.

Discussion: Compared with traditional dementia diagnostic pathways that often involve lengthy assessment cycles, this review's findings provide guidance on refining motion-based sensor selection, task design, and analytical methods to improve standardisation and reproducibility. Future research should prioritise: (1) large-scale, longitudinal data collection with confirmed dementia diagnoses to support machine learning model development; (2) standardisation of sensor types, placements, and motion metrics to enhance comparability; and (3) integration of multimodal data, including motion and brain signals, using explainable machine learning techniques to improve detection accuracy and clinical interpretability.

Introduction: Despite the potential of artificial intelligence (AI) to transform healthcare delivery and reduce costs, adoption remains uneven across populations. Understanding the demographic, behavioral, and cognitive factors influencing public willingness to use AI-powered health tools is critical for equitable implementation. This study examined determinants of AI adoption in healthcare among adults in the United States (U.S.).

Methods: A cross-sectional survey was conducted between March and June 2024 using convenience sampling across the U.S. The study included 568 adult respondents recruited via Qualtrics. The survey assessed demographic characteristics, digital health behaviors, self-reported health status, cognitive and attitudinal factors, and behavioral intentions related to AI use in healthcare. Logistic regression models were used to examine associations between predictors and willingness to adopt AI, with z-tests for subgroup comparisons and Bonferroni correction applied for multiple hypothesis testing.

Results: The sample was predominantly female (66.7%) and Hispanic/Latino (50.7%), with moderate income and education levels. Older age was negatively associated with AI adoption (β = -0.029), males were less likely to use AI than females (β = -0.388), and income was positively correlated with AI adoption (β = 0.096). Trust in AI was substantially lower than trust in physicians: 14.6% trusted ChatGPT's diagnosis for serious illness compared with 92.3% trusting physicians, and 17.1% versus 96.4% for specialist referrals. Telehealth use strongly predicted AI adoption (β = 1.012), while lower self-rated mental health was associated with higher AI use (β = -0.254). Uninsured participants reported higher trust in AI diagnostic capabilities than insured participants (57% vs. 43%, p < 0.05). Ethnic differences were observed, with Asian participants reporting higher AI usage rates than Hispanic participants (16.49% vs. 5.56%, p < 0.05).

Discussion: AI adoption in healthcare is shaped by the interaction of demographic, socioeconomic, and cultural factors. While AI has the potential to expand healthcare access, adoption patterns reflect existing disparities in healthcare access and trust. Trust emerged as a central determinant, with AI functioning as a compensatory tool when traditional healthcare access is limited. Given the U.S.-specific context, findings should be interpreted as exploratory and may not generalize to other healthcare systems. These results highlight the need for future research on transparency, digital literacy, and structural barriers to support equitable implementation of healthcare AI.

Background: This study aimed to investigate in knee osteoarthritis patients the feasibility of a digital anthropometric approach for body size and composition assessment in combination with assessments of physical and pain characteristics.

Methods: A convenience sample of 56 patients (34 females) was recruited. Clinical and radiographic evaluation, digital pain drawing and anthropometric assessments, and physical performance tests were performed.

Results: Pain had an anterior distribution in all patients and several patients showed also a posterior and bilateral distribution. Median values of body fat percentage, fat mass index, and appendicular lean mass index were 28.3%, 7.8 kg/m², and 8.4 kg/m² in 19 males and 40.0%, 12.5 kg/m², 6.8 kg/m² in 28 females. Most of the patients had fat mass index higher than the cut-points for excess fat, while 2 male patients and none of the female patients had appendicular lean mass index lower than the cut-point for low mass. A relevant impairment of physical performance was observed in all patients.

Conclusion: Innovative digital tools can be used to quantify the changes in body size and composition and the pain location and extension in patients with late-stage knee osteoarthritis.

Background: Screening methods are essential for detection of numerous pathologies. Chest x-ray radiography (CXR) is the most widely used screening modality. During the screening, radiologists primarily examine normal radiographs, which results in a substantial workload and an increased risk of errors. There is an increasing necessity to automate radiological screening in order to facilitate the autonomous sorting of normal studies.

Objective: We aimed to evaluate the capabilities of artificial intelligence (AI) techniques for the autonomous CXRs triage and to assess their potential for integration into routine clinical workflow.

Methods: A rapid evidence assessment methodology was employed to conduct this review. Literature searches were performed using relevant keywords across PubMed, arXiv, medRxiv, Elibrary, and Google Scholar covering the period from 2019 to 2025. Inclusion criteria comprised large-scale studies addressing multiple pathologies and providing abstracts in English. Meta-analysis was conducted using confusion matrices derived from reported diagnostic performance metrics in the selected studies. Methodological quality and the overall quality of evidence were assessed using a combination of QUADAS-2, QUADAS-CAD, and GRADE frameworks.

Results: Out of 327 records, 11 studies met the inclusion criteria. Among these, three studies analyzed datasets reflecting the real-world prevalence of pathologies. Three studies included very large cohorts exceeding 500,000 CXRs, whereas the remaining studies used considerably smaller samples. The proportion of autonomously triaged CXRs ranged from 15.0% to 99.8%, with a weighted average of 42.3% across all publications. Notably, in a study conducted under real-world clinical conditions on continuous data flow, this proportion was 54.8%. Sensitivity was 97.8% (95% CI: 94.8%-99.1%), and specificity was 94.8% (95% CI: 53.0%-99.7%). Fifty-five percent of the studies were classified as having a low risk of bias. Primarily, elevated risk of bias and heterogeneity of results were attributed to variability in sample selection criteria and reference standard evaluation.

Conclusions: Modern AI systems for autonomous triage of CXRs are ready to be implemented in clinical practice. AI-driven screening can reduce radiologists' workload, decrease sorting errors and lower the costs associated with screening programs. However, implementation is often hindered by regulatory and legislative barriers. Consequently, comprehensive clinical trials conducted under real-world conditions remain scarce.

By leveraging everyday technologies such as mobile apps, wearables, and AI-enabled tools, digital health interventions (DHIs) offer new pathways to integrate self-management and intervention programs into the fabric of daily life, while bridging gaps in care through continuous, context-aware support. Yet many tools underperform clinically because digital engagement ("screen time") is conflated with impact, while behavioral science is retrofitted, if applied at all. We propose the ENGAGE Framework: a cyclical, six-step model of precision engagement that integrates user needs, behavioral science, and adaptive personalization to transform initial curiosity into sustained real-world habits. By leveraging available data, users can be segmented according to their need (Step 1: Enroll & Segment), targeted with the most relevant and engaging message to increase micro-engagement (Step 2: Nudge & Hook), and persuaded to engage in real-world health behavior change (Step 3: Guide Behavior). From this macro-engagement step, additional core behavioral science principles are used to reinforce the real-world behaviors long enough to positively impact health outcomes (Step 4: Anchor Habits), while measuring progress (Step 5: Generate Evidence) to inform adaptive and optimized engagement strategies (Step 6: Expand & Evolve with AI) for tailored interventions and communications based on user characteristics, context, and clinical data for both new and existing users. Each step of the ENGAGE Framework maps to evidence-based techniques, implementation tactics (e.g., integration pathways and operational deployment strategies), and metrics that help translate superficial engagement into long-lasting behavior change and measurable clinical outcomes. We synthesize relevant engagement literature, identify gaps and challenges (e.g., measurement heterogeneity, lack of focus on macro-engagement, product development challenges, ecosystem barriers), and offer a practical checklist for innovators. By focusing on who needs what support, when and why, ENGAGE aims to help DHI developers and researchers design interventions that are effective, equitable, and empirically testable.

Digital biomarkers are revolutionizing medicine in ways that were unimaginable a few years ago. Consequently, precision medicine approaches now realistically can promise personalization, i.e., the right treatments for the right patients at the right time, including earlier, targeted interventions which lead to a major paradigm shift in how medicine is practiced from reactive to preventive action. Although the scientific evidence is clear on the power of digital biomarkers, there is an unmet need for translating these findings into actionable insights in clinical practice. In this paper, we focus on Alzheimer's disease and related dementias (ADRD), and how digital biomarkers could empower clinical decision making in its preclinical stages. We argue that a new all-encompassing score is needed, akin to a BrainHealth Index linked to the established and validated risk stratifications frameworks and is directed at the prevention of ADRD. Specifically, we propose the new concept "Digital Neuro Fingerprint (DNF)", built with simultaneous collection of multimodal digital biomarkers (speech, gait, eye movements etc.) from smartphone based augmented reality or virtual reality while an individual is immersed in activities of daily living. Fusing the captured multimodal digital biomarkers, data is automatically analyzed with custom combinations of machine- and deep-learning approaches and enhanced with explainable artificial intelligence (XAI) and uncertainty quantifications. We argue that DNF is useful for capturing ADRD progression and should supersede the biomarkers that are invasive and expensive to obtain, offering a sensitive and highly specific score that measures meaningful aspects of health for the patients in high-frequency intervals.

Assistive technologies can enhance safety, independence, and quality of life for people with blindness and low vision. Despite their benefits, abandonment of these technologies remains widespread, and recent research on this issue is limited. In this Perspective article, we draw on both professional experiences and relevant scientific literature to examine adoption and abandonment in the context of new artificial intelligence-powered applications. We highlight risks arising from misaligned design, inconsistent industry support, and inadequate user training. We synthesize existing knowledge on factors that influence abandonment and propose three priorities to realign assistive technology development: participatory and transdisciplinary research, integrated technology ecosystems, and socially supported engagement. Taken collectively, these priorities ensure that emerging assistive technologies better align with the needs of people with blindness and low vision, promoting lasting adoption rather than abandonment.