In this viewpoint, we explore the use of big data analytics and artificial intelligence (AI) and discuss important challenges to their ethical, effective, and equitable use within opioid use disorder (OUD) treatment settings. Applying our collective experiences as OUD policy and treatment experts, we discuss 8 key challenges that OUD treatment services must contend with to make the most of these rapidly evolving technologies: data and algorithmic transparency, clinical validation, new practitioner-technology interfaces, capturing data relevant to improving patient care, understanding and responding to algorithmic outputs, obtaining informed patient consent, navigating mistrust, and addressing digital exclusion and bias. Through this paper, we hope to critically engage clinicians and policy makers on important ethical considerations, clinical implications, and implementation challenges involved in big data analytics and AI deployment in OUD treatment settings.

In this article, we discuss the recently published article by Soni et al. This study explores the effectiveness of a comprehensive digital health program, RESET care, which integrates personalized dietary plans, structured exercise, and cognitive behavioral therapy delivered through a mobile app equipped with Internet of Things devices such as body composition analyzers and smartwatches. Metabolic dysfunction-associated liver disease (MASLD), a global health burden affecting approximately 25% of the population, demands sustainable lifestyle modifications as its primary management strategy. The study reports that 100% of participants in the comprehensive intervention group (diet + exercise + cognitive behavioral therapy) achieved a weight reduction ≥ 7% (6.99 ± 2.98 kg, 7.00% ± 3.39%; P = 0.002), a clinically significant threshold for MASLD improvement. In addition, participants showed a mean weight reduction of 6.99 kg (101.10 ± 17.85 vs 94.11 ± 17.38, P < 0.001) and a body mass index reduction of 2.18 kg/m² (32.90 ± 3.02 vs 30.72 ± 3.41, P < 0.001). These results underscore the potential of digital health platforms to provide scalable, evidence-based solutions for the treatment of MASLD. While these results highlight the potential of digital platforms in the scalable and personalized management of MASLD, the small study sample size and short duration of follow-up limit the generalizability of the results. Future large-scale, long-term trials are needed to confirm sustained benefits, cost-effectiveness, and broader applicability. This letter contextualizes the study within the evolving landscape of MASLD management and emphasizes the clinical implications of integrating digital technologies into standard care.

In the digital age, privacy preservation is of paramount importance while processing health-related sensitive information. This paper explores the integration of Federated Learning (FL) and Differential Privacy (DP) for breast cancer detection, leveraging FL's decentralized architecture to enable collaborative model training across healthcare organizations without exposing raw patient data. To enhance privacy, DP injects statistical noise into the updates made by the model. This mitigates adversarial attacks and prevents data leakage. The proposed work uses the Breast Cancer Wisconsin Diagnostic dataset to address critical challenges such as data heterogeneity, privacy-accuracy trade-offs, and computational overhead. From the experimental results, FL combined with DP achieves 96.1% accuracy with a privacy budget of ε = 1.9, ensuring strong privacy preservation with minimal performance trade-offs. In comparison, the traditional non-FL model achieved 96.0% accuracy, but at the cost of requiring centralized data storage, which poses significant privacy risks. These findings validate the feasibility of privacy-preserving artificial intelligence models in real-world clinical applications, effectively balancing data protection with reliable medical predictions.

To identify distinct patterns in consumer willingness to share health data with various stakeholders and analyze characteristics across consumer groups.

Data from the Rock Health Digital Health Consumer Adoption Survey from 2018, 2019, 2020, and 2022 were analyzed. This study comprised a Census-matched representative sample of U.S. adults. Latent class analysis (LCA) identified groups of respondents with similar data-sharing attitudes. Groups were compared by sociodemographics, health status, and digital health utilization.

We identified three distinct LCA groups: (1) Wary (36.8%), (2) Discerning (47.9%), and (3) Permissive (15.3%). The Wary subgroup exhibited reluctance to share health data with any stakeholder, with predicted probabilities of willingness to share ranging from 0.07 for pharmaceutical companies to 0.34 for doctors/clinicians. The Permissive group showed a high willingness, with predicted probabilities greater than 0.75 for most stakeholders except technology companies and government organizations. The Discerning group was selective, willing to share with healthcare-related entities and family (predicted probabilities >0.62), but reluctant to share with other stakeholders (predicted probabilities <0.29). Individual characteristics were associated with LCA group membership.

Findings highlight a persistent trust in traditional healthcare providers. However, the varying willingness to share with non-traditional stakeholders suggests that while some consumers are open to sharing, others remain hesitant and selective. Data privacy policies and practices need to recognize and respond to multifaceted and stakeholder-specific attitudes.

LCA reveals significant heterogeneity in health data-sharing attitudes among U.S. consumers, providing insights to inform the development of data privacy policies.

Chronic Kidney Disease (CKD) represents a significant global health challenge, contributing to increased morbidity and mortality rates. This review paper explores the current landscape of machine learning (ML) techniques employed in CKD prediction and diagnosis, highlighting recent trends, inherent challenges, innovative solutions, and future directions. Through an extensive literature survey, we identified key limitations and challenges, including the use of small datasets, the absence of stage-specific predictions, insufficient focus on model interpretability, and a lack of discussions on safeguarding patient privacy in managing sensitive CKD data. We considered these limitations and challenges as research gaps, and this review paper aims to address them. We emphasize the potential of Generative AI to augment dataset sizes, thereby enhancing model performance and reliability. To address the lack of stage-specific predictions, we highlight the need for effective multi-class models to accurately predict CKD stages, enabling tailored treatments and improved patient outcomes. Furthermore, we discuss the critical importance of model interpretability, utilizing methods such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to ensure transparency and trust among healthcare professionals. Privacy concerns surrounding sensitive patient data are also addressed. We present innovative privacy-preserving solutions using technologies, such as homomorphic encryption, federated learning, and blockchain. These solutions facilitate collaboration across institutions while maintaining patient confidentiality and addressing challenges related to limited generalizability and reproducibility in CKD prediction. This review informs healthcare professionals and researchers about advancements in ML for CKD prediction, to improve patient outcomes and address research gaps.

This position article discusses current major ethical and social issues related to genetic counseling and testing in clinical psychiatry (PsyGCT).

To address these complex issues in the context of clinical psychiatry relevant to PsyGCT, the interdisciplinary and pan-European expert Network EnGagE (Enhancing Psychiatric Genetic Counseling, Testing, and Training in Europe; CA17130) was established in 2018. We conducted an interdisciplinary, international workshop at which we identified gaps across European healthcare services and research in PsyGCT; the workshop output was summarized and systematized for this position article.

Four main unresolved ethical topics were identified as most relevant for the implementation of PsyGCT: (1) the problematic dualism between somatic and psychiatric disorders, (2) the impact of genetic testing on stigma, (3) fulfilling professional responsibilities, and (4) ethical issues in public health services. We provide basic recommendations to inform psychiatrists and other healthcare professionals involved in the clinical implementation of PsyGCT and conclude by pointing to avenues of future ethics research in PsyGCT.

This article draws attention to a set of unresolved ethical issues relevant for mental health professionals, professionals within clinical genetics, patients and their family members, and society as a whole and stresses the need for more interdisciplinary exchange to define standards in psychiatric counseling as well as in public communication. The use of PsyGCT may, in the future, expand and include genetic testing for additional psychiatric diagnoses. We advocate the development of pan-European ethical standards addressing the four identified areas of ethical-practical relevance in PsyGCT.

Digital health literacy is integral to accessing reliable information, especially for students making informed health decisions. This study aims to assess the digital health literacy level as well as sociodemographic factors of students of universities in Asadabad County, Hamadan, Western Iran.

The present research was a descriptive-cross-sectional study conducted between May to June 2024. The statistical population included 500 students from the following Iranian universities in Asadabad county: Islamic Azad University, Payame Noor University, Technical and Vocational College, and Asadabad School of Medical Sciences. The van der Vaart Digital Health Literacy Scale was used for data collection.

The study showed that students' digital health literacy status is moderate (47.19 ± 8.34). In the dimensions of digital health literacy, operational skills (61.84 ± 32.97) were at a desirable level, with the most significant issues related to privacy protection (23.51 ± 21.72). The mean digital health literacy score of students of Medical Sciences University was significantly higher than Azad University (P < 0.001) but lower than Technical and Vocational University (P = 0.048). There was a significant relationship between digital health literacy and the variables of the university of study (p < 0.001), gender (p = 0.049), education level (p = 0.017), nativity status (p = 0.001), and residence status (p < 0.001).

The results of the present study revealed that the digital health literacy of students in Iran was moderate, depending on sociodemographic factors. The findings from this study can be used to develop and implement interventions and strategies to improve digital health literacy.

A high prevalence of chronic diseases exposes diverse healthcare pain points due to the limited effectiveness of pharmaceutical drugs and biologics, sedentary lifestyles, insufficient health literacy, chronic stress, unsatisfactory patient experience, environmental pollution and competition with commercial determinants of health. To improve patient care and long-term outcomes, the impact of the home environment is overlooked and underutilized by healthcare. This cross-disciplinary work describes perspectives on (1) the home environment as a therapeutic target for the prevention and treatment of chronic diseases and (2) transforming health-centric household goods e-commerce platforms into digital health interventions. We provide a rationale for creating therapeutic home environments grounded in biophilic design (multisensory, environmental enrichment) and supporting physical activities, quality sleep, nutrition, music, stress reduction, self-efficacy, social support and health education, hence providing clinical benefits through the modulation of the autonomic nervous system, neuroplasticity and behavior change. These pleiotropic "active non-pharmacological ingredients" can be personalized for people living with depression, anxiety, migraine, chronic pain, cancer, cardiovascular and other conditions. We discuss prospects for integrating e-commerce with digital health platforms to create "therapeutic home environment" interventions delivered through digital therapeutics and their combinations with prescription drugs. This multimodal approach can enhance patient engagement while bridging consumer spending with healthcare outcomes.

Artificial intelligence (AI) and its subset, machine learning, have tremendous potential to transform health care, medicine, and population health through improved diagnoses, treatments, and patient care. However, the effectiveness of these technologies hinges on the quality and diversity of the data used to train them. Many datasets currently used in machine learning are inherently biased and lack diversity, leading to inaccurate predictions that may perpetuate existing health disparities. This commentary highlights the challenges of biased datasets, the impact on marginalized communities, and the critical need for strategies to address these disparities throughout the research continuum. To overcome these challenges, it is essential to adopt more inclusive data collection practices, engage collaboratively with community stakeholders, and leverage innovative approaches like federated learning. These steps can help mitigate bias and enhance the accuracy and fairness of AI-assisted or informed health care solutions. By addressing systemic biases embedded across research phases, we can build a better foundation for AI to enhance diagnostic and treatment capabilities and move society closer to the goal where improved health and health care can be a fundamental right for all, and not just for some.

This study investigates the feasibility and effectiveness of integrating Attribute-Based Encryption (ABE) into smart healthcare networks, with a particular focus on its role in enhancing anti-corruption mechanisms. The study provides a comprehensive analysis of current vulnerabilities in these networks, identifying potential data security risks. An anti-corruption mechanism is designed to ensure data integrity and reliability. The ABE approach is then empirically compared to other prominent encryption algorithms, such as Identity-Based Encryption, Data Encryption Standard, Advanced Encryption Standard, and Rivest-Shamir-Adleman algorithms. These methods are evaluated based on access latency, data transmission speed, system stability, and anti-corruption capabilities. Experimental results highlight the strengths of the ABE algorithm, demonstrating an average access latency of 31.6 ms, a data transmission speed of 3.56 MB/s, and an average system stability of 98.74 %. Furthermore, when integrated into anti-corruption mechanisms, ABE effectively protects against data tampering and misuse, ensuring secure data transmission. Compared to alternative algorithms, ABE offers a more efficient, secure, and stable solution for data management within smart healthcare networks, supported by its robust anti-corruption capabilities. This positions ABE as an optimal choice for safeguarding the integrity and security of healthcare data.

Suicide is a global public health issue disproportionately impacting equity-deserving groups. Recent advances in Artificial Intelligence and increased access to a variety of digital data sources have enabled the development of novel and personalized suicide prevention strategies. However, standards on how to harness these data in a comprehensive and equitable way remain unclear. The primary aim of this study is to identify considerations for the collection and use of digital health data for suicide prevention and care. The results will inform the development of a data governance framework for a multinational suicide prevention mHealth platform.

We used a modified Cochrane Rapid Reviews Method. Inclusion criteria focused on primary studies published in English from 2007 to the present that referenced the use of digital health data in the context of suicide prevention and care. Screening and data extraction was performed independently by multiple reviewers, with disagreements resolved through discussion. Qualitative and quantitative synthesis methods were employed to identify emergent themes.

Our search identified 2453 potential articles, with 70 meeting inclusion criteria. We found little consensus on best practices for the collection and use of digital health data for suicide prevention and care. Issues of data quality, fairness and equity persist, compounded by inadequate consideration of key governance issues including privacy and trust, especially in multinational initiatives.

Recommendations for future research and practice include prioritizing engagement with knowledge users, establishing robust data governance frameworks aligned with clinical guidelines, and leveraging advanced analytics, such as natural language processing, to improve the quality of health equity data.

Given the increasing number of artificial intelligence and machine learning (AI/ML) tools in healthcare, we aimed to gain an understanding of consumer perspectives on the use of AI/ML tools for healthcare diagnostics.

We conducted a qualitative systematic review, following established standardized methods, of the existing literature indexed in the following databases up to 4 April 2022: OVID MEDLINE, OVID EMBASE, Scopus and Web of Science.

Fourteen studies were identified as appropriate for inclusion in the meta-synthesis and systematic review. Most studies (n = 12) were conducted in high-income countries, with data extracted from both mixed methods (42.9%) and qualitative (57.1%) studies. The meta-synthesis identified four overarching themes across the included studies: (1) Trust, fear, and uncertainty; (2) Data privacy and ML governance; (3) Impact on healthcare delivery and access; and (4) Consumers want to be engaged.

The current evidence demonstrates consumers' understandings of AI/ML for medical diagnosis are complex. Consumers express a complex combination of both hesitancy and support towards AI/ML in healthcare diagnosis. Importantly, their views of the use of AI/ML in medical diagnosis are influenced by the perceived trustworthiness of their healthcare providers who use these AI/ML tools. Consumers recognize the potential for AI/ML tools to improve diagnostic accuracy, efficiency and access, and express a strong interest to be engaged in the development and implementation process of AI/ML into routine healthcare.

Evaluating precision oncology outcomes requires access to real-world and clinical trial data. Access is based on consent, and consent is based on patients' informed preferences when deciding to share their data. Decision-making is often modeled using utility theory, but a complex decision context calls for a consideration of how heuristic, intuitive thought processes interact with rational utility maximization. Data-sharing decision-making has been studied using heuristic theory, but almost no heuristic research exists in the health data context. This study explores this evidence gap, applying a qualitative approach to probe for evidence of heuristic mechanisms behind the health data-sharing preferences of those who have experienced cancer. Exploring qualitative decision-making reveals the types of heuristics used and how they are related to the process of decision-making to better understand whether consent mechanisms should consider nonrational processes to better serve patient decision-making.

This study aimed to explore how patients with cancer use heuristics when deciding whether to share their data for research.

The researchers conducted a focus group study of Canadians who have experienced cancer. We recruited participants through an online advertisement, screening individuals based on their ability to increase demographic diversity in the sample. We reviewed the literature on data-sharing platforms to develop a semistructured topic guide on concerns about data sharing, incentives to share, and consent and control. Focus group facilitators led the open-ended discussions about data-sharing preferences that revealed underlying heuristics. Two qualitative analysts coded transcripts using a heuristic framework developed from a review of the literature. Transcripts were analyzed for heuristic instances which were grouped according to sociocultural categories. Using thematic analysis, the analysts generated reflexive themes through norming sessions and consultations.

A total of 3 focus groups were held with 19 participants in total. The analysis identified 12 heuristics underlying intentions to share data. From the thematic analysis, we identified how the heuristics of social norms and community building were expressed through altruism; the recognition, reputation, and authority heuristics led to (dis)trust in certain institutions; the need for security prompted the illusion of control and transparency heuristics; and the availability and affect heuristics influenced attitudes around risk and benefit. These thematic relationships all had impacts on the participants' intentions to share their health data.

The findings provide a novel qualitative understanding of how health data-sharing decisions and preferences may be based on heuristic processing. As patients consider the extent of risks and benefits, heuristic processes influence their assessment of anticipated outcomes, which may not result in rational, truly informed consent. This study shows how considering heuristic processing when designing current consent mechanisms opens up the opportunity for more meaningful and realistic interactions with the complex decision-making context.

Oral cancer, the most prevalent cancer worldwide, is far more likely to occur after the age of forty-five, according to the World Health Organization. Although many biomarkers have been discovered over the years using non-invasive saliva samples, biopsies, and human blood, these biomarkers have not been incorporated into standard clinical practice. Investigating the function of microRNAs (miRNAs) in the diagnosis, aetiology, prognosis, and treatment of oral cancer has drawn more attention in recent years. Though salivary microRNA can act as a window into the molecular environment of the tumour, there are challenges due to the heterogeneity of oral squamous cell carcinoma (OSCC), diversity in sample collection, processing techniques, and storage conditions. The up and downregulation of miRNAs has been found to have a profound role in OSCC as it regulates tumour stages by targeting many genes. As a result, the regulatory functions of miRNAs in OSCC underscore their significance in the field of cancer biology. Salivary miRNAs are useful diagnostic and prognostic indicators because their abnormal expression profiles shed light on tumour behaviour and patient prognosis. In addition to their diagnostic and prognostic value, miRNAs hold promise as therapeutic targets for oral cancer intervention. The current review sheds light on the challenges and potentials of microRNA studies that could lead to a better understanding of oral cancer prognosis, diagnosis, and therapeutic intervention. Furthermore, the clinical translation of OSCC biomarkers requires cooperation between investigators, physicians, regulatory bodies, and business partners. There is much potential for improving early identification, tracking therapy response, and forecasting outcomes in OSCC patients by including saliva-based miRNAs as biomarkers.

There are avoidable differences (i.e., inequities) in the prevalence and distribution of chronic pain across diverse populations, as well as in access to and outcomes of pain management services. Digital pain self-reporting tools have the potential to reduce or exacerbate these inequities. This study aimed to better understand how to optimise the health equity impact of digital pain self-reporting tools on people who are experiencing (or are at risk of) digital pain inequities.

This was a qualitative study, guided by the Health Equity Impact Assessment tool-digital health supplement (HEIA-DH). We conducted three scoping focus groups with multiple stakeholders to identify the potential impacts of digital pain self-reporting tools and strategies to manage these impacts. Each group focused on one priority group experiencing digital pain inequities, including older adults, ethnic minorities, and people living in socio-economically deprived areas. A fourth consensus focus group was organised to discuss and select impact management strategies. Focus groups were audio-recorded, transcribed verbatim, and analysed using a framework approach. We derived codes, grouped them under four pre-defined categories from the HEIA-DH, and illustrated them with participants' quotes.

A total of fifteen people living with musculoskeletal pain conditions and thirteen professionals took part. Participants described how digital pain self-reports can have a positive health equity impact by better capturing pain fluctuations and enriching patient-provider communication, which in turn can enhance clinical decisions and self-management practices. Conversely, participants identified that incorrect interpretation of pain reports, lack of knowledge of pain terminologies, and digital (e.g., no access to technology) and social (e.g., gender stereotyping) exclusions may negatively impact on people's health equity. The participants identified 32 strategies, of which 20 were selected as being likely to mitigate these negative health equity impacts. Example strategies included, e.g., option to customise self-reporting tools in line with users' personal preferences, or resources to better explain how self-reported pain data will be used to build trust.

Linked to people's personal and social characteristics, there are equity-based considerations for developing accessible digital pain self-reporting tools, as well as resources and skills to enable the adoption and use of these tools among priority groups. Future research should focus on implementing these equity-based considerations or strategies identified by our study and monitoring their impact on the health equity of people living with chronic pain.

Clinical drug trials are intricate, involving numerous stakeholders, substantial data, and stringent regulations. Traditional systems for recording, storing, and sharing trial data often face data integrity, transparency, security, and interoperability challenges. The utilization of blockchain technology has emerged as a transformative influence in various industries, and its potential within healthcare, particularly in clinical drug trials, is increasingly gaining recognition.

Blockchain technology presents a decentralized and immutable ledger system that holds promise in effectively addressing these challenges. As the healthcare industry continues its journey of digital transformation, the incorporation of blockchain technology for monitoring clinical drug trials represents a paradigm shift that can result in more reliable, efficient, and transparent trials.

This review explores the innovative application of blockchain technology in transforming the monitoring and management of clinical drug trials and provides a comprehensive overview of the possibilities, challenges, and future directions of blockchain-based monitoring in the context of clinical drug trials, contributing to the progress of both blockchain technology and healthcare research practices.

Large data-intensive health research programmes benefit from collaboration amongst researchers who may be located in different institutions and international contexts. However, complexities in navigating privacy frameworks and data protection laws across various jurisdictions pose significant challenges to researchers seeking to share or transfer data outside of institutional boundaries. Research on the awareness of data protection and privacy laws amongst stakeholders is limited. Our qualitative study, drawn from a larger project in Singapore, revealed insights into stakeholders' perceptions of the role of law in cross-national health data research. Stakeholders in our study demonstrated a range of perceptions regarding the role of data protection law in governing the collection and transfer of health data for research. The main criticisms included inadequate legal protection to data and lack of uniformed data protection standards. Despite these criticisms, participants recognised the importance of data protection law in supporting cross-border data transfers and proposed measures to improve perceived limitations of existing laws. These measures include strengthening existing legal framework, establishing contractual agreements and imposing severe punishments for data misuse.

Oral cancer retains one of the lowest survival rates worldwide, despite recent therapeutic advancements signifying a tenacious challenge in healthcare. Artificial intelligence exhibits noteworthy potential in escalating diagnostic and treatment procedures, offering promising advancements in healthcare. This review entails the traditional imaging techniques for the oral cancer treatment. The role of artificial intelligence in prognosis of oral cancer including predictive modeling, identification of prognostic factors and risk stratification also discussed significantly in this review. The review also encompasses the utilization of artificial intelligence such as automated image analysis, computer-aided detection and diagnosis integration of machine learning algorithms for oral cancer diagnosis and treatment. The customizing treatment approaches for oral cancer through artificial intelligence based personalized medicine is also part of this review. See also the graphical abstract(Fig. 1).

The advent of affordable technology has significantly influenced the practice of digital pathology, leading to its growing adoption within the pathology community. This review article aimed to outline the latest developments in digital pathology, the cutting-edge advancements in artificial intelligence (AI) applications within this field, and the pertinent United States regulatory frameworks. The content is based on a thorough analysis of original research articles and official United States Federal guidelines. Findings from our review indicate that several Food and Drug Administration-approved digital scanners and image management systems are establishing a solid foundation for the seamless integration of advanced technologies into everyday pathology workflows, which may reduce device and operational costs in the future. AI is particularly transforming the way morphologic diagnoses are automated, notably in cancers like prostate and colorectal, within screening initiatives, albeit challenges such as data privacy issues and algorithmic biases remain. The regulatory environment, shaped by standards from the Food and Drug Administration, Centers for Medicare & Medicaid Services/Clinical Laboratory Improvement Amendments, and College of American Pathologists, is evolving to accommodate these innovations while ensuring safety and reliability. Centers for Medicare & Medicaid Services/Clinical Laboratory Improvement Amendments have issued policies to allow pathologists to review and render diagnoses using digital pathology remotely. Moreover, the introduction of new digital pathology Current Procedural Terminology codes designed to complement existing pathology Current Procedural Terminology codes is facilitating reimbursement processes. Overall, these advancements are heralding a new era in pathology that promises enhanced diagnostic precision and efficiency through digital and AI technologies, potentially improving patient care as well as bolstering educational and research activities.

To examine whether comfort with the use of ChatGPT in society differs from comfort with other uses of AI in society and to identify whether this comfort and other patient characteristics such as trust, privacy concerns, respect, and tech-savviness are associated with expected benefit of the use of ChatGPT for improving health.

We analyzed an original survey of U.S. adults using the NORC AmeriSpeak Panel (n = 1787). We conducted paired t-tests to assess differences in comfort with AI applications. We conducted weighted univariable regression and 2 weighted logistic regression models to identify predictors of expected benefit with and without accounting for trust in the health system.

Comfort with the use of ChatGPT in society is relatively low and different from other, common uses of AI. Comfort was highly associated with expecting benefit. Other statistically significant factors in multivariable analysis (not including system trust) included feeling respected and low privacy concerns. Females, younger adults, and those with higher levels of education were less likely to expect benefits in models with and without system trust, which was positively associated with expecting benefits (P = 1.6 × 10-11). Tech-savviness was not associated with the outcome.

Understanding the impact of large language models (LLMs) from the patient perspective is critical to ensuring that expectations align with performance as a form of calibrated trust that acknowledges the dynamic nature of trust.

Including measures of system trust in evaluating LLMs could capture a range of issues critical for ensuring patient acceptance of this technological innovation.

Artificial Intelligence (AI) has shown remarkable potential to revolutionise healthcare by enhancing diagnostics, improving treatment outcomes, and streamlining administrative processes. In the global regulatory landscape, several countries are working on regulating AI in healthcare. There are five key regulatory issues that need to be addressed: (i) data security and protection-measures to cover the "digital health footprints" left unknowingly by patients when they access AI in health services; (ii) data quality-availability of safe and secure data and more open database sources for AI, algorithms, and datasets to ensure equity and prevent demographic bias; (iii) validation of algorithms-mapping of the explainability and causability of the AI system; (iv) accountability-whether this lies with the healthcare professional, healthcare organisation, or the personified AI algorithm; (v) ethics and equitable access-whether fundamental rights of people are met in an ethical manner. Policymakers may need to consider the entire life cycle of AI in healthcare services and the databases that were used for the training of the AI system, along with requirements for their risk assessments to be publicly accessible for effective regulatory oversight. AI services that enhance their functionality over time need to undergo repeated algorithmic impact assessment and must also demonstrate real-time performance. Harmonising regulatory frameworks at the international level would help to resolve cross-border issues of AI in healthcare services.

In the digital age, maintaining patient confidentiality while ensuring effective care coordination poses significant challenges for healthcare providers, particularly nurses.

To investigate the challenges and strategies associated with balancing patient confidentiality and effective care coordination in the digital age.

A cross-sectional study was conducted in a general hospital in Egypt to collect data from 150 nurses across various departments with at least six months of experience in patient care. Data were collected using six tools: Demographic Form, HIPAA Compliance Checklist, Privacy Impact Assessment (PIA) Tool, Data Sharing Agreement (DSA) Framework, EHR Privacy and Security Assessment Tool, and NIST Cybersecurity Framework. Validity and Reliability were ensured through pilot testing and factor analysis.

Participants were primarily aged 31-40 years (45%), with 75% female and 60% staff nurses. High compliance was observed in the HIPAA Compliance Checklist, especially in Administrative Safeguards (3.8 ± 0.5), indicating strong management and training processes, with an overall score of 85 ± 10. The PIA Tool showed robust privacy management, with Project Descriptions scoring 4.5 ± 0.3 and a total score of 30 ± 3. The DSA Framework had a mean total score of 20 ± 2, with Data Protection Measures scoring highest at 4.0 ± 0.4. The EHR assessments revealed high scores in Access Controls (4.4 ± 0.3) and Data Integrity Measures (4.3 ± 0.3), with an overall score of 22 ± 1.5. The NIST Cybersecurity Framework had a total score of 18 ± 2, with the highest scores in Protect (3.8) and lower in Detect (3.6). Strong positive correlations were found between HIPAA Compliance and EHR Privacy (r = 0.70, p < 0.05) and NIST Cybersecurity (r = 0.55, p < 0.05), reflecting effective data protection practices.

The study suggests that continuous improvement in privacy practices among healthcare providers, through ongoing training and comprehensive privacy frameworks, is vital for enhancing patient confidentiality and supporting effective care coordination.

The COVID-19 pandemic underscored the critical importance of vaccination to global health security and highlighted the potential of digital health solutions to improve immunization strategies. This article explores integrating digital health technologies with immunization programs to improve coverage, monitoring, and public health outcomes. It examines the current landscape of digital tools used in immunization initiatives, such as mobile health apps, electronic health records, and data analytics platforms. Case studies from different regions demonstrate the effectiveness of these technologies in addressing challenges such as vaccine hesitancy, logistics, and real-time monitoring of vaccine distribution and adverse events. The paper also examines ethical considerations, data privacy issues, and the need for a robust digital infrastructure to support these innovations. By analyzing the successes and limitations of digital health interventions in immunization campaigns during and after the COVID-19 pandemic, we provide recommendations for future integration strategies to ensure resilient and responsive immunization systems. This research aims to guide policymakers, health professionals, and technologists in leveraging digital health to strengthen immunization efforts and prepare for future public health emergencies.

The ever-evolving landscape of digital health technology has dramatically enhanced patients' ability to manage their health through self-care effectively. These advancements have created various categories of self-care products, including medication management, health tracking, and wellness. There is no published research regarding integrating digital health into pharmacy self-care courses. This study aims to identify pertinent digital health devices and applications to incorporate into self-care course education. Digital health limitations, challenges incorporating digital health in self-care pharmacy education, and potential solutions are also reviewed. In conducting this research, many resources, including PubMed, APhA, ASHP, fda.gov, and digital.health, were reviewed in March 2024 to gather information on digital health devices and applications. To supplement this, targeted keyword searches were conducted on topics such as "digital health", "devices", "applications", "technology", and "self-care" across various online platforms. We identified digital health devices and applications suitable for self-care education across eight topics, as follows: screening, insomnia, reproductive disorders, eye disorders, home medical equipment, GI disorders, pediatrics, and respiratory disorders. Among these topics, wellness screening had the most digital health products available. For all other topics, at least three or more products were identified as relevant to self-care curriculum. By equipping students with digital health knowledge, they can effectively apply it in patient care throughout their rotations and future practice. Many digital health products, including telemedicine, electronic health records, mobile health applications, and wearable devices, are ideal for inclusion in pharmacy curriculum as future educational material. Future research is needed to develop the best strategies for incorporating relevant digital health into self-care education and defining the best student-learning strategies.

We explored the challenges and solutions for managing data within the Whole Health System (WHS), which operates as a Learning Health System and a patient-centered healthcare approach that combines conventional and complementary approaches. Addressing these challenges is critical for enhancing patient care and improving outcomes within WHS. The proposed solutions include prioritizing interoperability for seamless data exchange, incorporating patient-centered comparative clinical effectiveness research and real-world data to personalize treatment plans and validate integrative approaches, and leveraging advanced data analytics tools to incorporate patient-reported outcomes, objective metrics, robust data platforms. Implementing these measures will enable WHS to fulfill its mission as a holistic and patient-centered healthcare model, promoting greater collaboration among providers, boosting the well-being of patients and providers, and improving patient outcomes.

Wearable devices are increasingly used by a growing portion of the population to track health and illnesses. The data emerging from these devices can potentially transform health care. This requires an interoperability framework that enables the deployment of platforms, sensors, devices, and software applications within diverse health systems, aiming to facilitate innovation in preventing and treating cardiovascular disease. However, the current data ecosystem includes several noninteroperable systems that inhibit such objectives. The design of clinically meaningful systems for accessing and incorporating these data into clinical workflows requires strategies to ensure the quality of data and clinical content and patient and caregiver accessibility. This scientific statement aims to address the best practices, gaps, and challenges pertaining to data interoperability in this area, with considerations for (1) data integration and the scope of measures, (2) application of these data into clinical approaches/strategies, and (3) regulatory/ethical/legal issues.

Acute pulmonary thromboembolism (PTE) is a common cardiovascular disease and recognizing low prognosis risk patients with PTE accurately is significant for clinical treatment. This study evaluated the value of federated learning (FL) technology in PTE prognosis risk assessment while ensuring the security of clinical data.

A retrospective dataset consisted of PTE patients from 12 hospitals were collected, and 19 physical indicators of patients were included to train the FL-based prognosis assessment model to predict the 30-day death event. Firstly, multiple machine learning methods based on FL were compared to choose the superior model. And then performance of models trained on the independent (IID) and non-independent identical distributed(Non-IID) datasets was calculated and they were tested further on Real-world data. Besides, the optimal model was compared with pulmonary embolism severity index (PESI), simplified PESI (sPESI), Peking Union Medical College Hospital (PUMCH).

The area under the receiver operating characteristic curve (AUC) of logistic regression(0.842) outperformed convolutional neural network (0.819) and multi layer perceptron (0.784). Under IID, AUC of model trained using FL(Fed) on the training, validation and test sets was 0.852 ± 0.002, 0.867 ± 0.012 and 0.829 ± 0.004. Under Real-world, AUC of Fed was 0.855 ± 0.005, 0.882 ± 0.003 and 0.835 ± 0.005. Under IID and Real-world, AUC of Fed surpassed centralization model(NonFed) (0.847 ± 0.001, 0.841 ± 0.001 and 0.811 ± 0.001). Under Non-IID, although AUC of Fed (0.846 ± 0.047) outperformed NonFed (0.841 ± 0.001) on validation set, it (0.821 ± 0.016 and 0.799 ± 0.031) slightly lagged behind NonFed (0.847 ± 0.001 and 0.811 ± 0.001) on the training and test sets. In practice, AUC of Fed (0.853, 0.884 and 0.842) outshone PESI (0.812, 0.789 and 0.791), sPESI (0.817, 0.770 and 0.786) and PUMCH(0.848, 0.814 and 0.832) on the training, validation and test sets. Additionally, Fed (0.842) exhibited higher AUC values across test sets compared to those trained directly on the clients (0.758, 0.801, 0.783, 0.741, 0.788).

In this study, the FL based machine learning model demonstrated commendable efficacy on PTE prognostic risk prediction, rendering it well-suited for deployment in hospitals.

Digital health interventions offer a promising approach for monitoring during postoperative recovery. However, the effectiveness of these interventions remains poorly understood, particularly in children. The objective of this study was to assess the efficacy of digital health interventions for postoperative recovery in children.

A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, with the use of automation tools for searching and screening. We searched five electronic databases for randomised controlled trials or non-randomised studies of interventions that utilised digital health interventions to monitor postoperative recovery in children. The study quality was assessed using Cochrane Collaboration's Risk of Bias tools. The systematic review protocol was prospectively registered with PROSPERO (CRD42022351492).

The review included 16 studies involving 2728 participants from six countries. Tonsillectomy was the most common surgery and smartphone apps (WeChat) were the most commonly used digital health interventions. Digital health interventions resulted in significant improvements in parental knowledge about the child's condition and satisfaction regarding perioperative instructions (standard mean difference=2.16, 95% confidence interval 1.45-2.87; z=5.98, P<0.001; I2=88%). However, there was no significant effect on children's pain intensity (standard mean difference=0.09, 95% confidence interval -0.95 to 1.12; z=0.16, P=0.87; I2=98%).

Digital health interventions hold promise for improving parental postoperative knowledge and satisfaction. However, more research is needed for child-centric interventions with validated outcome measures. Future work should focus development and testing of user-friendly digital apps and wearables to ease the healthcare burden and improve outcomes for children.

PROSPERO (CRD42022351492).

The COVID-19 pandemic prompted new interest in non-traditional data sources to inform response efforts and mitigate knowledge gaps. While non-traditional data offers some advantages over traditional data, it also raises concerns related to biases, representativity, informed consent and security vulnerabilities. This study focuses on three specific types of non-traditional data: mobility, social media, and participatory surveillance platform data. Qualitative results are presented on the successes, challenges, and recommendations of key informants who used these non-traditional data sources during the COVID-19 pandemic in Spain and Italy.

A qualitative semi-structured methodology was conducted through interviews with experts in artificial intelligence, data science, epidemiology, and/or policy making who utilized non-traditional data in Spain or Italy during the pandemic. Questions focused on barriers and facilitators to data use, as well as opportunities for improving utility and uptake within public health. Interviews were transcribed, coded, and analyzed using the framework analysis method.

Non-traditional data proved valuable in providing rapid results and filling data gaps, especially when traditional data faced delays. Increased data access and innovative collaborative efforts across sectors facilitated its use. Challenges included unreliable access and data quality concerns, particularly the lack of comprehensive demographic and geographic information. To further leverage non-traditional data, participants recommended prioritizing data governance, establishing data brokers, and sustaining multi-institutional collaborations. The value of non-traditional data was perceived as underutilized in public health surveillance, program evaluation and policymaking. Participants saw opportunities to integrate them into public health systems with the necessary investments in data pipelines, infrastructure, and technical capacity.

While the utility of non-traditional data was demonstrated during the pandemic, opportunities exist to enhance its impact. Challenges reveal a need for data governance frameworks to guide practices and policies of use. Despite the perceived benefit of collaborations and improved data infrastructure, efforts are needed to strengthen and sustain them beyond the pandemic. Lessons from these findings can guide research institutions, multilateral organizations, governments, and public health authorities in optimizing the use of non-traditional data.

This review provides an in-depth analysis of the intersection between health and technology, focusing specifically on social media's and online communities' role in healthcare. It explores the significance of these digital platforms in patient education, empowerment, and support, highlighting their potential to improve healthcare delivery and patient outcomes. Key findings are synthesized by examining existing literature, including the wide-reaching impact of social media on health information dissemination and the value of online communities in facilitating peer support. However, privacy concerns and misinformation are also addressed, emphasizing the need for careful consideration and strategic implementation of these technologies. The implications for healthcare practice and research are discussed, with recommendations for future actions and priorities outlined. Overall, this review underscores the transformative potential of social media and online communities in reshaping the healthcare landscape. It also highlights the importance of ethical and responsible use to maximize benefits.

Communities' use of technology and the internet for online health information (OHI) is increasing exponentially. An understanding of how and why individuals access OHI, and how this information influences decisions on health, medicines and self-care practices is critical. This review aims to: (1) identify the factors influencing OHI-seeking behaviour; (2) evaluate the evidence of OHI on self-care practices; and (3) outline strategies to improve online informed decision-making and assess the impact of these strategies on consumer outcomes.

A review of systematic reviews was conducted in November of 2023, following the Cochrane Handbook and PRISMA guidelines, and using PubMed, Scopus, Web of Science and EBSCOhost databases. The methodological quality of retrieved reviews was appraised using the AMSTAR 2 tool.

The search retrieved 1725 records. Of these, 943 were screened, and 33 were included in the final analysis. The most frequently identified reasons for seeking OHI were to retrieve diagnostic and treatment information, and well-being and emotional support. Level of education and socio-economic status influenced OHI-seeking. OHI directly influenced self-care decision-making by individuals and their relationships and communication with healthcare providers. Overall, OHI-seeking (and interventions to promote the use of OHI) enhanced individuals' confidence, skills and knowledge.

The findings highlight the benefits of OHI-seeking and its potential influence on self-care decisions. Future research should focus on strategies that would promote the pursuit of high-quality, up-to-date OHI and on the development of interventions for healthcare professionals to improve patients' use of OHI in self-care and self-efficacy.

The effective management of chronic conditions requires an approach that promotes a shift in care from the clinic to the home, improves the efficiency of health care systems, and benefits all users irrespective of their needs and preferences. Digital health can provide a solution to this challenge, and in this paper, we provide our vision for a smart health ecosystem. A smart health ecosystem leverages the interoperability of digital health technologies and advancements in big data and artificial intelligence for data collection and analysis and the provision of support. We envisage that this approach will allow a comprehensive picture of health, personalization, and tailoring of behavioral and clinical support; drive theoretical advancements; and empower people to manage their own health with support from health care professionals. We illustrate the concept with 2 use cases and discuss topics for further consideration and research, concluding with a message to encourage people with chronic conditions, their caregivers, health care professionals, policy and decision makers, and technology experts to join their efforts and work toward adopting a smart health ecosystem.

It has become increasingly difficult for individuals to exercise meaningful control over the personal data they disclose to companies or to understand and track the ways in which that data is exchanged and used. These developments have led to an emerging consensus that existing privacy and data protection laws offer individuals insufficient protections against harms stemming from current data practices. However, an effective and ethically justified way forward remains elusive. To inform policy in this area, we propose the Ethical Data Practices framework. The framework outlines six principles relevant to the collection and use of personal data-minimizing harm, fairly distributing benefits and burdens, respecting autonomy, transparency, accountability, and inclusion-and translates these principles into action-guiding practical imperatives for companies that process personal data. In addition to informing policy, the practical imperatives can be voluntarily adopted by companies to promote ethical data practices.

Facial recognition technology (FRT) has been adopted as a precision medicine tool. The medical genetics field highlights both the clinical potential and privacy risks of this technology, putting the discipline at the forefront of a new digital privacy debate. Investigating how geneticists perceive the privacy concerns surrounding FRT can help shape the evolution and regulation of the field, and provide lessons for medicine and research more broadly. Five hundred and sixty-two genetics clinicians and researchers were approached to fill out a survey, 105 responded, and 80% of these completed. The survey consisted of 48 questions covering demographics, relationship to new technologies, views on privacy, views on FRT, and views on regulation. Genetics professionals generally placed a high value on privacy, although specific views differed, were context-specific, and covaried with demographic factors. Most respondents (88%) agreed that privacy is a basic human right, but only 37% placed greater weight on it than other values such as freedom of speech. Most respondents (80%) supported FRT use in genetics, but not necessarily for broader clinical use. A sizeable percentage (39%) were unaware of FRT's lower accuracy rates in marginalized communities and of the mental health effects of privacy violations (62%), but most (76% and 75%, respectively) expressed concern when informed. Overall, women and those who self-identified as politically progressive were more concerned about the lower accuracy rates in marginalized groups (88% vs. 64% and 83% vs. 63%, respectively). Younger geneticists were more wary than older geneticists about using FRT in genetics (28% compared to 56% "strongly" supported such use). There was an overall preference for more regulation, but respondents had low confidence in governments' or technology companies' ability to accomplish this. Privacy views are nuanced and context-dependent. Support for privacy was high but not absolute, and clear deficits existed in awareness of crucial FRT-related discrimination potential and mental health impacts. Education and professional guidelines may help to evolve views and practices within the field.

Protecting medical privacy can create obstacles in the analysis and distribution of healthcare graphs and statistical inferences accompanying them. We pose a graph simulation model which generates networks using degree and property augmentation and provide a flexible R package that allows users to create graphs that preserve vertex attribute relationships and approximating the retention of topological properties observed in the original graph (e.g., community structure). We illustrate our proposed algorithm using a case study based on Zachary's karate network and a patient-sharing graph generated from Medicare claims data in 2019. In both cases, we find that community structure is preserved, and normalized root mean square error between cumulative distributions of the degrees across the generated and the original graphs is low (0.0508 and 0.0514 respectively).

Digital health information has many potential health applications, but privacy is a growing concern among consumers and policy makers. Consent alone is increasingly seen as inadequate to safeguard privacy.

To determine whether different privacy protections are associated with consumers' willingness to share their digital health information for research, marketing, or clinical uses.

This 2020 national survey with an embedded conjoint experiment recruited US adults from a nationally representative sample with oversampling of Black and Hispanic individuals. Willingness to share digital information across 192 different scenarios reflecting the product of 4 possible privacy protections, 3 uses of information, 2 users of information, and 2 sources of digital information was evaluated. Each participant was randomly assigned 9 scenarios. The survey was administrated between July 10 and July 31, 2020, in Spanish and English. Analysis for this study was conducted between May 2021 and July 2022.

Participants rated each conjoint profile on a 5-point Likert scale measuring their willingness to share their personal digital information (with 5 indicating the most willingness to share). Results are reported as adjusted mean differences.

Of the 6284 potential participants, 3539 (56%) responded to the conjoint scenarios. A total of 1858 participants (53%) were female, 758 (21%) identified as Black, 833 (24%) identified as Hispanic, 1149 (33%) had an annual income less than $50 000, and 1274 (36%) were 60 years or older. Participants were more willing to share health information with the presence of each individual privacy protection, including consent (difference, 0.32; 95% CI, 0.29-0.35; P < .001), followed by data deletion (difference, 0.16; 95% CI, 0.13-0.18; P < .001), oversight (difference, 0.13; 95% CI, 0.10-0.15; P < .001), and transparency of data collected (difference, 0.08; 95% CI, 0.05-0.10; P < .001). The relative importance (importance weight on a 0%-100% scale) was greatest for the purpose of use (29.9%) but when considered collectively, the 4 privacy protections together were the most important (51.5%) factor in the conjoint experiment. When the 4 privacy protections were considered separately, consent was the most important (23.9%).

In this survey study of a nationally representative sample of US adults, consumers' willingness to share personal digital health information for health purposes was associated with the presence of specific privacy protections beyond consent alone. Additional protections, including data transparency, oversight, and data deletion may strengthen consumer confidence in sharing their personal digital health information.

The increasing integration of digital health tools into care may result in a greater flow of personal health information (PHI) between patients and providers. Although privacy legislation governs how entities may collect, use, or share PHI, such legislation has not kept pace with digital health innovations, resulting in a lack of guidance on implementing meaningful consent. Understanding patient perspectives when implementing meaningful consent is critical to ensure that it meets their needs. Consent for research in the context of digital health is limited.

This state-of-the-art review aimed to understand the current state of research as it relates to patient perspectives on digital health consent. Its objectives were to explore what is known about the patient perspective and experience with digital health consent and provide recommendations on designing and implementing digital health consent based on the findings.

A structured literature search was developed and deployed in 4 electronic databases-MEDLINE, IEEE Xplore, Scopus, and Web of Science-for articles published after January 2010. The initial literature search was conducted in March 2021 and updated in March 2022. Articles were eligible for inclusion if they discussed electronic consent or consent, focused on the patient perspective or preference, and were related to digital health or digital PHI. Data were extracted using an extraction template and analyzed using qualitative content analysis.

In total, 75 articles were included for analysis. Most studies were published within the last 5 years (58/75, 77%) and conducted in a clinical care context (33/75, 44%) and in the United States (48/75, 64%). Most studies aimed to understand participants' willingness to share PHI (25/75, 33%) and participants' perceived usability and comprehension of an electronic consent notice (25/75, 33%). More than half (40/75, 53%) of the studies did not describe the type of consent model used. The broad open consent model was the most explored (11/75, 15%). Of the 75 studies, 68 (91%) found that participants were willing to provide consent; however, their consent behaviors and preferences were context-dependent. Common patient consent requirements included clear and digestible information detailing who can access PHI, for what purpose their PHI will be used, and how privacy will be ensured.

There is growing interest in understanding the patient perspective on digital health consent in the context of providing clinical care. There is evidence suggesting that many patients are willing to consent for various purposes, especially when there is greater transparency on how the PHI is used and oversight mechanisms are in place. Providing this transparency is critical for fostering trust in digital health tools and the innovative uses of data to optimize health and system outcomes.

Digital and mobile (mHealth) solutions are online or application-based services intended to support individuals with health needs. Despite evidence supporting the use of mHealth for patients with chronic pain, and the increasing desire of these types of solutions by both patients and providers, adoption of mHealth solutions remains limited. Implementation mapping can serve as a practical method to facilitate implementation and adoption of mHealth solutions within healthcare settings.

Implementation mapping was used to develop implementation strategies based on contextual determinants organized within the Consolidated Framework for Implementation Research (CFIR) for mHealth eLearning solutions across an integrated, multi-site healthcare system. We describe our experience identifying stakeholders, delineating implementation facilitators and barriers, defining implementation outcomes using RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework, outlining initial implementation strategies, and iterating on implementation strategies.

A total of 30 implementation strategies were identified and implemented. Over the first year, primary and specialty care providers across all the clinical sites (n = 70) placed 2559 orders for the mHealth solution. Most patients reported receiving the mHealth eLearning module (74%), and most patients felt that the tool improved their knowledge regarding their condition (82%) and their ability to provide self-care related to the condition (73%).

Practical applications of implementation science methods can help enable change within healthcare settings. Implementation mapping is an exercise that can engage stakeholders to facilitate the incorporation of new methods of care delivery, including mHealth solutions.

This is a review on the use of artificial intelligence for digital breast pathology. A systematic search on PubMed was conducted, identifying 17,324 research papers related to breast cancer pathology. Following a semimanual screening, 664 papers were retrieved and pursued. The papers are grouped into six major tasks performed by pathologists-namely, molecular and hormonal analysis, grading, mitotic figure counting, ki-67 indexing, tumour-infiltrating lymphocyte assessment, and lymph node metastases identification. Under each task, open-source datasets for research to build artificial intelligence (AI) tools are also listed. Many AI tools showed promise and demonstrated feasibility in the automation of routine pathology investigations. We expect continued growth of AI in this field as new algorithms mature.