Emerging Artificial Intelligence–Empowered mHealth: Scoping Review

Advances in biosensors for major depressive disorder diagnostic biomarkers

Depression is one of the most common mental disorders and is mainly characterized by low mood or lack of interest and pleasure. It can be accompanied by varying degrees of cognitive and behavioral changes and may lead to suicide risk in severe cases. Due to the subjectivity of diagnostic methods and the complexity of patients' conditions, the diagnosis of major depressive disorder (MDD) has always been a difficult problem in psychiatry. With the discovery of more diagnostic biomarkers associated with MDD in recent years, especially emerging non-coding RNAs (ncRNAs), it is possible to quantify the condition of patients with mental illness based on biomarker levels. Point-of-care biosensors have emerged due to their advantages of convenient sampling, rapid detection, miniaturization, and portability. After summarizing the pathogenesis of MDD, representative biomarkers, including proteins, hormones, and RNAs, are discussed. Furthermore, we analyzed recent advances in biosensors for detecting various types of biomarkers of MDD, highlighting representative electrochemical sensors. Future trends in terms of new biomarkers, new sample processing methods, and new detection modalities are expected to provide a complete reference for psychiatrists and biomedical engineers.

Just-in-Time Adaptive Intervention for Stabilizing Sleep Hours of Japanese Workers: Microrandomized Trial

BACKGROUND

Sleep disturbance is a major contributor to future health and occupational issues. Mobile health can provide interventions that address adverse health behaviors for individuals in a vulnerable health state in real-world settings (just-in-time adaptive intervention).

OBJECTIVE

This study aims to identify a subpopulation with vulnerable sleep state in daily life (study 1) and, immediately afterward, to test whether providing mobile health intervention improved habitual sleep behaviors and psychological wellness in real-world settings by conducting a microrandomized trial (study 2).

METHODS

Japanese workers (n=182) were instructed to collect data on their habitual sleep behaviors and momentary symptoms (including depressive mood, anxiety, and subjective sleep quality) using digital devices in a real-world setting. In study 1, we calculated intraindividual mean and variability of sleep hours, midpoint of sleep, and sleep efficiency to characterize their habitual sleep behaviors. In study 2, we designed and conducted a sleep just-in-time adaptive intervention, which delivered objective push-type sleep feedback messages to improve their sleep hours for a subset of participants in study 1 (n=81). The feedback messages were generated based on their sleep data measured on previous nights and were randomly sent to participants with a 50% chance for each day (microrandomization).

RESULTS

In study 1, we applied hierarchical clustering to dichotomize the population into 2 clusters (group A and group B) and found that group B was characterized by unstable habitual sleep behaviors (large intraindividual variabilities). In addition, linear mixed-effect models showed that the interindividual variability of sleep hours was significantly associated with depressive mood (β=3.83; P=.004), anxiety (β=5.70; P=.03), and subjective sleep quality (β=-3.37; P=.03). In study 2, we found that providing sleep feedback prolonged subsequent sleep hours (increasing up to 40 min; P=.01), and this effect lasted for up to 7 days. Overall, the stability of sleep hours in study 2 was significantly improved among participants in group B compared with the participants in study 1 (P=.001).

CONCLUSIONS

This is the first study to demonstrate that providing sleep feedback can benefit the modification of habitual sleep behaviors in a microrandomized trial. The findings of this study encourage the use of digitalized health intervention that uses real-time health monitoring and personalized feedback.

Analyzing the Impact of Personalization on Fairness in Federated Learning for Healthcare

As machine learning (ML) usage becomes more popular in the healthcare sector, there are also increasing concerns about potential biases and risks such as privacy. One countermeasure is to use federated learning (FL) to support collaborative learning without the need for patient data sharing across different organizations. However, the inherent heterogeneity of data distributions among participating FL parties poses challenges for exploring group fairness in FL. While personalization within FL can handle performance degradation caused by data heterogeneity, its influence on group fairness is not fully investigated. Therefore, the primary focus of this study is to rigorously assess the impact of personalized FL on group fairness in the healthcare domain, offering a comprehensive understanding of how personalized FL affects group fairness in clinical outcomes. We conduct an empirical analysis using two prominent real-world Electronic Health Records (EHR) datasets, namely eICU and MIMIC-IV. Our methodology involves a thorough comparison between personalized FL and two baselines: standalone training, where models are developed independently without FL collaboration, and standard FL, which aims to learn a global model via the FedAvg algorithm. We adopt Ditto as our personalized FL approach, which enables each client in FL to develop its own personalized model through multi-task learning. Our assessment is achieved through a series of evaluations, comparing the predictive performance (i.e., AUROC and AUPRC) and fairness gaps (i.e., EOPP, EOD, and DP) of these methods. Personalized FL demonstrates superior predictive accuracy and fairness over standalone training across both datasets. Nevertheless, in comparison with standard FL, personalized FL shows improved predictive accuracy but does not consistently offer better fairness outcomes. For instance, in the 24-h in-hospital mortality prediction task, personalized FL achieves an average EOD of 27.4% across racial groups in the eICU dataset and 47.8% in MIMIC-IV. In comparison, standard FL records a better EOD of 26.2% for eICU and 42.0% for MIMIC-IV, while standalone training yields significantly worse EOD of 69.4% and 54.7% on these datasets, respectively. Our analysis reveals that personalized FL has the potential to enhance fairness in comparison to standalone training, yet it does not consistently ensure fairness improvements compared to standard FL. Our findings also show that while personalization can improve fairness for more biased hospitals (i.e., hospitals having larger fairness gaps in standalone training), it can exacerbate fairness issues for less biased ones. These insights suggest that the integration of personalized FL with additional strategic designs could be key to simultaneously boosting prediction accuracy and reducing fairness disparities. The findings and opportunities outlined in this paper can inform the research agenda for future studies, to overcome the limitations and further advance health equity research.

AI Quality Standards in Health Care: Rapid Umbrella Review

BACKGROUND

In recent years, there has been an upwelling of artificial intelligence (AI) studies in the health care literature. During this period, there has been an increasing number of proposed standards to evaluate the quality of health care AI studies.

OBJECTIVE

This rapid umbrella review examines the use of AI quality standards in a sample of health care AI systematic review articles published over a 36-month period.

METHODS

We used a modified version of the Joanna Briggs Institute umbrella review method. Our rapid approach was informed by the practical guide by Tricco and colleagues for conducting rapid reviews. Our search was focused on the MEDLINE database supplemented with Google Scholar. The inclusion criteria were English-language systematic reviews regardless of review type, with mention of AI and health in the abstract, published during a 36-month period. For the synthesis, we summarized the AI quality standards used and issues noted in these reviews drawing on a set of published health care AI standards, harmonized the terms used, and offered guidance to improve the quality of future health care AI studies.

RESULTS

We selected 33 review articles published between 2020 and 2022 in our synthesis. The reviews covered a wide range of objectives, topics, settings, designs, and results. Over 60 AI approaches across different domains were identified with varying levels of detail spanning different AI life cycle stages, making comparisons difficult. Health care AI quality standards were applied in only 39% (13/33) of the reviews and in 14% (25/178) of the original studies from the reviews examined, mostly to appraise their methodological or reporting quality. Only a handful mentioned the transparency, explainability, trustworthiness, ethics, and privacy aspects. A total of 23 AI quality standard-related issues were identified in the reviews. There was a recognized need to standardize the planning, conduct, and reporting of health care AI studies and address their broader societal, ethical, and regulatory implications.

CONCLUSIONS

Despite the growing number of AI standards to assess the quality of health care AI studies, they are seldom applied in practice. With increasing desire to adopt AI in different health topics, domains, and settings, practitioners and researchers must stay abreast of and adapt to the evolving landscape of health care AI quality standards and apply these standards to improve the quality of their AI studies.

Artificial Intelligence in Endocrinology: On Track Toward Great Opportunities

In endocrinology, the types and quantity of digital data are increasing rapidly. Computing capabilities are also developing at an incredible rate, as illustrated by the recent expansion in the use of popular generative artificial intelligence (AI) applications. Numerous diagnostic and therapeutic devices using AI have already entered routine endocrine practice, and developments in this field are expected to continue to accelerate. Endocrinologists will need to be supported in managing AI applications. Beyond technological training, interdisciplinary vision is needed to encompass the ethical and legal aspects of AI, to manage the profound impact of AI on patient/provider relationships, and to maintain an optimal balance between human input and AI in endocrinology.

Exploring the Use and Implications of AI in Sexual and Reproductive Health and Rights: Protocol for a Scoping Review

BACKGROUND

Artificial intelligence (AI) has emerged as a transformative force across the health sector and has garnered significant attention within sexual and reproductive health and rights (SRHR) due to polarizing views on its opportunities to advance care and the heightened risks and implications it brings to people's well-being and bodily autonomy. As the fields of AI and SRHR evolve, clarity is needed to bridge our understanding of how AI is being used within this historically politicized health area and raise visibility on the critical issues that can facilitate its responsible and meaningful use.

OBJECTIVE

This paper presents the protocol for a scoping review to synthesize empirical studies that focus on the intersection of AI and SRHR. The review aims to identify the characteristics of AI systems and tools applied within SRHR, regarding health domains, intended purpose, target users, AI data life cycle, and evidence on benefits and harms.

METHODS

The scoping review follows the standard methodology developed by Arksey and O'Malley. We will search the following electronic databases: MEDLINE (PubMed), Scopus, Web of Science, and CINAHL. Inclusion criteria comprise the use of AI systems and tools in sexual and reproductive health and clear methodology describing either quantitative or qualitative approaches, including program descriptions. Studies will be excluded if they focus entirely on digital interventions that do not explicitly use AI systems and tools, are about robotics or nonhuman subjects, or are commentaries. We will not exclude articles based on geographic location, language, or publication date. The study will present the uses of AI across sexual and reproductive health domains, the intended purpose of the AI system and tools, and maturity within the AI life cycle. Outcome measures will be reported on the effect, accuracy, acceptability, resource use, and feasibility of studies that have deployed and evaluated AI systems and tools. Ethical and legal considerations, as well as findings from qualitative studies, will be synthesized through a narrative thematic analysis. We will use the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) format for the publication of the findings.

RESULTS

The database searches resulted in 12,793 records when the searches were conducted in October 2023. Screening is underway, and the analysis is expected to be completed by July 2024.

CONCLUSIONS

The findings will provide key insights on usage patterns and evidence on the use of AI in SRHR, as well as convey key ethical, safety, and legal considerations. The outcomes of this scoping review are contributing to a technical brief developed by the World Health Organization and will guide future research and practice in this highly charged area of work.

TRIAL REGISTRATION

OSF Registries osf.io/ma4d9; https://osf.io/ma4d9.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/53888.

Translating Suicide Safety Planning Components Into the Design of mHealth App Features: Systematic Review

BACKGROUND

Suicide safety planning is an evidence-based approach used to help individuals identify strategies to keep themselves safe during a mental health crisis. This study systematically reviewed the literature focused on mobile health (mHealth) suicide safety planning apps.

OBJECTIVE

This study aims to evaluate the extent to which apps integrated components of the safety planning intervention (SPI), and if so, how these safety planning components were integrated into the design-based features of the apps.

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically analyzed 14 peer-reviewed studies specific to mHealth apps for suicide safety planning. We conducted an analysis of the literature to evaluate how the apps incorporated SPI components and examined similarities and differences among the apps by conducting a comparative analysis of app features. An independent review of SPI components and app features was conducted by downloading the available apps.

RESULTS

Most of the mHealth apps (5/7, 71%) integrated SPI components and provided customizable features that expanded upon traditional paper-based safety planning processes. App design features were categorized into 5 themes, including interactive features, individualized user experiences, interface design, guidance and training, and privacy and sharing. All apps included access to community supports and revisable safety plans. Fewer mHealth apps (3/7, 43%) included interactive features, such as associating coping strategies with specific stressors. Most studies (10/14, 71%) examined the usability, feasibility, and acceptability of the safety planning mHealth apps. Usability findings were generally positive, as users often found these apps easy to use and visually appealing. In terms of feasibility, users preferred using mHealth apps during times of crisis, but the continuous use of the apps outside of crisis situations received less support. Few studies (4/14, 29%) examined the effectiveness of mHealth apps for suicide-related outcomes. Positive shifts in attitudes and desire to live, improved coping strategies, enhanced emotional stability, and a decrease in suicidal thoughts or self-harm behaviors were examined in these studies.

CONCLUSIONS

Our study highlights the need for researchers, clinicians, and app designers to continue to work together to align evidence-based research on mHealth suicide safety planning apps with lessons learned for how to best deliver these technologies to end users. Our review brings to light mHealth suicide safety planning strategies needing further development and testing, such as lethal means guidance, collaborative safety planning, and the opportunity to embed more interactive features that leverage the advanced capabilities of technology to improve client outcomes as well as foster sustained user engagement beyond a crisis. Although preliminary evidence shows that these apps may help to mitigate suicide risk, clinical trials with larger sample sizes and more robust research designs are needed to validate their efficacy before the widespread adoption and use.

Development of a crowdsourcing- and gamification-based mobile application to collect epidemiological information and promote healthy lifestyles in Mexico

We developed a mobile application to promote healthy lifestyles and collect non-communicable disease (NCD) data in Mexico. Its theoretical foundations are supported by a framework-guided literature review. With design sprints, Scrum, Model-View-Controller, and Representational State Transfer architecture, we operationalized evidence-based nutrition/physical activity information into a crowdsourcing- and gamification-based application. The application was piloted for three months to monitor the response of 520 adults. Potential improvements were characterized, considering benchmarking, expert guidance, and standards. Salud Activa (English: Active Health) has two crowdsourcing modules: Nutritional scanner, scanning products' bar codes, providing nutritional data, and allowing new product registry feeding our databases; Surveys, comprising gradually-released NCD questions. Three intervention modules were generated: Drinks diary, a beverage assessment component to receive hydration recommendations; Step counter, monitoring users' steps via Google Fit/Health-iOS; Metabolic Avatar, interconnecting modules and changing as a function of beverage and step records. The 3-month median of Salud Activa use was seven days (IQR = 3-12), up to 35% of participants completed a Survey section, and 157 food products were registered through Nutritional scanner. Better customization might benefit usability and user engagement. Quantitative and qualitative data will enhance Salud Activa's design, user uptake, and efficacy in interventions delivered through this platform.

Leveraging artificial intelligence for perioperative cancer risk assessment of oral potentially malignant disorders

Oral potentially malignant disorders (OPMDs) are mucosal conditions with an inherent disposition to develop oral squamous cell carcinoma. Surgical management is the most preferred strategy to prevent malignant transformation in OPMDs, and surgical approaches to treatment include conventional scalpel excision, laser surgery, cryotherapy, and photodynamic therapy. However, in reality, since all patients with OPMDs will not develop oral squamous cell carcinoma in their lifetime, there is a need to stratify patients according to their risk of malignant transformation to streamline surgical intervention for patients with the highest risks. Artificial intelligence (AI) has the potential to integrate disparate factors influencing malignant transformation for robust, precise, and personalized cancer risk stratification of OPMD patients than current methods to determine the need for surgical resection, excision, or re-excision. Therefore, this article overviews existing AI models and tools, presents a clinical implementation pathway, and discusses necessary refinements to aid the clinical application of AI-based platforms for cancer risk stratification of OPMDs in surgical practice.

Diagnosis and management of subarachnoid haemorrhage

Aneurysmal subarachnoid haemorrhage (aSAH) presents a challenge to clinicians because of its multisystem effects. Advancements in computed tomography (CT), endovascular treatments, and neurocritical care have contributed to declining mortality rates. The critical care of aSAH prioritises cerebral perfusion, early aneurysm securement, and the prevention of secondary brain injury and systemic complications. Early interventions to mitigate cardiopulmonary complications, dyselectrolytemia and treatment of culprit aneurysm require a multidisciplinary approach. Standardised neurological assessments, transcranial doppler (TCD), and advanced imaging, along with hypertensive and invasive therapies, are vital in reducing delayed cerebral ischemia and poor outcomes. Health care disparities, particularly in the resource allocation for SAH treatment, affect outcomes significantly, with telemedicine and novel technologies proposed to address this health inequalities. This article underscores the necessity for comprehensive multidisciplinary care and the urgent need for large-scale studies to validate standardised treatment protocols for improved SAH outcomes.

Revolutionizing Biological Science: The Synergy of Genomics in Health, Bioinformatics, Agriculture, and Artificial Intelligence

With climate emergency, COVID-19, and the rise of planetary health scholarship, the binary of human and ecosystem health has been deeply challenged. The interdependence of human and nonhuman animal health is increasingly acknowledged and paving the way for new frontiers in integrative biology. The convergence of genomics in health, bioinformatics, agriculture, and artificial intelligence (AI) has ushered in a new era of possibilities and applications. However, the sheer volume of genomic/multiomics big data generated also presents formidable sociotechnical challenges in extracting meaningful biological, planetary health and ecological insights. Over the past few years, AI-guided bioinformatics has emerged as a powerful tool for managing, analyzing, and interpreting complex biological datasets. The advances in AI, particularly in machine learning and deep learning, have been transforming the fields of genomics, planetary health, and agriculture. This article aims to unpack and explore the formidable range of possibilities and challenges that result from such transdisciplinary integration, and emphasizes its radically transformative potential for human and ecosystem health. The integration of these disciplines is also driving significant advancements in precision medicine and personalized health care. This presents an unprecedented opportunity to deepen our understanding of complex biological systems and advance the well-being of all life in planetary ecosystems. Notwithstanding in mind its sociotechnical, ethical, and critical policy challenges, the integration of genomics, multiomics, planetary health, and agriculture with AI-guided bioinformatics opens up vast opportunities for transnational collaborative efforts, data sharing, analysis, valorization, and interdisciplinary innovations in life sciences and integrative biology.

Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges

The use of artificial intelligence (AI) and big data in medicine has increased in recent years. Indeed, the use of AI in mobile health (mHealth) apps could considerably assist both individuals and health care professionals in the prevention and management of chronic diseases, in a person-centered manner. Nonetheless, there are several challenges that must be overcome to provide high-quality, usable, and effective mHealth apps. Here, we review the rationale and guidelines for the implementation of mHealth apps and the challenges regarding quality, usability, and user engagement and behavior change, with a special focus on the prevention and management of noncommunicable diseases. We suggest that a cocreation-based framework is the best method to address these challenges. Finally, we describe the current and future roles of AI in improving personalized medicine and provide recommendations for developing AI-based mHealth apps. We conclude that the implementation of AI and mHealth apps for routine clinical practice and remote health care will not be feasible until we overcome the main challenges regarding data privacy and security, quality assessment, and the reproducibility and uncertainty of AI results. Moreover, there is a lack of both standardized methods to measure the clinical outcomes of mHealth apps and techniques to encourage user engagement and behavior changes in the long term. We expect that in the near future, these obstacles will be overcome and that the ongoing European project, Watching the risk factors (WARIFA), will provide considerable advances in the implementation of AI-based mHealth apps for disease prevention and health promotion.

Exploring digital biomarkers of illness activity in mood episodes: hypotheses generating and model development study

BACKGROUND

Depressive and manic episodes within bipolar disorder (BD) and major depressive disorder (MDD) involve altered mood, sleep, and activity alongside physiological alterations that wearables can capture.

OBJECTIVE

We explored whether physiological wearable data could predict: (aim 1) the severity of an acute affective episode at the intra-individual level, (aim 2) the polarity of an acute affective episode and euthymia among different individuals. Secondarily, we explored which physiological data were related to the prior predictions, generalization across patients, and associations between affective symptoms and physiological data.

METHODS

We conducted a prospective exploratory observational study including patients with BD and MDD on acute affective episodes (manic, depressed, and mixed) whose physiological data were recorded with a research-grade wearable (Empatica E4) across three consecutive timepoints (acute, response, and remission of episode). Euthymic patients and healthy controls (HC) were recorded during a single session (∼48 hours). Manic and depressive symptoms were assessed with standardized psychometric scales. Physiological wearable data included the following channels: acceleration (ACC), temperature (TEMP), blood volume pulse (BVP), heart rate (HR), and electrodermal activity (EDA). For data pre-processing, invalid physiological data were removed using a rule-based filter, channels were time-aligned at 1 second time units and then segmented window lengths of 32 seconds, since those parameters showed the best performances. We developed deep learning predictive models, assessed channels' individual contribution using permutation feature importance analysis, and computed physiological data to psychometric scales' items normalized mutual information (NMI). We present a novel fully automated method for analysis of physiological data from a research-grade wearable device, including a rule-based filter for invalid data and a viable supervised learning pipeline for time-series analyses.

RESULTS

35 sessions (1,512 hours) from 12 patients (manic, depressed, mixed, and euthymic) and 7 HC (age 39.7±12.6; 31.6% female) were analyzed. (aim 1) The severity of mood episodes was predicted with moderate (62%-85%) accuracies. (aim 2) The polarity of episodes was predicted with moderate (70%) accuracy. The most relevant features for the former tasks were ACC, EDA, and HR. Kendall W showed fair agreement (0.383) in feature importance across classification tasks. Generalization of the former models were of overall low accuracy, with better results for the intra-individual models. "Increased motor activity" was associated with ACC (NMI>0.55), "aggressive behavior" with EDA (NMI=1.0), "insomnia" with ACC (NMI∼0.6), "motor inhibition" with ACC (NMI∼0.75), and "psychic anxiety" with EDA (NMI=0.52).

CONCLUSIONS

Physiological data from wearables show potential to identify mood episodes and specific symptoms of mania and depression quantitatively, both in BD and MDD. Motor activity and stress-related physiological data (EDA and HR) stand out as potential digital biomarkers for predicting mania and depression respectively. These findings represent a promising pathway towards personalized psychiatry, in which physiological wearable data could allow early identification and intervention of mood episodes.

CLINICALTRIAL

Artificial Intelligence in Medicine and Dentistry

INTRODUCTION

Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry.

OBJECTIVE

Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages.

CONCLUSION

The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.