Validation of a Virtual Assistant for Improving Medication Adherence in Patients with Comorbid Type 2 Diabetes Mellitus and Depressive Disorder

Artificial intelligence virtual assistants in primary eye care practice

PURPOSE

To propose a novel artificial intelligence (AI)-based virtual assistant trained on tabular clinical data that can provide decision-making support in primary eye care practice and optometry education programmes.

METHOD

Anonymised clinical data from 1125 complete optometric examinations (2250 eyes; 63% women, 37% men) were used to train different machine learning algorithm models to predict eye examination classification (refractive, binocular vision dysfunction, ocular disorder or any combination of these three options). After modelling, adjustment, mining and preprocessing (one-hot encoding and SMOTE techniques), 75 input (preliminary data, history, oculomotor test and ocular examinations) and three output (refractive, binocular vision status and eye disease) features were defined. The data were split into training (80%) and test (20%) sets. Five machine learning algorithms were trained, and the best algorithms were subjected to fivefold cross-validation. Model performance was evaluated for accuracy, precision, sensitivity, F1 score and specificity.

RESULTS

The random forest algorithm was the best for classifying eye examination results with a performance >95.2% (based on 35 input features from preliminary data and history), to propose a subclassification of ocular disorders with a performance >98.1% (based on 65 features from preliminary data, history and ocular examinations) and to differentiate binocular vision dysfunctions with a performance >99.7% (based on 30 features from preliminary data and oculomotor tests). These models were integrated into a responsive web application, available in three languages, allowing intuitive access to the AI models via conventional clinical terms.

CONCLUSIONS

An AI-based virtual assistant that performed well in predicting patient classification, eye disorders or binocular vision dysfunction has been developed with potential use in primary eye care practice and education programmes.

Exploring the Role of Voice Assistants in Managing Noncommunicable Diseases: A Systematic Review on Clinical, Behavioral Outcomes, Quality of Life, and User Experiences

BACKGROUND

Non-communicable diseases (NCDs) represent a leading cause of global mortality, demanding innovative approaches to management. Voice assistants (VAs) have emerged as promising tools in healthcare, offering support for self-management, behavioral engagement, and patient care. This systematic review evaluates the role of VAs in NCD management, analyzing their impact on clinical and behavioral outcomes, quality of life, usability, and user experiences while identifying barriers to their adoption.

METHODS

A systematic search was conducted in PubMed, Scopus, and Web of Science from January 2014 to October 2024. Studies were selected based on predefined inclusion and exclusion criteria using the PRISMA guidelines. Data extraction focused on outcomes such as usability, acceptability, adherence, clinical metrics, and quality of life. The risk of bias was assessed using the Cochrane Risk of Bias (RoB) 2 and ROBINS-I tools.

RESULTS

Eight studies involving 541 participants were included, examining VAs across various NCD contexts such as diabetes, cardiovascular diseases, and mental health. While VAs demonstrated good usability and moderate adherence, their clinical and quality-of-life outcomes were modest. Behavioral improvements, such as increased physical activity and problem-solving skills, were noted in some interventions. Key challenges included privacy concerns, speech recognition errors, and accessibility issues.

CONCLUSIONS

VAs show potential as supportive tools in NCD management, especially for enhancing patient engagement and self-management, and their impact on clinical outcomes and long-term usability requires further investigation. Future research should focus on diverse populations, standardized metrics, and comparative studies with alternative technologies.

Current Implementation of Digital Health in Chronic Disease Management: Scoping Review

BACKGROUND

Approximately 1 in 3 adults live with multiple chronic diseases. Digital health is being harnessed to improve continuity of care and management of chronic diseases. However, meaningful uptake of digital health for chronic disease management remains low. It is unclear how these innovations have been implemented and evaluated.

OBJECTIVE

This scoping review aims to identify how digital health innovations for chronic disease management have been implemented and evaluated: what implementation frameworks, methods, and strategies were used; how successful these strategies were; key barriers and enablers to implementation; and lessons learned and recommendations shared by study authors.

METHODS

We used the Joanna Briggs Institute methodology for scoping reviews. Five databases were searched for studies published between January 2015 and March 2023: PubMed, Scopus, CINAHL, PsycINFO, and IEEE Xplore. We included primary studies of any study design with any type of digital health innovations for chronic diseases that benefit patients, caregivers, or health care professionals. We extracted study characteristics; type of digital health innovation; implementation frameworks, strategies, and outcome measures used; barriers and enablers to implementation; lessons learned; and recommendations reported by study authors. We used established taxonomies to synthesize extracted data. Extracted barriers and enablers were grouped into categories for reporting. Descriptive statistics were used to consolidate extracted data.

RESULTS

A total of 252 studies were included, comprising mainly mobile health (107/252, 42.5%), eHealth (61/252, 24.2%), and telehealth (97/252, 38.5%), with some studies involving more than 1 innovation. Only 23 studies (23/252, 9.1%) reported using an implementation science theory, model, or framework; the most common were implementation theories, classic theories, and determinant frameworks, with 7 studies each. Of 252 studies, 144 (57.1%) used 2 to 5 implementation strategies. Frequently used strategies were "obtain and use patient or consumer feedback" (196/252, 77.8%); "audit and provide feedback" (106/252, 42.1%); and piloting before implementation or "stage implementation scale-up" (85/252, 33.7%). Commonly measured implementation outcomes were acceptability, feasibility, and adoption of the digital innovation. Of 252 studies, 247 studies (98%) did not measure service outcomes, while patient health outcomes were measured in 89 studies (35.3%). The main method used to assess outcomes was surveys (173/252, 68.7%), followed by interviews (95/252, 37.7%). Key barriers impacting implementation were data privacy concerns and patient preference for in-person consultations. Key enablers were training for health care workers and personalization of digital health features to patient needs.

CONCLUSIONS

This review generated a summary of how digital health in chronic disease management is currently implemented and evaluated and serves as a useful resource for clinicians, researchers, health system managers, and policy makers planning real-world implementation. Future studies should investigate whether using implementation science frameworks, including how well they are used, would yield better outcomes compared to not using them.

Application of Chatbots to Help Patients Self-Manage Diabetes: Systematic Review and Meta-Analysis

BACKGROUND

The number of people with diabetes is on the rise globally. Self-management and health education of patients are the keys to control diabetes. With the development of digital therapies and artificial intelligence, chatbots have the potential to provide health-related information and improve accessibility and effectiveness in the field of patient self-management.

OBJECTIVE

This study systematically reviews the current research status and effectiveness of chatbots in the field of diabetes self-management to support the development of diabetes chatbots.

METHODS

A systematic review and meta-analysis of chatbots that can help patients with diabetes with self-management was conducted. PubMed and Web of Science databases were searched using keywords around diabetes, chatbots, conversational agents, virtual assistants, and more. The search period was from the date of creation of the databases to January 1, 2023. Research articles in English that fit the study topic were selected, and articles that did not fit the study topic or were not available in full text were excluded.

RESULTS

In total, 25 studies were included in the review. In terms of study type, all articles could be classified as systematic design studies (n=8, 32%), pilot studies (n=8, 32%), and intervention studies (n=9, 36%). Many articles adopted a nonrandomized controlled trial design in intervention studies (n=6, 24%), and there was only 1 (4%) randomized controlled trial. In terms of research strategy, all articles can be divided into quantitative studies (n=10, 40%), mixed studies (n=6, 24%), and qualitative studies (n=1, 4%). The evaluation criteria for chatbot effectiveness can be divided into technical performance evaluation, user experience evaluation, and user health evaluation. Most chatbots (n=17, 68%) provided education and management focused on patient diet, exercise, glucose monitoring, medications, and complications, and only a few studies (n=2, 8%) provided education on mental health. The meta-analysis found that the chatbot intervention was effective in lowering blood glucose (mean difference 0.30, 95% CI 0.04-0.55; P=.02) and had no significant effect in reducing weight (mean difference 1.41, 95% CI -2.29 to 5.11; P=.46) compared with the baseline.

CONCLUSIONS

Chatbots have potential for the development of self-management for people with diabetes. However, the evidence level of current research is low, and higher level research (such as randomized controlled trials) is needed to strengthen the evidence base. More use of mixed research in the research strategy is needed to fully use the strengths of both quantitative and qualitative research. Appropriate and innovative theoretical frameworks should be used in the research to provide theoretical support for the study. In addition, researchers should focus on the personalized and user-friendly interactive features of chatbots, as well as improvements in study design.

Comprehensive review of virtual assistants in vascular surgery

Virtual assistants, broadly defined as digital services designed to simulate human conversation and provide personalized responses based on user input, have the potential to improve health care by supporting clinicians and patients in terms of diagnosing and managing disease, performing administrative tasks, and supporting medical research and education. These tasks are particularly helpful in vascular surgery, where the clinical and administrative burden is high due to the rising incidence of vascular disease, the medical complexity of the patients, and the potential for innovation and care advancement. The rapid development of artificial intelligence, machine learning, and natural language processing techniques have facilitated the training of large language models, such as GPT-4 (OpenAI), which can support the development of increasingly powerful virtual assistants. These tools may support holistic, multidisciplinary, and high-quality vascular care delivery throughout the pre-, intra-, and postoperative stages. Importantly, it is critical to consider the design, safety, and challenges related to virtual assistants, including data security, ethical, and equity concerns. By combining the perspectives of patients, clinicians, data scientists, and other stakeholders when developing, implementing, and monitoring virtual assistants, there is potential to harness the power of this technology to care for vascular surgery patients more effectively. In this comprehensive review article, we introduce the concept of virtual assistants, describe potential applications of virtual assistants in vascular surgery for clinicians and patients, highlight the benefits and drawbacks of large language models, such as GPT-4, and discuss considerations around the design, safety, and challenges associated with virtual assistants in vascular surgery.

Engagement With Conversational Agent-Enabled Interventions in Cardiometabolic Disease Management: Protocol for a Systematic Review

BACKGROUND

Cardiometabolic diseases (CMDs) are a group of interrelated conditions, including heart failure and diabetes, that increase the risk of cardiovascular and metabolic complications. The rising number of Australians with CMDs has necessitated new strategies for those managing these conditions, such as digital health interventions. The effectiveness of digital health interventions in supporting people with CMDs is dependent on the extent to which users engage with the tools. Augmenting digital health interventions with conversational agents, technologies that interact with people using natural language, may enhance engagement because of their human-like attributes. To date, no systematic review has compiled evidence on how design features influence the engagement of conversational agent-enabled interventions supporting people with CMDs. This review seeks to address this gap, thereby guiding developers in creating more engaging and effective tools for CMD management.

OBJECTIVE

The aim of this systematic review is to synthesize evidence pertaining to conversational agent-enabled intervention design features and their impacts on the engagement of people managing CMD.

METHODS

The review is conducted in accordance with the Cochrane Handbook for Systematic Reviews of Interventions and reported in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Searches will be conducted in the Ovid (Medline), Web of Science, and Scopus databases, which will be run again prior to manuscript submission. Inclusion criteria will consist of primary research studies reporting on conversational agent-enabled interventions, including measures of engagement, in adults with CMD. Data extraction will seek to capture the perspectives of people with CMD on the use of conversational agent-enabled interventions. Joanna Briggs Institute critical appraisal tools will be used to evaluate the overall quality of evidence collected.

RESULTS

This review was initiated in May 2023 and was registered with the International Prospective Register of Systematic Reviews (PROSPERO) in June 2023, prior to title and abstract screening. Full-text screening of articles was completed in July 2023 and data extraction began August 2023. Final searches were conducted in April 2024 prior to finalizing the review and the manuscript was submitted for peer review in July 2024.

CONCLUSIONS

This review will synthesize diverse observations pertaining to conversational agent-enabled intervention design features and their impacts on engagement among people with CMDs. These observations can be used to guide the development of more engaging conversational agent-enabled interventions, thereby increasing the likelihood of regular intervention use and improved CMD health outcomes. Additionally, this review will identify gaps in the literature in terms of how engagement is reported, thereby highlighting areas for future exploration and supporting researchers in advancing the understanding of conversational agent-enabled interventions.

TRIAL REGISTRATION

PROSPERO CRD42023431579; https://tinyurl.com/55cxkm26.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/52973.

Evaluating the Potential and Pitfalls of AI-Powered Conversational Agents as Humanlike Virtual Health Carers in the Remote Management of Noncommunicable Diseases: Scoping Review

BACKGROUND

The rising prevalence of noncommunicable diseases (NCDs) worldwide and the high recent mortality rates (74.4%) associated with them, especially in low- and middle-income countries, is causing a substantial global burden of disease, necessitating innovative and sustainable long-term care solutions.

OBJECTIVE

This scoping review aims to investigate the impact of artificial intelligence (AI)-based conversational agents (CAs)-including chatbots, voicebots, and anthropomorphic digital avatars-as human-like health caregivers in the remote management of NCDs as well as identify critical areas for future research and provide insights into how these technologies might be used effectively in health care to personalize NCD management strategies.

METHODS

A broad literature search was conducted in July 2023 in 6 electronic databases-Ovid MEDLINE, Embase, PsycINFO, PubMed, CINAHL, and Web of Science-using the search terms "conversational agents," "artificial intelligence," and "noncommunicable diseases," including their associated synonyms. We also manually searched gray literature using sources such as ProQuest Central, ResearchGate, ACM Digital Library, and Google Scholar. We included empirical studies published in English from January 2010 to July 2023 focusing solely on health care-oriented applications of CAs used for remote management of NCDs. The narrative synthesis approach was used to collate and summarize the relevant information extracted from the included studies.

RESULTS

The literature search yielded a total of 43 studies that matched the inclusion criteria. Our review unveiled four significant findings: (1) higher user acceptance and compliance with anthropomorphic and avatar-based CAs for remote care; (2) an existing gap in the development of personalized, empathetic, and contextually aware CAs for effective emotional and social interaction with users, along with limited consideration of ethical concerns such as data privacy and patient safety; (3) inadequate evidence of the efficacy of CAs in NCD self-management despite a moderate to high level of optimism among health care professionals regarding CAs' potential in remote health care; and (4) CAs primarily being used for supporting nonpharmacological interventions such as behavioral or lifestyle modifications and patient education for the self-management of NCDs.

CONCLUSIONS

This review makes a unique contribution to the field by not only providing a quantifiable impact analysis but also identifying the areas requiring imminent scholarly attention for the ethical, empathetic, and efficacious implementation of AI in NCD care. This serves as an academic cornerstone for future research in AI-assisted health care for NCD management.

TRIAL REGISTRATION

Open Science Framework; https://doi.org/10.17605/OSF.IO/GU5PX.

Service innovations for people with multiple long-term conditions: reflections of a rapid evaluation team

Background

People living with multiple long-term conditions represent a significant concern for National Health Service policy and practice, and their care is a major theme in the 2019 National Health Service Long Term Plan. The Birmingham RAND and Cambridge Rapid Evaluation Centre team has undertaken a thematic synthesis of the 10 evaluations it has conducted from 2018 to 2023, exploring the needs, priorities and implications for people with multiple long-term conditions.

Objectives

The aims for this overarching study were to: (1) build a body of learning about service innovations in primary and community settings for people of all ages with multiple long-term conditions, focused on questions that matter most to people with multimorbidity; and (2) develop methodological insights about how rapid evaluation can be used to inform the scoping, testing and implementation of service innovations for people with multiple long-term conditions.

Design

The focus on multiple long-term conditions came from a Birmingham RAND and Cambridge Rapid Evaluation Centre prioritisation process undertaken in 2018 using James Lind Alliance methods. Cross-analysis of the findings from the 10 individual rapid evaluations was supplemented by (1) building aspects of multimorbidity into the design of later evaluations; (2) interviewing national and regional stakeholders (n=19) working in or alongside integrated care systems; (3) undertaking a rapid review of evidence on remote monitoring for people with multiple long-term conditions (19 papers included); and (4) testing overall insights with organisations representing patients and carers through a patient, public and professional engagement workshop with 10 participants plus members of the research team.

Results

While living with multiple long-term conditions is common and is the norm for people over the age of 50 using health and care services, it is not often a focus of health service provision or innovation, nor of research and evaluation activity. We discuss six themes emerging from the totality of the study: (1) our health system is mainly organised around single conditions and not multiple long-term conditions; (2) research calls and studies usually focus on single conditions and associated services; (3) building opportunities for engaged, informed individuals and carers and improved self-management; (4) the importance of measures that matter for patients and carers; (5) barriers to developing and implementing service innovations for people with multiple long-term conditions; and (6) what is needed to make patients with multiple long-term conditions a priority in healthcare planning and delivery.

Limitations

Care of people with multiple long-term conditions was not the principal focus of several of the rapid evaluations. While this was a finding in itself, it limited our learning about designing and implementing, as well as methodological approaches to evaluating, service innovations for people with multiple long-term conditions.

Conclusions

Through a thematic analysis of the portfolio of evaluations, we have deduced a set of suggested implications for how the needs of people with multiple long-term conditions can be better embedded in policy, research and practice.

Future work

Areas of uncertainty related to the care of people with multiple long-term conditions should be further explored, including developing and testing measures of patient experience of (un)co-ordinated care across settings, and interrogating the experience of health and care staff when working with people with multiple long-term conditions, to understand what works.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: NIHR134284) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 15. See the NIHR Funding and Awards website for further award information.

Depiction of conversational agents as health professionals: a scoping review

OBJECTIVE

The purpose of this scoping review was to examine the depiction of conversational agents as health professionals. We identified the professional characteristics that are used with these depictions and determined the prevalence of these characteristics among conversational agents that are used for health care.

INTRODUCTION

The depiction of conversational agents as health professionals has implications for both the users and the developers of these programs. For this reason, it is important to know more about these depictions and how they are implemented in practical settings.

INCLUSION CRITERIA

This review included scholarly literature on conversational agents that are used for health care. It focused on conversational agents designed for patients and health seekers, not health professionals or trainees. Conversational agents that address physical and/or mental health care were considered, as were programs that promote healthy behaviors.

METHODS

This review was conducted in accordance with JBI methodology for scoping reviews. The databases searched included MEDLINE (PubMed), Embase, CINAHL with Full Text (EBSCOhost), Scopus, Web of Science, ACM Guide to Computing Literature (Association for Computing Machinery Digital Library), and IEEE Xplore (IEEE). The main database search was conducted in June 2021, and an updated search was conducted in January 2022. Extracted data included characteristics of the report, basic characteristics of the conversational agent, and professional characteristics of the conversational agent. Extracted data were summarized using descriptive statistics. Results are presented in a narrative summary and accompanying tables.

RESULTS

A total of 38 health-related conversational agents were identified across 41 reports. Six of these conversational agents (15.8%) had professional characteristics. Four conversational agents (10.5%) had a professional appearance in which they displayed the clothing and accessories of health professionals and appeared in professional settings. One conversational agent (2.6%) had a professional title (Dr), and 4 conversational agents (10.5%) were described as having professional roles. Professional characteristics were more common among embodied vs disembodied conversational agents.

CONCLUSIONS

The results of this review show that the depiction of conversational agents as health professionals is not particularly common, although it does occur. More discussion is needed on the potential ethical and legal issues surrounding the depiction of conversational agents as health professionals. Future research should examine the impact of these depictions, as well as people's attitudes toward them, to better inform recommendations for practice.

Are e-Health Interventions Effective in Reducing Diabetes-Related Distress and Depression in Patients with Type 2 Diabetes? A Systematic Review with Meta-Analysis

Background: e-Health refers to any health care service delivered through the internet or related technologies, to improve quality of life. Despite the increasing use of e-health interventions to manage type 2 diabetes (T2D), there is a lack of evidence about the effectiveness on diabetes distress and depression, which are common issues in those living with T2D. Purpose: To synthesize and determine the effects of e-health interventions on diabetes distress and depression among patients with T2D. Methods: We systematically searched PubMed, Scopus, Cochrane CENTRAL, and Web of Science for randomized controlled trials (RCTs), non-RCTs and observational cohort studies for the effects of e-health interventions on diabetes distress and depression in patients with T2D up to September 14, 2022. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 recommendations were followed. The risk of bias was assessed according to the Risk-of-Bias 2 tool (RCTs), the Risk Of Bias In Non-randomised Studies-of Interventions (ROBINS-I) (non-RCTs) and the National Institute of Health tool (observational). The standardized mean difference (SMD) and its related 95% confidence intervals (CIs) were estimated with the DerSimonian-Laird method through random-effect models. A pooled raw mean difference (MD) meta-analysis was conducted for RCTs comparing the effects of e-health versus control on diabetes distress screening to display the clinical impact. Results: A total of 41 studies (24 RCTs, 14 non-RCTs, and 3 observational) involving 8,667 individuals were included. The pooled SMD for the effect of e-health versus the control group on diabetes distress was -0.14 (95% CI = -0.24 to -0.04; I2 = 23.9%; n = 10 studies), being -0.06 (95% CI = -0.15 to 0.02; I2 = 7.8%; n = 16 studies) for depression. The pooled raw MD on diabetes distress screening showed a reduction of -0.54 points (95% CI = -0.81 to -0.27; I2 = 85.1%; n = 7 studies). Conclusion: e-Health interventions are effective in diminishing diabetes distress among adults with T2D, inducing clinically meaningful reductions.

Exploring the characteristics of conversational agents in chronic disease management interventions: A scoping review

Objective

With the increasing global burden of chronic diseases, there is the potential for conversational agents (CAs) to assist people in actively managing their conditions. This paper reviews different types of CAs used for chronic condition management, delving into their characteristics and the chosen study designs. This paper also discusses the potential of these CAs to enhance the health and well-being of people with chronic conditions.

Methods

A search was performed in February 2023 on PubMed, ACM Digital Library, Scopus, and IEEE Xplore. Studies were included if they focused on chronic disease management or prevention and if systems were evaluated on target user groups.

Results

The 42 selected studies explored diverse types of CAs across 11 health conditions. Personalization varied, with 25 CAs not adapting message content, while others incorporated user characteristics and real-time context. Only 12 studies used medical records in conjunction with CAs for conditions like diabetes, mental health, cardiovascular issues, and cancer. Despite measurement method variations, the studies predominantly emphasized improved health outcomes and positive user attitudes toward CAs.

Conclusions

The results underscore the need for CAs to adapt to evolving patient needs, customize interventions, and incorporate human support and medical records for more effective care. It also highlights the potential of CAs to play a more active role in helping individuals manage their conditions and notes the value of linguistic data generated during user interactions. The analysis acknowledges its limitations and encourages further research into the use and potential of CAs in disease-specific contexts.

Impact of text reminders on pneumatic compression device (PCD) compliance in patients with breast cancer-related lymphedema

PURPOSE

Do cell phone text reminders impact the rate of compliance with pneumatic compression device (PCD) therapy among women with breast cancer-related lymphedema (BCRL)?

METHODS

A prospective, randomized, 2-group feasibility study conducted at 2 centers. Participants were adult females (≥18 years old) with unilateral BCRL who had the capability of receiving reminder text messages. All participants underwent PCD therapy. Participants were randomized 1:1 to control (no text messages) or test group (received text message reminders if the PCD had not been used for 2 consecutive days). The rate of compliance between treatment groups was the main outcome measure. Secondary outcome measures were changes in arm girth, quality of life (QOL), and symptom severity.

RESULTS

Twenty-nine participants were enrolled and randomized, 25 were available for follow-up at 60 days (14 test, 11 control). Overall, 52.2% (12/23) of all participants were completely compliant, an additional 43.5% (10/23) were partially compliant, and 1 patient (4.3%) was noncompliant. The test and control groups did not differ in device compliance. In the pooled population, weight, BMI, and arm girth were improved. Overall disease-specific QOL and symptom severity were improved. Regression analysis showed benefits were greater among participants with higher rates of compliance.

CONCLUSIONS

Automated text reminders did not improve compliance in patients with BCRL as compliance rates were already high in this patient population. Improvements in weight, BMI, arm girth, disease-specific quality of life, and symptom severity measures were observed regardless of the treatment assignment. Full compliance resulted in greater functional and QOL benefits.

TRIAL REGISTRATION

The study was registered at www.

CLINICALTRIALS

gov (NCT04432727) on June 16, 2020.

Decoding Applications of Artificial Intelligence in Rheumatology

Artificial intelligence (AI) is not a newcomer in medicine. It has been employed for image analysis, disease diagnosis, drug discovery, and improving overall patient care. ChatGPT (Chat Generative Pre-trained Transformer, Inc., Delaware) has renewed interest and enthusiasm in artificial intelligence. Algorithms, machine learning, deep learning, and data analysis are some of the complex terminologies often encountered when health professionals try to learn AI. In this article, we try to review the practical applications of artificial intelligence in vernacular language in the fields of medicine and rheumatology in particular. From the standpoint of the everyday physician, we have endeavored to encapsulate the influence of AI on the cutting edge of medical practice and the potential revolutionary shift in the realm of rheumatology.

When Virtual Assistants Meet Teledermatology: Validation of a Virtual Assistant to Improve the Quality of Life of Psoriatic Patients

Teledermatology has given dermatologists a tool to track patients' responses to therapy using images. Virtual assistants, the programs that interact with users through text or voice messages, could be used in teledermatology to enhance the interaction of the tool with the patients and healthcare professionals and the overall impact of the medication and quality of life of patients. As such, this work aimed to investigate the effectiveness of using a virtual assistant for teledermatology and its impact on the quality of life. We conducted surveys with the participants and measured the usability of the system with the System Usability Scale (SUS). A total of 34 participants (30 patients diagnosed with moderate-severe psoriasis and 4 healthcare professionals) were included in the study. The measurement of the improvement of quality of life was done by analyzing Psoriasis Quality of Life (PSOLIFE) and Dermatology Life Quality Index (DLQI) questionnaires. The results showed that, on average, the quality of life improved (from 63.8 to 64.8 for PSOLIFE (with a p-value of 0.66 and an effect size of 0.06) and 4.4 to 2.8 for DLQI (with a p-value of 0.04 and an effect size of 0.31)). Patients also used the virtual assistant to do 52 medical consultations. Moreover, the usability is above average, with a SUS score of 70.1. As supported by MMAS-8 results, adherence also improved slightly. Our work demonstrates the improvement of the quality of life with the use of a virtual assistant in teledermatology, which could be attributed to the sense of security or peace of mind the patients get as they can contact their dermatologists directly within the virtual assistant-integrated system.

Electronic health strategies to improve medication adherence in patients with cardiometabolic disease: current status and future directions

PURPOSE OF REVIEW

Enabled by widespread technological advancements, electronic health (eHealth) strategies have expanded rapidly over the last decade, presenting opportunities to support self-management including medication adherence for cardiometabolic disease control. eHealth can minimize access barriers to medications, enable timely assessment and shared decision-making, and provide medication reminders and health data feedback. This review summarizes current evidence for effectiveness of eHealth strategies for improving medication adherence in patients with hypertension, type 2 diabetes, and/or hyperlipidemia, and identifies priorities for future research.

RECENT FINDINGS

Current research supports the effectiveness of eHealth strategies to improve medication adherence and clinical outcomes for cardiometabolic disease. Although patient acceptability of eHealth strategies is generally high, engagement may decline over time. In addition, differences in effectiveness across intervention characteristics and sociodemographic groups are understudied, limiting generalizability and tailoring of interventions to local health system resources, culture, and patient needs or preferences.

SUMMARY

eHealth is a promising tool for addressing low medication adherence. Further work incorporating rigorous evaluation, assessment of patient engagement over time and effectiveness of intervention characteristics and components, and a health equity lens addressing eHealth use in vulnerable groups will increase understanding of the full potential of eHealth for improving medication adherence in diverse patients with cardiometabolic disease.