AI and disability: A systematic scoping review.

Background: Artificial intelligence (AI) can enhance life experiences and present challenges for people with disabilities. Objectives: This study aims to investigate the relationship between AI and disability, exploring the potential benefits and challenges of using AI for people with disabilities. Methods: A systematic scoping review was conducted using eight online databases; 45 scholarly articles from the last 5 years were identified and selected for thematic analysis. Results: The review's findings revealed AI's potential to enhance healthcare; however, it showed a high prevalence of a narrow medical model of disability and an ableist perspective in AI research. This raises concerns about the perpetuation of biases and discrimination against individuals with disabilities in the development and deployment of AI technologies. Conclusion: We recommend shifting towards a social model of disability, promoting interdisciplinary collaboration, addressing AI bias and discrimination, prioritizing privacy and security in AI development, focusing on accessibility and usability, investing in education and training, and advocating for robust policy and regulatory frameworks. The review emphasizes the urgent need for further research to ensure that AI benefits all members of society equitably and that future AI systems are designed with inclusivity and accessibility as core principles.

AI-based epidemic and pandemic early warning systems: A systematic scoping review.

Background: Timely detection of disease outbreaks is critical in public health. Artificial Intelligence (AI) can identify patterns in data that signal the onset of epidemics and pandemics. This scoping review examines the effectiveness of AI in epidemic and pandemic early warning systems (EWS). Objective: To assess the capability of AI-based systems in predicting epidemics and pandemics and to identify challenges and strategies for improvement. Methods: A systematic scoping review was conducted. The review included studies from the last 5 years, focusing on AI and machine learning applications in EWS. After screening 1087 articles, 33 were selected for thematic analysis. Results: The review found that AI-based EWS have been effectively implemented in various contexts, using a range of algorithms. Key challenges identified include data quality, model explainability, bias, data volume, velocity, variety, availability, and granularity. Strategies for mitigating AI bias and improving system adaptability were also discussed. Conclusion: AI has shown promise in enhancing the speed and accuracy of epidemic detection. However, challenges related to data quality, bias, and model transparency need to be addressed to improve the reliability and generalizability of AI-based EWS. Continuous monitoring and improvement, as well as incorporating social and environmental data, are essential for future development.

Exploring the Intersection of AI and Inclusive Design for People with Disabilities.

This scoping review examines current research on AI for inclusive design for people with disabilities. We identified both advantages and challenges of AI-based solutions and suggested future research directions. Our search of four online databases for studies from the last five years revealed promising AI applications in education, daily living, home environments, workplaces, and healthcare. However, limitations include limited research, lack of user involvement, potential data bias, and reporting deficiencies. We stress the importance of future research prioritizing user-centered design, inclusive participation, AI bias mitigation, consideration of diverse populations, and ensuring user-friendly performance to fully realize AI's potential for accessibility and inclusion.

Identifying the panorama of potential pandemic pathogens and their key characteristics: a systematic scoping review.

The globe has recently seen several terrifying pandemics and outbreaks, underlining the ongoing danger presented by infectious microorganisms. This literature review aims to explore the wide range of infections that have the potential to lead to pandemics in the present and the future and pave the way to the conception of epidemic early warning systems. A systematic review was carried out to identify and compile data on infectious agents known to cause pandemics and those that pose future concerns. One hundred and fifteen articles were included in the review. They provided insights on 25 pathogens that could start or contribute to creating pandemic situations. Diagnostic procedures, clinical symptoms, and infection transmission routes were analyzed for each of these pathogens. Each infectious agent's potential is discussed, shedding light on the crucial aspects that render them potential threats to the future. This literature review provides insights for policymakers, healthcare professionals, and researchers in their quest to identify potential pandemic pathogens, and in their efforts to enhance pandemic preparedness through building early warning systems for continuous epidemiological monitoring.

Predicting the Effectiveness of a Mindfulness Virtual Community Intervention for University Students: Machine Learning Model.

BACKGROUND: Students' mental health crisis was recognized before the COVID-19 pandemic. Mindfulness virtual community (MVC), an 8-week web-based mindfulness and cognitive behavioral therapy program, has proven to be an effective web-based program to reduce symptoms of depression, anxiety, and stress. Predicting the success of MVC before a student enrolls in the program is essential to advise students accordingly. OBJECTIVE: The objectives of this study were to investigate (1) whether we can predict MVC's effectiveness using sociodemographic and self-reported features and (2) whether exposure to mindfulness videos is highly predictive of the intervention's success. METHODS: Machine learning models were developed to predict MVC's effectiveness, defined as success in reducing symptoms of depression, anxiety, and stress as measured using the Patient Health Questionnaire-9 (PHQ-9), the Beck Anxiety Inventory (BAI), and the Perceived Stress Scale (PSS), to at least the minimal clinically important difference. A data set representing a sample of undergraduate students (N=209) who took the MVC intervention between fall 2017 and fall 2018 was used for this secondary analysis. Random forest was used to measure the features' importance. RESULTS: Gradient boosting achieved the best performance both in terms of area under the curve (AUC) and accuracy for predicting PHQ-9 (AUC=0.85 and accuracy=0.83) and PSS (AUC=1 and accuracy=1), and random forest had the best performance for predicting BAI (AUC=0.93 and accuracy=0.93). Exposure to online mindfulness videos was the most important predictor for the intervention's effectiveness for PHQ-9, BAI, and PSS, followed by the number of working hours per week. CONCLUSIONS: The performance of the models to predict MVC intervention effectiveness for depression, anxiety, and stress is high. These models might be helpful for professionals to advise students early enough on taking the intervention or choosing other alternatives. The students' exposure to online mindfulness videos is the most important predictor for the effectiveness of the MVC intervention. TRIAL REGISTRATION: ISRCTN Registry ISRCTN12249616; https://www.isrctn.com/ISRCTN12249616.

A cross-sectional study of university students' mental health and lifestyle practices amidst the COVID-19 pandemic.

OBJECTIVES: University students are regarded as the backbone of society, and their mental health during a pandemic may have a substantial impact on their performance and life outcomes. The purpose of this study was to assess university students' mental health, specifically depression, anxiety, and stress, during Lebanon's extended COVID-19 pandemic, as well as the sociodemographic factors and lifestyle practices associated with it. METHODS: An online anonymous survey assessed the rates of mental health problems during COVID-19, controlling for socio-demographics and other lifestyle practices, in 329 undergraduate and graduate university students. Instruments utilized were the Patient Health Questionnaire (PHQ-9) for depression, the Beck Anxiety Inventory (21-BAI) for anxiety, and the Perceived Stress Scale (PSS-10) for stress. The study employed descriptive statistics and multiple logistic regression models to analyze the association between depression, anxiety, and stress with sociodemographic and lifestyle factors. Results were evaluated using adjusted odds ratios and confidence intervals, with a significance level of 0.05. RESULTS: Moderate to severe rates of depression, anxiety and stress among students were reported by 75.9%, 72.2%, and 89.3%, respectively. The odds of anxiety and stress were higher among women compared to men. Students who used private counseling services had higher odds of anxiety and stress than those who did not. Overall rated health was a major predictor of depression and anxiety, with the "poor" and "fair" overall-reported health groups having higher odds than the "Excellent" group. When compared to those who did not smoke, students who increased their smoking intake had higher odds of depression, anxiety and stress. Students who reduced their alcohol consumption had lower odds of anxiety compared to those who did not consume alcohol. Students who reduced their physical activity had higher odds than those who increased it. Finally, students who slept fewer than seven hours daily had higher odds of depression than those who slept seven to nine hours. CONCLUSION: Our findings indicate a national student mental health crisis, with exceptionally high rates of moderate to severe depression, anxiety, and stress. Factors such as gender, university program, overall rated health, importance of religion in daily decisions, private counseling, smoking cigarettes, alcohol consumption, physical activity, and sleeping, were all found to have an impact on mental health outcomes. Our study highlights the need for university administrators and mental health professionals to consider targeted mental health programming for students, particularly for women and those with poor or fair overall perceived health.

Predictive Models for Canadian Healthcare Workers Mental Health During COVID-19.

PURPOSE: COVID-19 impact on the population's mental health has been reported worldwide. Predicting healthcare workers' mental health and life stress is needed to proactively plan for future emergencies. DESIGN: Statistics Canada has surveyed Canadian healthcare workers and those working in healthcare settings to gauge their perceived mental health and perceived life stress. SETTING: A cross-sectional survey of healthcare workers in Canada. SUBJECTS: A sample of 18,139 healthcare workers respondents. ANALYSIS: Eight algorithms, including Logistic Regression, Random Forest (RF), Naive Bayes (NB), K Nearest Neighbours (KNN), Adaptive boost (AdaBoost), Multi-layer perceptron (MLP), XGBoost, and LightBoost. AUC scores, accuracy and precision were measured for all models. RESULTS: XGBoost provided the highest performing model AUC score (AUC = 82.07%) for predicting perceived mental health, and Random Forest performed the best for predicting perceived life stress (AUC = 77.74%). Perceived health, age group of participants, and perceived mental health compared to before the pandemic were found to be the most important 3 features to predict perceived mental health and perceived stress. Perceived mental health compared to before the pandemic was the most important predictor for perceived life stress. CONCLUSION: Our models are highly predictive of healthcare workers' perceived mental health and life stress. Implementing scalable, non-expensive virtual mental health solutions to address mental health challenges in the workplace could mitigate the impact of workplace conditions on healthcare workers' mental health.

Predictive Machine Learning Models for Assessing Lebanese University Students' Depression, Anxiety, and Stress During COVID-19.

University students are experiencing a mental health crisis. COVID-19 has exacerbated this situation. We have surveyed students in 2 universities in Lebanon to gauge their mental health challenges. We have constructed a machine learning (ML) approach to predict symptoms of depression, anxiety, and stress based on demographics and self-rated health measures. Our approach involved developing 8 ML predictive models, including Logistic Regression (LR), multi-layer perceptron (MLP) neural network, support vector machine (SVM), random forest (RF) and XGBoost, AdaBoost, Naïve Bayes (NB), and K-Nearest neighbors (KNN). Following their construction, we compared their respective performances. Our evaluation shows that RF (AUC = 78.27%), NB (AUC = 76.37%), and AdaBoost (AUC = 72.96%) have provided the highest-performing AUC scores for depression, anxiety, and stress, respectively. Self-rated health is found to be the top feature in predicting depression, while age was the top feature in predicting anxiety and stress, followed by self-rated health. Future work will focus on using data augmentation approaches and extending to multi-class anxiety predictions.

Coping with the COVID-19 pandemic: A cross-sectional study to investigate how mental health, lifestyle, and socio-demographic factors shape students' quality of life.

PURPOSE: The high prevalence of COVID-19 has had an impact on the Quality of Life (QOL) of people across the world, particularly students. The purpose of this study was to investigate the social, lifestyle, and mental health aspects that are associated with QOL among university students in Lebanon. METHODS: A cross-sectional study design was implemented using a convenience sampling approach. Data collection took place between November 2021 and February 2022, involving 329 undergraduate and graduate students from private and public universities. Quality of life was assessed using the Quality-of-Life Scale (QOLS). Descriptive statistics, Cronbach's alpha, and linear regression-based methods were used to analyze the association between QOL and socio-demographic, health-related, lifestyle, and mental health factors. The significance level for statistical analysis was predetermined at α = 0.05. RESULTS: The study participants' average (SD) QOL score was 76.03 (15.6) with a Cronbach alpha of 0.911. QOL was positively associated with importance of religion in daily decisions (ß = 6.40, p = 0.006), household income (ß = 5.25, p = 0.017), general health ratings (ß Excellent/poor = 23.52, p <0.001), access to private counseling (ß = 4.05, p = 0.020), physical exercise (ß = 6.67, p <0.001), and a healthy diet (ß = 4.62, p = 0.026); and negatively associated with cigarette smoking (ß increased = -6.25, p = 0.030), internet use (ß ≥4 hours = -7.01, p = 0.005), depression (ß = -0.56, p = 0.002) and stress (ß = -0.93, p <0.001). CONCLUSION: In conclusion, this study reveals the key factors that positively and negatively influence students' quality of life (QOL). Factors such as religion, higher income, and a healthy diet improve QOL, while depression, stress, excessive internet use, and cigarette smoking negatively impact it. Universities should prioritize initiatives like physical activity promotion, affordable nutritious options, destigmatizing mental health, counseling services, and self-help interventions to support student well-being and enhance their QOL.

A Machine Learning Study to Predict Anxiety on Campuses in Lebanon.

University students are experiencing a mental health crisis across the world. COVID-19 has exacerbated this situation. We have conducted a survey among university students in two universities in Lebanon to gauge mental health challenges experienced by students. We constructed a machine learning approach to predict anxiety symptoms among the sample of 329 respondents based on student survey items including demographics and self-rated health. Five algorithms including logistic regression, multi-layer perceptron (MLP) neural network, support vector machine (SVM), random forest (RF) and XGBoost were used to predict anxiety. Multi-Layer Perceptron (MLP) provided the highest performing model AUC score (AUC=80.70%) and self-rated health was found to be the top ranked feature to predict anxiety. Future work will focus on using data augmentation approaches and extending to multi-class anxiety predictions. Multidisciplinary research is crucial in this emerging field.

Equity within AI systems: What can health leaders expect?

Artificial Intelligence (AI) for health has a great potential; it has already proven to be successful in enhancing patient outcomes, facilitating professional work and benefiting administration. However, AI presents challenges related to health equity defined as an opportunity for people to reach their fullest health potential. This article discusses the opportunities and challenges that AI presents in health and examines ways in which inequities related to AI can be mitigated.

Need of Equity in Virtual Mental Health in Canada in the Times of COVID-19.

We have conducted a systematic review on the use of virtual care for mental health purposes in Canada during COVID-19. Our review shows that existing infrastructures in Canada need to be adapted for eMental Health services to be offered proactively to the population. Equity is key for successful implementation.

A Machine Learning Approach to Predict the Outcome of Urinary Calculi Treatment Using Shock Wave Lithotripsy: Model Development and Validation Study.

BACKGROUND: Shock wave lithotripsy (SWL), ureteroscopy, and percutaneous nephrolithotomy are established treatments for renal stones. Historically, SWL has been a predominant and commonly used procedure for treating upper tract renal stones smaller than 20 mm in diameter due to its noninvasive nature. However, the reported failure rate of SWL after one treatment session ranges from 30% to 89%. The failure rate can be reduced by identifying candidates likely to benefit from SWL and manage patients who are likely to fail SWL with other treatment modalities. This would enhance and optimize treatment results for SWL candidates. OBJECTIVE: We proposed to develop a machine learning model that can predict SWL outcomes to assist practitioners in the decision-making process when considering patients for stone treatment. METHODS: A data set including 58,349 SWL procedures performed during 31,569 patient visits for SWL to a single hospital between 1990 and 2016 was used to construct and validate the predictive model. The AdaBoost algorithm was applied to a data set with 17 predictive attributes related to patient demographics and stone characteristics, with success or failure as an outcome. The AdaBoost algorithm was also applied to a training data set. The generated model's performance was compared to that of 5 other machine learning algorithms, namely C4.5 decision tree, naïve Bayes, Bayesian network, K-nearest neighbors, and multilayer perceptron. RESULTS: The developed model was validated with a testing data set and performed significantly better than the models generated by the other 5 predictive algorithms. The sensitivity and specificity of the model were 0.875 and 0.653, respectively, while its positive predictive value was 0.7159 and negative predictive value was 0.839. The C-statistics of the receiver operating characteristic (ROC) analysis was 0.843, which reflects an excellent test. CONCLUSIONS:  We have developed a rigorous machine learning model to assist physicians and decision-makers to choose patients with renal stones who are most likely to have successful SWL treatment based on their demographics and stone characteristics. The proposed machine learning model can assist physicians and decision-makers in planning for SWL treatment and allow for more effective use of limited health care resources and improve patient prognoses.

Fear of COVID-19 and depression, anxiety, stress, and PTSD among Syrian refugee parents in Canada.

INTRODUCTION: The high frequency of COVID-19 has had an impact on the psychological health of all countries and socioeconomic groups around the world, with refugees suffering the brunt of the burden. The aim was to assess the relationship between fear of COVID-19 and depression, anxiety, stress, and PTSD among Syrian refugee parents residing in the Greater Toronto Area. METHODS: A convenience sample of 274 Syrian refugee parents residing in Ontario was recruited. Fear of COVID-19 was measured using the Fear of COVID-19 Scale (FCV-19S). Levels of depression, anxiety, stress and PTSD were assessed using the Depression Anxiety Stress Scales (DASS-21), and Primary Care PTSD screen (PC-PTSD). Multiple Linear Regression analyses were performed to assess the relationship between FCV-19S and the DASS-21 subscales and PC-PTSD. RESULTS: Severe levels of depression, anxiety, and stress were reported by 12.2%, 26.8%, and 9.7% of participants respectively, and 24.1% screened positive for PTSD. FCV-19S was associated with higher levels of stress (ß = 0.27, p = 0.006), anxiety (ß = 0.40, p <0.001), depression (ß = 0.32, p = 0.001) and PTSD (ß = 0.04, p = 0.015). DISCUSSION: Government initiatives should consider tackling fear concerning pandemics among Syrian refugee parents to help enhance their mental well-being.

Effectiveness of online mindfulness interventions on medical students' mental health: a systematic review.

BACKGROUND: Medical school typically presents students with a combination of academic and personal stressors that may lead to substandard mental health wellbeing. Meditation practices such as mindfulness facilitate a greater awareness of one's thoughts and feelings, thereby decreasing emotional reactivity. The use of mindfulness-based interventions delivered online has considerable potential in fostering self-care and helping medical students to handle mental health challenges. We examined the available evidence on the use of online mindfulness interventions in order to determine whether they are feasible and effective for improving medical students' mental health. METHODS: We performed a systematic review guided by PRISMA guidelines and utilised the following databases: ProQuest, Medline, PubMed, PsycINFO, Web of Science, IEEE Explore, Cochrane, and CINAHL. The key search terms used include mindfulness, cognitive behavioural therapy, acceptance and commitment therapy, online, web, virtual, internet cyber, app, medical students, residency students, and residents. English-language articles published in the last ten years that described online interventions for medical students or residents were included in the review. RESULTS: Two studies describing the impact of online mindfulness interventions on medical students' mental health were identified. Research in this domain is nascent; available qualitative and quantitative evidence suggests benefits in self-compassion, perceived stress, cognitive skill use, mindfulness, creating coping mechanisms, and greater awareness of emotions and feelings. There was no evidence of the effectiveness of online mindfulness interventions on depression, anxiety and burnout. There was, however, general low program usage and participation tended to diminish near the conclusion of the interventions. CONCLUSIONS: The evidence found in the systematic review exhibits the potential for online mindfulness interventions to be effective in addressing some mental health challenges of medical students. There was insufficient evidence to support the use of online mindfulness interventions for burnout, depression, and anxiety. Longitudinal studies with randomised controlled trials are required to generate stronger and robust evidence.

A Virtual Community for Disability Advocacy: Development of a Searchable Artificial Intelligence-Supported Platform.

BACKGROUND: The lack of availability of disability data has been identified as a major challenge hindering continuous disability equity monitoring. It is important to develop a platform that enables searching for disability data to expose systemic discrimination and social exclusion, which increase vulnerability to inequitable social conditions. OBJECTIVE: Our project aims to create an accessible and multilingual pilot disability website that structures and integrates data about people with disabilities and provides data for national and international disability advocacy communities. The platform will be endowed with a document upload function with hybrid (automated and manual) paragraph tagging, while the querying function will involve an intelligent natural language search in the supported languages. METHODS: We have designed and implemented a virtual community platform using Wikibase, Semantic Web, machine learning, and web programming tools to enable disability communities to upload and search for disability documents. The platform data model is based on an ontology we have designed following the United Nations Convention on the Rights of Persons with Disabilities (CRPD). The virtual community facilitates the uploading and sharing of validated information, and supports disability rights advocacy by enabling dissemination of knowledge. RESULTS: Using health informatics and artificial intelligence techniques (namely Semantic Web, machine learning, and natural language processing techniques), we were able to develop a pilot virtual community that supports disability rights advocacy by facilitating uploading, sharing, and accessing disability data. The system consists of a website on top of a Wikibase (a Semantic Web-based datastore). The virtual community accepts 4 types of users: information producers, information consumers, validators, and administrators. The virtual community enables the uploading of documents, semiautomatic tagging of their paragraphs with meaningful keywords, and validation of the process before uploading the data to the disability Wikibase. Once uploaded, public users (information consumers) can perform a semantic search using an intelligent and multilingual search engine (QAnswer). Further enhancements of the platform are planned. CONCLUSIONS: The platform ontology is flexible and can accommodate advocacy reports and disability policy and legislation from specific jurisdictions, which can be accessed in relation to the CRPD articles. The platform ontology can be expanded to fit international contexts. The virtual community supports information upload and search. Semiautomatic tagging and intelligent multilingual semantic search using natural language are enabled using artificial intelligence techniques, namely Semantic Web, machine learning, and natural language processing.

The Potential of an Artificial Intelligence for Disability Advocacy: The WikiDisability Project.

Human rights monitoring for people with disabilities is in urgent need for disability data that is shared and available for local and international disability stakeholders (e.g., advocacy groups). Our aim is to use a Wikibase for editing, integrating, storing structured disability related data and to develop a Natural Language Processing (NLP) enabled multilingual search engine to tap into the wikibase data. In this paper, we explain the project first phase.

Correction: A Mindfulness-Based Intervention for Student Depression, Anxiety, and Stress: Randomized Controlled Trial.

[This corrects the article DOI: 10.2196/23491.].

A Mindfulness-Based Intervention for Student Depression, Anxiety, and Stress: Randomized Controlled Trial.

BACKGROUND: University students are experiencing higher levels of distress and mental health disorders than before. In addressing mental health needs, web-based interventions have shown increasing promise in overcoming geographic distances and high student-to-counselor ratios, leading to the potential for wider implementation. The Mindfulness Virtual Community (MVC) program, a web-based program, guided by mindfulness and cognitive behavioral therapy principles, is among efforts aimed at effectively and efficiently reducing symptoms of depression, anxiety, and perceived stress in students. OBJECTIVE: This study's aim was to evaluate the efficacy of an 8-week MVC program in reducing depression, anxiety, and perceived stress (primary outcomes), and improving mindfulness (secondary outcome) in undergraduate students at a large Canadian university. Guided by two prior randomized controlled trials (RCTs) that each demonstrated efficacy when conducted during regular university operations, this study coincided with a university-wide labor strike. Nonetheless, the students' response to an online mental health program on a disrupted campus can provide useful information for anticipating the impact of other disruptions, including those related to the COVID-19 pandemic as well as future disruptions. METHODS: In this parallel-arm RCT, 154 students were randomly allocated to an 8-week MVC intervention (n=76) or a wait-list control (WLC) condition (n=78). The MVC intervention included the following: (1) educational and mindfulness video modules, (2) anonymous peer-to-peer discussions, and (3) anonymous, group-based, professionally guided, 20-minute videoconferences. Study outcomes were evaluated at baseline and at 8-week follow-up using the following: Patient Health Questionnaire-9 (PHQ-9), the Beck Anxiety Inventory (BAI), the Perceived Stress Scale (PSS), and the Five Facets Mindfulness Questionnaire Short Form (FFMQ-SF). Generalized estimation equations with an AR (1) covariance structure were used to evaluate the impact of the intervention, with outcome evaluations performed on both an intention-to-treat (ITT) and per-protocol (PP) basis. RESULTS: Participants (n=154) included 35 males and 117 females with a mean age of 23.1 years. There were no statistically significant differences at baseline between the MVC and WLC groups on demographics and psychological characteristics, indicating similar demographic and psychological characteristics across the two groups. Results under both ITT and PP approaches indicated that there were no statistically significant between-group differences in PHQ-9 (ITT: ß=-0.44, P=.64; PP: ß=-0.62, P=.053), BAI (ITT: ß=-2.06, P=.31; PP: ß=-2.32, P=.27), and FFMQ-SF (ITT: ß=1.33, P=.43; PP: ß=1.44, P=.41) compared to WLC. There was a significant difference for the PSS (ITT: ß=-2.31, P=.03; PP: ß=-2.38, P=.03). CONCLUSIONS: During a university labor strike, the MVC program led to statistically significant reductions in PSS compared to the WLC group, but there were no other significant between-group differences. Comparisons with previous cycles of intervention testing, undertaken during nondisrupted university operations, when efficacy was demonstrated, are discussed. TRIAL REGISTRATION: ISRCTN Registry ISRCTN92827275; https://www.isrctn.com/ISRCTN92827275.

Effectiveness of ICT-based intimate partner violence interventions: a systematic review.

BACKGROUND: Intimate Partner Violence is a "global pandemic". Meanwhile, information and communication technologies (ICT), such as the internet, mobile phones, and smartphones, are spreading worldwide, including in low- and middle-income countries. We reviewed the available evidence on the use of ICT-based interventions to address intimate partner violence (IPV), evaluating the effectiveness, acceptability, and suitability of ICT for addressing different aspects of the problem (e.g., awareness, screening, prevention, treatment, mental health). METHODS: We conducted a systematic review, following PRISMA guidelines, using the following databases: PubMed, PsycINFO, and Web of Science. Key search terms included women, violence, domestic violence, intimate partner violence, information, communication technology, ICT, technology, email, mobile, phone, digital, ehealth, web, computer, online, and computerized. Only articles written in English were included. RESULTS: Twenty-five studies addressing screening and disclosure, IPV prevention, ICT suitability, support and women's mental health were identified. The evidence reviewed suggests that ICT-based interventions were effective mainly in screening, disclosure, and prevention. However, there is a lack of homogeneity among the studies' outcome measurements and the sample sizes, the control groups used (if any), the type of interventions, and the study recruitment space. Questions addressing safety, equity, and the unintended consequences of the use of ICT in IPV programming are virtually non-existent. CONCLUSIONS: There is a clear need to develop women-centered ICT design when programming for IPV. Our study showed only one study that formally addressed software usability. The need for more research to address safety, equity, and the unintended consequences of the use of ICT in IPV programming is paramount. Studies addressing long term effects are also needed.