Smartphone Apps for Domestic Violence Prevention: A Systematic Review.

Smartphone applications or apps are increasingly being produced to help with protection against the risk of domestic violence. There is a need to formally evaluate their features. OBJECTIVE: This study systematically reviewed app-based interventions for domestic violence prevention, which will be helpful for app developers. METHODS: We overviewed all apps concerning domestic violence awareness and prevention without language restrictions, collating information about features and limitations. We conducted searches in Google, the Google Play Store, and the App Store (iOS) covering a 10-year time period (2012-2022). We collected data related to the apps from the developers' descriptions, peer reviewed research articles, critical reviews in blogs, news articles, and other online sources. RESULTS: The search identified 621 potentially relevant apps of which 136 were selected for review. There were five app categories: emergency assistance (n = 61, 44.9%), avoidance (n = 29, 21.3%), informative (n = 29, 21.3%), legal information (n = 10, 7.4%), and self-assessment (n = 7, 5.1%). Over half the apps (n = 97, 71%) were released in 2020-22. Around a half were from north-east America (n = 63, 46.3%). Where emergency alerts existed, they required triggering by the potential victim. There was no automation. Content analysis showed 20 apps with unique features, including geo-fences, accelerometer-based alert, shake-based alert, functionality under low resources, alert auto-cancellation, anonymous communication, and data encryption. None of the apps deployed artificial intelligence to assist the potential victims. CONCLUSIONS: Apps currently have many limitations. Future apps should focus on automation, making better use of artificial intelligence deploying multimedia (voice, video, image capture, text and sentiment analysis), speech recognition, and pitch detection to aid in live analysis of the situation and for accurately generating emergency alerts.

Artificial intelligence in COVID-19 evidence syntheses was underutilized, but impactful: a methodological study.

OBJECTIVES: A rapidly developing scenario like a pandemic requires the prompt production of high-quality systematic reviews, which can be automated using artificial intelligence (AI) techniques. We evaluated the application of AI tools in COVID-19 evidence syntheses. STUDY DESIGN: After prospective registration of the review protocol, we automated the download of all open-access COVID-19 systematic reviews in the COVID-19 Living Overview of Evidence database, indexed them for AI-related keywords, and located those that used AI tools. We compared their journals' JCR Impact Factor, citations per month, screening workloads, completion times (from pre-registration to preprint or submission to a journal) and AMSTAR-2 methodology assessments (maximum score 13 points) with a set of publication date matched control reviews without AI. RESULTS: Of the 3,999 COVID-19 reviews, 28 (0.7%, 95% CI 0.47-1.03%) made use of AI. On average, compared to controls (n = 64), AI reviews were published in journals with higher Impact Factors (median 8.9 vs. 3.5, P < 0.001), and screened more abstracts per author (302.2 vs. 140.3, P = 0.009) and per included study (189.0 vs. 365.8, P < 0.001) while inspecting less full texts per author (5.3 vs. 14.0, P = 0.005). No differences were found in citation counts (0.5 vs. 0.6, P = 0.600), inspected full texts per included study (3.8 vs. 3.4, P = 0.481), completion times (74.0 vs. 123.0, P = 0.205) or AMSTAR-2 (7.5 vs. 6.3, P = 0.119). CONCLUSION: AI was an underutilized tool in COVID-19 systematic reviews. Its usage, compared to reviews without AI, was associated with more efficient screening of literature and higher publication impact. There is scope for the application of AI in automating systematic reviews.

Artificial intelligence and automation of systematic reviews in women's health.

PURPOSE OF REVIEW: Evidence-based women's healthcare is underpinned by systematic reviews and guidelines. Generating an evidence synthesis to support guidance for clinical practice is a time-consuming and labour-intensive activity that delays transfer of research into practice. Artificial intelligence has the potential to rapidly collate, combine, and update high-quality medical evidence with accuracy and precision, and without bias. RECENT FINDINGS: This article describes the main fields of artificial intelligence with examples of its application to systematic reviews. These include the capabilities of processing natural language texts, retrieving information, reasoning, and learning. The complementarity and interconnection of the various artificial intelligence techniques can be harnessed to solve difficult problems in automation of reviews. Computer science can advance evidence-based medicine through development, testing, and refinement of artificial intelligence tools to deploy automation, creating 'living' evidence syntheses. SUMMARY: Groundbreaking, high-quality, and impactful artificial intelligence will accelerate the transfer of individual research studies seamlessly into evidence syntheses for contemporaneously improving the quality of healthcare.

Temperatus® software: A new tool to efficiently manage the massive information generated by iButtons.

An iButton is a temperature sensor of small dimensions (button-sized; 16 × 6 mm2), relatively low cost (˜US50$), with a stable and autonomous system that measures temperature and records the data in a protected memory section. These devices are used in different fields and the company offers a software (One-Wire Viewer) with several limitations. The present study describes Temperatus® software with the main aim of making the task of programming, downloading, and analysing the massive amount of data generated by iButtons smoothly, intuitive, time-efficient, and user-friendly.

A comparison of learning algorithms for Bayesian networks: a case study based on data from an emergency medical service.

Due to the uncertainty of many of the factors that influence the performance of an emergency medical service, we propose using Bayesian networks to model this kind of system. We use different algorithms for learning Bayesian networks in order to build several models, from the hospital manager's point of view, and apply them to the specific case of the emergency service of a Spanish hospital. This first study of a real problem includes preliminary data processing, the experiments carried out, the comparison of the algorithms from different perspectives, and some potential uses of Bayesian networks for management problems in the health service.