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Background: Chatbots are increasingly utilized in the healthcare landscape, including in sexual and reproductive health (SRH). These tools have shown significant potential in enhancing accessibility to health education and services, particularly with the addition of artificial intelligence. This commentary explores the utilization of chatbots for delivering SRH care, assessing their potential to impact health equity and access within rural and underserved settings and evaluating their effectiveness and safety.
Methods: Synthesizing existing literature on SRH chatbots, this commentary examines their development and functionality, assessing their impact on health equity, particularly across diverse demographic and socio-cultural contexts. The effectiveness and safety of these tools are also reviewed based on their design, content validation processes, and adherence to privacy regulations.
Results: SRH chatbots have the potential to enhance health literacy and equity, increasing user knowledge and autonomy. Through equitable development and implementation, they can democratize access to essential health information across diverse populations, providing tailored advice based on individual factors. This personalized guidance empowers users to make informed decisions about their health. However, their effectiveness can be influenced by factors such as technological literacy, ethics, and privacy concerns.
Conclusions: Chatbots in SRH present a promising tool for improving healthcare delivery and empower patients by providing immediate, personalized information. They increase access to evidenced based information while allowing healthcare workers to allocate more resources to complex cases. As AI becomes increasingly available, those using it for SRH should focus on enhancing technological capabilities, ensuring rigorous content validation, and overcoming technological and socio-economic barriers to maximize the public health benefits, including misinformation correction, of chatbots in SRH. The development of these tools must prioritize equity and user trust by ensuring equitable access, data privacy, and tailoring interactions to meet diverse needs across populations.
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http://dx.doi.org/10.1016/j.contraception.2025.111199 | DOI Listing |
Thromb Res
September 2025
Center for Thrombosis and Hemostasis, University Medical Center of the Johannes Gutenberg University, Mainz, Germany. Electronic address:
Warfarin is a widely used vitamin K antagonist (VKA) with known pleiotropic effects beyond anticoagulation. Preclinical and case-control evidence suggests that warfarin may affect hematopoiesis, but longitudinal human evidence is lacking. To explore this potential effect, we conducted a post-hoc analysis of participants in the Hokusai-VTE and ENGAGE AF-TIMI 48 trials, which randomized patients to warfarin or the direct oral anticoagulant edoxaban with routine laboratory testing at predefined follow-up visits.
View Article and Find Full Text PDFPublic Health
September 2025
Department of Public Health, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
Objectives: Participation rates in fecal immunochemical test (FIT)-based colorectal cancer (CRC) screening differ across socio-demographic subgroups. The largest health gains could be achieved in subgroups with low participation rates and high risk of CRC. We investigated the CRC risk within different socio-demographic subgroups with low participation in the Dutch CRC screening program.
View Article and Find Full Text PDFDriven by eutrophication and global warming, the occurrence and frequency of harmful cyanobacteria blooms (CyanoHABs) are increasing worldwide, posing a serious threat to human health and biodiversity. Early warning enables precautional control measures of CyanoHABs within water bodies and in water works, and it becomes operational with high frequency in situ data (HFISD) of water quality and forecasting models by machine learning (ML). However, the acceptance of early warning systems by end-users relies significantly on the interpretability and generalizability of underlying models, and their operability.
View Article and Find Full Text PDFAm J Emerg Med
September 2025
University of Toronto, Rotman School of Management, Canada.
Study Objective: Accurately predicting which Emergency Department (ED) patients are at high risk of leaving without being seen (LWBS) could enable targeted interventions aimed at reducing LWBS rates. Machine Learning (ML) models that dynamically update these risk predictions as patients experience more time waiting were developed and validated, in order to improve the prediction accuracy and correctly identify more patients who LWBS.
Methods: The study was deemed quality improvement by the institutional review board, and collected all patient visits to the ED of a large academic medical campus over 24 months.