Large Language Models (LLMs) have been positioned as having the potential to expand access to health information in the Global South, yet their evaluation remains heavily dependent on benchmarks designed around Western norms. We present insights from a preliminary benchmarking exercise with a chatbot for sexual and reproductive health (SRH) for an underserved community in India. We evaluated using HealthBench, a benchmark for conversational health models by OpenAI. We extracted 637 SRH queries from the dataset and evaluated on the 330 single-turn conversations. Responses were evaluated using HealthBench's rubric-based automated grader, which rated responses consistently low. However, qualitative analysis by trained annotators and public health experts revealed that many responses were actually culturally appropriate and medically accurate. We highlight recurring issues, particularly a Western bias, such as for legal framing and norms (e.g., breastfeeding in public), diet assumptions (e.g., fish safe to eat during pregnancy), and costs (e.g., insurance models). Our findings demonstrate the limitations of current benchmarks in capturing the effectiveness of systems built for different cultural and healthcare contexts. We argue for the development of culturally adaptive evaluation frameworks that meet quality standards while recognizing needs of diverse populations.

Low-resource countries represent over 90% of maternal deaths, with Pakistan among the top four countries contributing nearly half in 2023. Since these deaths are mostly preventable, large language models (LLMs) can help address this crisis by automating health communication and risk assessment. However, sexual and reproductive health (SRH) communication in conservative contexts often relies on indirect language that obscures meaning, complicating LLM-based interventions. We conduct a two-stage study in Pakistan: (1) analyzing data from clinical observations, interviews, and focus groups with clinicians and patients, and (2) evaluating the interpretive capabilities of five popular LLMs on this data. Our analysis identifies two axes of communication (referential domain and expression approach) and shows LLMs struggle with semantic drift, myths, and polysemy in clinical interactions. We contribute: (1) empirical themes in SRH communication, (2) a categorization framework for indirect communication, (3) evaluation of LLM performance, and (4) design recommendations for culturally-situated SRH communication.

While Artificial Intelligence (AI) shows promise in healthcare applications, existing conversational systems often falter in complex and sensitive medical domains such as Sexual and Reproductive Health (SRH). These systems frequently struggle with hallucination and lack the specialized knowledge required, particularly for sensitive SRH topics. Furthermore, current AI approaches in healthcare tend to prioritize diagnostic capabilities over comprehensive patient care and education. Addressing these gaps, this work at the UNC School of Nursing introduces SARHAchat, a proof-of-concept Large Language Model (LLM)- based chatbot. SARHAchat is designed as a reliable, user-centered system integrating medical expertise with empathetic communication to enhance SRH care delivery. Our evaluation demonstrates SARHAchat's ability to provide accurate and contextually appropriate contraceptive counseling while maintaining a natural conversational flow. The demo is available at https://sarhachat.com/.

Comprehensive sex education (SE) is crucial in promoting sexual health and responsible behavior among students, particularly in elementary schools. Despite its significance, teaching SE can be challenging due to students' attitudes, shyness, and emotional barriers. Socially assistive robots (SARs) sometimes are perceived as more trustworthy than humans, based on research showing that they are not anticipated as judgmental. Inspired by those evidences, this study aims to assess the success of a SAR as a facilitator for SE lessons for elementary school students. This study conducted two experiments to assess the effectiveness of a SAR in facilitating SE education for elementary school students. We conducted two experiments, a) a group activity in the school classroom where the Nao robot gave a SE lecture, and we evaluated how much information the students acquired from the lecture, and b) an individual activity where the students interacted 1:1 with the robot, and we evaluated their attitudes towards the subject of SE, and if they felt comfortable to ask SE related questions to the robot. Data collected from pre- and post-questionnaires, as well as video annotations, revealed that the SAR significantly improved students' attitudes toward SE. Furthermore, students were more open to asking SE-related questions to the robot than their human teacher. The study emphasized specific SAR characteristics, such as embodiment and non-judgmental behavior, as key factors contributing to their effectiveness in supporting SE education, paving the way for innovative and effective approaches to sexual education in schools.

How to Do It is Slate's sex advice column. Send it to Stoya and Rich here. My husband and I have an awesome, intelligent 14-year-old son who identifies as bisexual. We are totally accepting and supportive of him. He has had a few short-lived crushes on different genders, though he doesn't seem to be particularly interested in dating right now. His internet search histories are pretty benign--mostly video game stuff, and the occasional search for "hot girls" and "boobs."

We introduce SexTok, a multi-modal dataset composed of TikTok videos labeled as sexually suggestive (from the annotator's point of view), sex-educational content, or neither. Such a dataset is necessary to address the challenge of distinguishing between sexually suggestive content and virtual sex education videos on TikTok. Children's exposure to sexually suggestive videos has been shown to have adversarial effects on their development. Meanwhile, virtual sex education, especially on subjects that are more relevant to the LGBTQIA+ community, is very valuable. The platform's current system removes or penalizes some of both types of videos, even though they serve different purposes. Our dataset contains video URLs, and it is also audio transcribed. To validate its importance, we explore two transformer-based models for classifying the videos. Our preliminary results suggest that the task of distinguishing between these types of videos is learnable but challenging. These experiments suggest that this dataset is meaningful and invites further study on the subject.

A proper forecast of the menstrual cycle is meaningful for women's health, as it allows individuals to take preventive actions to minimize cycle-associated discomforts. In addition, precise prediction can be useful for planning important events in a woman's life, such as family planning. In this work, we explored the use of machine learning techniques to predict regular and irregular menstrual cycles. We implemented some time series forecasting algorithm approaches, such as AutoRegressive Integrated Moving Average, Huber Regression, Lasso Regression, Orthogonal Matching Pursuit, and Long Short-Term Memory Network. Moreover, we generated synthetic data to achieve our purposes. The results showed that it is possible to accurately predict the onset and duration of menstrual cycles using machine learning techniques.

An innovative chatbot designed for sharing critical information about sexual and reproductive health (SRH) with young people in India is demonstrating how artificial intelligence (AI) applications can engage vulnerable and hard-to-reach population segments. Working with the Population Foundation of India (PFI), Helen Wang, associate professor of communication, College of Arts and Sciences, examined the user-centered design and engagement of SnehAI, the first Hinglish (Hindi and English) chatbot purposefully developed for social and behavioral change. "Many AI technologies today are motivated by profit, but we must also be aware that AI can be leveraged in ways that facilitate social and behavior change," says Wang, who specializes in entertainment-education and storytelling as instruments for health promotion. "SnehAI is a powerful testimonial of the vital potential that lies in AI for good." The findings from Wang's instrumental case study appear in the Journal of Medical Internet Research.

In this paper, we have discussed initial findings and results of our experiment to predict sexual and reproductive health vulnerabilities of migrants in a data-constrained environment. Notwithstanding the limited research and data about migrants and migration cities, we propose a solution that simultaneously focuses on data gathering from migrants, augmenting awareness of the migrants to reduce mishaps, and setting up a mechanism to present insights to the key stakeholders in migration to act upon. We have designed a webapp for the stakeholders involved in migration: migrants, who would participate in data gathering process and can also use the app for getting to know safety and awareness tips based on analysis of the data received; public health workers, who would have an access to the database of migrants on the app; policy makers, who would have a greater understanding of the ground reality, and of the patterns of migration through machine-learned analysis. Finally, we have experimented with different machine learning models on an artificially curated dataset. We have shown, through experiments, how machine learning can assist in predicting the migrants at risk and can also help in identifying the critical factors that make migration dangerous for migrants. The results for identifying vulnerable migrants through machine learning algorithms are statistically significant at an alpha of 0.05.

Chatting with Roo is free and private, so go ahead and ask the things you don't want to ask out loud. Expert answers, every time Roo's answers are backed by professional health educators from Planned Parenthood, the most trusted provider of sexual education. Roo is built around the questions teens are actually asking. That thing you've always wondered about? Roo gets a little bit smarter every time you ask a question.