The emergence of large language models (LLMs) is transforming various health-related domains, including approaches to obesity management. Obesity remains one of the world’s leading health issues, prompting the research community to develop various weight-loss applications focused on physical activity, dietary planning, and related interventions. In this study, we explore the capability of the LLM ChatGPT for personalized dietary planning. We conducted two case studies: Case Study 1 examined self-supervised recipe generation using ChatGPT alone, while Case Study 2 investigated a self-supervised approach combining National Institute of Health standards with ChatGPT recipe recommendations. We also performed a user study to evaluate recipe recommendations from ChatGPT. Our results show that ChatGPT recommends appropriate recipes based on comparisons with the United States Department of Agriculture’s (USDA) recipe calculator. We found no significant difference between ChatGPT-generated recipe recommendation calories and USDA standards for either Case Study 1 (p = 0.8530) or Case Study 2 (p = 0.0687). In addition, we found significant weight loss in participants following these recipes in both Case Study 1 (p < 0.00001) and Case Study 2 (p = 0.0014). Furthermore, the user study with potential weight-loss participants revealed varying levels of satisfaction (p = 0.001) and identified themes related to meal preferences, effective prompt generation, and mixed concerns regarding privacy, trust, user consent, and data storage. We conclude by discussing additional findings from our case and user studies, and present opportunities, challenges, and design and ethical considerations for the research community.

Modern visualization software and programming libraries have made data visualization construction easier for everyone. However, the extent of accessibility design they support for blind and low‐vision people is relatively unknown. It is also unclear how they can improve chart content accessibility beyond conventional alternative text and data tables. To address these issues, we examined the current accessibility features in popular visualization tools, revealing limited support for the standard accessibility methods and scarce support for chart content exploration. Next, we investigate two promising accessibility approaches that provide off‐the‐shelf solutions for chart content accessibility: structured navigation and conversational interaction. We present a comparative evaluation study and discuss what to consider when incorporating them into visualization tools.

Background/Objectives: The aging population faces a multitude of health challenges, particularly when it comes to maintaining proper nutrition. Age-related physiological changes, such as decreased metabolism, diminished taste perception, and difficulty in chewing, can lead to insufficient nutrient intake, ultimately resulting in malnutrition. It is crucial to address these issues to promote not only physical health but also overall well-being. In this modern era, artificial intelligence (AI) technologies, including robots and machine learning algorithms, are being increasingly harnessed to encourage healthy eating habits among older adults. This is critical to support healthy aging and mitigate diet-related chronic diseases. However, little or no synthesis has established their effectiveness in delivering personalized, scalable, and adaptive interventions for older adults. This systematic review considers the state-of-the-art application of AI-based interventions aimed at improving dietary behaviors and nutritional outcomes in older adults. Methods: Following the PRISMA 2020 guidelines and a registered PROSPERO protocol (ID: CRD420241045268), we systematically analyzed 30 studies we collected from five databases, published between 2015 and 2025 based on different AI techniques, including machine learning, natural language processing, and recommender systems. We synthesized data collected from these studies to examine the intervention types, outcomes, and methodological approaches. Results: Findings from our review highlight the potential of AI-based interventions to promote engagement among older adults and improve adherence to healthy eating guidelines. Additionally, we found some challenges related to ethical concerns such as privacy and transparency, and limited evidence of their long-term effectiveness. Conclusions: AI-based interventions offer significant promise in promoting healthy eating among older adults through personalized, adaptive, and scalable interventions. Yet, current evidence is constrained by some methodological limitations and ethical concerns, which calls for future research to design inclusive, evidence-based AI interventions that address the unique physiological, psychological, and social needs of older adults.