An Interdisciplinary Approach to Long-Term Care: Artificial Intelligence Applications for Monitoring Nutritional Status and Social Well-Being

AI-Driven Integrated Caregiving and Health Monitoring Framework for Elderly Well-Being

The rapid growth of the aging population has brought about some serious challenges, particularly in managing illnesses, feelings of loneliness, cognitive decline, and mental health issues. Traditional caregiving methods often depend on occasional assessments and hands-on supervision, which can fall short in providing the ongoing and adaptable support that’s really needed. This paper introduces an innovative caregiving and monitoring framework powered by AI, aimed at offering integrated, real-time, and comprehensive assistance for older adults. The system harnesses the power of Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and health data analytics to combine physical health monitoring, nutrition planning, smart routine coaching, and therapist-led mental health support all in one platform. With features like voice-based conversations and journaling, it makes emotional expression and behavioral analysis more accessible, helping to gain a deeper insight into users’ mental well-being. Predictive analytics and anomaly detection are used to spot early signs of health risks and shifts in behavior, allowing for timely interventions. Plus, remote access means caregivers and healthcare professionals can keep an eye on users and offer informed advice. By shifting caregiving from a reactive approach to a proactive and preventive one, this system not only improves quality of life but also encourages independent living and eases the burden on caregivers.

Artificial Intelligence in Clinical Nutrition: Current Uses, Challenges, and Opportunities

Overall, AI offers substantial opportunities to improve the personalization, efficiency, and scalability of clinical nutrition practice, but its safe and effective implementation will require continued validation, careful oversight, and integration with clinical expertise.

The Role of Artificial Intelligence in Addressing Menopausal Mental Health Challenges.

Menopause marks a crucial transition in a woman's life and is often accompanied by physical and psychological changes that can adversely affect mental health. Depression, anxiety, cognitive changes, and sleep disturbances are common during the menopausal transition, yet they are frequently underdiagnosed and undertreated, particularly in lowand middle-income countries. Emerging technologies, especially artificial intelligence (AI), offer new opportunities to narrow this care gap. Although AI has shown considerable promise in identifying menopause-related physical health conditions (e.g., osteoporosis and endometrial cancer), its use for mental health during this life stage remains limited. We discuss the potential of AI-driven tools-including machine learning algorithms, digital therapeutics, symptom trackers, and large language models-to improve the detection, monitoring, and personalized management of menopause-associated mental health disorders. By integrating genetic, clinical, lifestyle, and wearable data, AI systems may help predict risk, identify symptom patterns, and support tailored interventions. These approaches could enable scalable, accessible, and cost-effective mental healthcare, reduce stigma, and address service gaps. Harnessing AI in this area offers a significant opportunity to improve quality of life for millions of women worldwide.

Innovative Technologies in Social

Artificial Intelligence-Assisted Nutritional Counseling for Weight Management in Adult Obesity: A Systematic Review of Randomized Controlled Trials and Primary Studies

AI-assisted nutritional counseling demonstrates robust, clinically meaningful efficacy across multiple outcome domains, and Precision nutrition algorithms integrating postprandial glycemic response prediction and gut microbiome profiling achieved the most comprehensive cardiometabolic optimization.

Exploration of Remote Nutrition Intervention for Metabolic Diseases Supported by Multimodal Dietary Data

The prevalence rate of metabolic syndrome, type 2 diabetes and dyslipidemia in adults in China continues to rise, and the affected population is becoming younger. Nutritional intervention is a key measure for the control of metabolic chronic diseases. Traditional offline guidance is constrained by time and space, and dietary data relies on subjective recollection, which has shortcomings such as large errors and insufficient personalization. This article aims to explore new intervention pathways that are suitable for long-term management of chronic diseases. This study integrates multi-dimensional data on diet, physiology, metabolism, and behavior, relying on image recognition, wearable devices, and cloud technology, to build a remote nutrition intervention system that integrates data collection, intelligent analysis, individualized intervention plans, and online follow-up and guidance. Combining authoritative monitoring data and scientific research achievements, the architecture and application path are sorted out. The results confirm that multimodal data can overcome the inherent limits of traditional nutritional interventions and effectively improve the level of dietary management and the effectiveness of chronic disease control. This model is suitable for the development needs of primary healthcare and can provide theoretical and practical support for digital remote nutrition management of metabolic diseases.