9th ARIAL Workshop will be held in conjunction with the 35th International Joint Conference on AI, 2026.
New: MAISON-LLF Data Challenge
The global aging population continues to grow rapidly, bringing significant challenges to healthcare, rehabilitation, and assisted living systems. Older adults often experience progressive physical, cognitive, and mental health changes that affect mobility, independence, and quality of life, increasing vulnerability to injury, disability, and chronic conditions. Supporting safe, active, and independent aging in clinical care, community settings and long-term care facilities requires scalable, continuous, and personalized approaches that go beyond traditional models of care.
Artificial intelligence, digital health and assistive technologies offer promising solutions by enabling continuous monitoring, rehabilitation, and early detection of health risks using multimodal data sources, such as wearable sensors, ambient devices, video, audio, and clinical records. However, translating these data into actionable insights remains challenging. Aging-related data are often sparse, noisy, imbalanced, and highly personalized, with rare but safety-critical events such as falls, agitation, or rapid health decline. Moreover, real-world deployment raises additional concerns related to model robustness, interpretability, privacy, fairness, and ethical use of sensitive health data, particularly for vulnerable populations.
In line with IJCAI guidelines, we welcome submissions exploring the use of machine learning, deep learning, large language models, agentic AI, and foundation models in aging and rehabilitation research, such as clinical text analysis, multimodal data analysis, decision support, and personalized care. Authors are responsible for ensuring the accuracy, originality, and transparency of their work and must clearly document the role of such models within their methodology.
Previous ARIAL Workshops