AI Applications to Reduce Loneliness Among Older Adults: A Systematic Review of Effectiveness and Technologies

Background/Objectives: Loneliness among older adults is a prevalent issue, significantly impacting their quality of life and increasing the risk of physical and mental health complications. The application of artificial intelligence (AI) technologies in behavioral interventions offers a promising avenue to overcome challenges in designing and implementing interventions to reduce loneliness by enabling personalized and scalable solutions. This study systematically reviews the AI-enabled interventions in addressing loneliness among older adults, focusing on the effectiveness and underlying technologies used. Methods: A systematic search was conducted across eight electronic databases, including PubMed and Web of Science, for studies published up to 31 January 2024. Inclusion criteria were experimental studies involving AI applications to mitigate loneliness among adults aged 55 and older. Data on participant demographics, intervention characteristics, AI methodologies, and effectiveness outcomes were extracted and synthesized. Results: Nine studies were included, comprising six randomized controlled trials and three pre–post designs. The most frequently implemented AI technologies included speech recognition (n = 6) and emotion recognition and simulation (n = 5). Intervention types varied, with six studies employing social robots, two utilizing personal voice assistants, and one using a digital human facilitator. Six studies reported significant reductions in loneliness, particularly those utilizing social robots, which demonstrated emotional engagement and personalized interactions. Three studies reported non-significant effects, often due to shorter intervention durations or limited interaction frequencies. Conclusions: AI-driven interventions show promise in reducing loneliness among older adults. Future research should focus on long-term, culturally competent solutions that integrate quantitative and qualitative findings to optimize intervention design and scalability.