Applications of generative artificial intelligence in long-term care services: A systematic review of the evidence on efficacy for improving resident health related quality of life.

BACKGROUND: Generative artificial intelligence (GAI) has rapidly expanded into health and social care, offering new opportunities for communication, clinical support, and personalized interventions. Long-term care (LTC) represents a critical application area for improved health related quality of life (HRQoL) due to workforce shortages, increasing care complexity, and the need for scalable, person-centered solutions. A systematic assessment of current evidence is needed to inform safe, effective, and ethical implementation. METHODS: A mixed-methods systematic review was conducted using searches of major academic databases and literature sources. Twenty-three studies met inclusion criteria. Data was analyzed using narrative synthesis, to evaluate the current evidence base on the use, benefits, risks, and implementation considerations of GAI for older adults HRQoL in LTC services. RESULTS: GAI applications included conversational agents, virtual companions, decision-support systems, documentation tools, and training platforms. Reported benefits included enhanced emotional engagement, cognitive stimulation, improved communication, and reduced administrative burden for caregivers. However, studies were early stage, with small samples, short implementation periods, and limited evaluation of clinical outcomes of fall risk prevention, medical adherence, hydration, and nutrition for improved HRQoL. Ethical and practical concerns were prominent, including data privacy, transparency, algorithmic bias, and potential erosion of human interaction. CONCLUSIONS: GAI shows promise as an emerging technology that may enhance quality, efficiency, and personalization in LTC. However, substantial evidence gaps remain regarding effectiveness, cost-effectiveness, safety, and organizational impact. Robust evaluation frameworks and participatory design processes are essential to guide responsible adoption and ensure that GAI complements the human-centered foundation of LTC.