Generative Artificial Intelligence in Health Research and Care: A Narrative Review of Applications and Implications for Alzheimer’s Disease and Related Dementia

The advent of generative artificial intelligence (AI), particularly large language models (LLMs) and deep generative networks, is transforming the landscape of Alzheimer’s disease and related dementia (ADRD) research and care. This narrative review aims to synthesize current applications of generative AI in ADRD research and care, highlight ethical and implementation challenges, and propose strategies for responsible integration into clinical practice. We conducted a narrative review of literature, regulatory documents, program reports, and relevant sources published between 2016 and 2026, with a specific focus on cognitive screening, synthetic data generation, digital twin modeling, and caregiver support. Techniques discussed include generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, multimodal transformers, and LLM-powered chatbots. Generative AI enables applications such as virtual patient companions, remote cognitive assessments via conversational agents, multimodal image-to-text tools, synthetic data augmentation, disease progression simulation, and improved caregiver communication. Challenges include model explainability, clinical validation, regulatory compliance, privacy, data bias, hallucination risk, patient autonomy, and global disparities. Responsible development requires transparent reporting, stakeholder co-design, rigorous clinical and economic validation, and continuous post-deployment monitoring to support fairness and effectiveness in real-world settings.