Recent advances in foundation models (FMs) have enabled artificial intelligence systems to acquire generalizable capabilities, with language and visual models demonstrating adaptability across diverse healthcare applications and multimodal vision-language FMs (VLFMs) supporting complex tasks such as report generation and question answering. Building on these developments, autonomous agents, particularly vision-language agents (VLAs), represent the next frontier, extending AI from perception and recognition to cognition, decision-making, and action. By integrating multimodal understanding with planning, interaction, and tool use, VLAs introduce autonomous intelligence capable of managing multi-step clinical workflows, adapting over time, and collaborating with clinicians. This survey provides a comprehensive perspective on VLAs in healthcare. We examine their current progress, analyze the major challenges hindering their integration, and highlight promising directions for future development. Unlike prior surveys that primarily focus on diverse FMs, our work prioritizes emerging VLFMs and incorporates agentic concepts, emphasizing their transformative potential beyond narrow research prototypes. Specifically, we (ⅰ) evaluate VLAs in terms of VLFM architectures and adaptation strategies and agentic design, (ⅱ) assess their potential impact across diverse clinical workflows, and (ⅲ) discuss pathways towards next-generation clinical decision support, considering technical and clinical challenges.

Population aging presents a growing societal challenge and imposes a heavy burden on the healthcare system in many Asian countries. Given the limited availability of formal long‐term care (LTC) facilities and personnel, family caregivers play a vital role in providing care for the increasing population of older adults. While awareness of the challenges faced by caregivers is rising, discussions often remain within academic circles, resulting in the lived experiences, well‐being, and needs of family caregivers being frequently overlooked. In this review, we identify four key priority areas to advance research, practice, and policy related to family caregivers in Asia: (1) Emphasizing family caregivers as sociocultural navigators in the healthcare system; (2) addressing the mental and physical health needs of family caregivers; (3) recognizing the diverse caregiving experiences across different cultural backgrounds, socioeconomic status, and countries of residence; and (4) strengthening policy support for family caregivers. Our review also identifies deficiencies in institutional LTC and underscores the importance of providing training and empowerment to caregivers. Policymakers, practitioners, and researchers interested in supporting family caregivers should prioritize these key areas to tackle the challenge of population aging in Asian countries. Cross‐country knowledge exchange and capacity development are crucial for better serving both the aging population and their caregivers.