Artificial intelligence (AI) is increasingly integrated into scientific discovery processes, such as protein design, gene analysis, and materials research, significantly enhancing the efficiency of discoveries in these fields. While much recent literature emphasizes fully automated pipelines, it is crucial to acknowledge that scientific discovery is inherently a creative and high-stakes endeavor. Therefore, it relies heavily on human expertise for judgment and guidance, especially in the face of uncertainty. Despite rapid growth in human-in-the-loop and collaborative systems, the field lacks a unifying survey that explains how humans and AI actually collaborate across the scientific discovery life-cycle. In this paper, we present a systematic review of human–AI (HAI) collaboration for scientific discovery. Specifically, we have identified four representative roles of humans and AI. Using this lens, we then distill common HAI collaboration patterns across three distinct stages in the scientific discovery process (i.e., observation, hypothesis, and experiment). Finally, we identify key gaps in existing approaches and outline future research directions for developing trustworthy, role-aware human–AI systems in scientific discovery.

In healthcare, AI techniques are widely used for tasks like risk assessment and anomaly detection. Despite AI's potential as a valuable assistant, its role in complex medical data analysis often oversimplifies human-AI collaboration dynamics. To address this, we collaborated with a local hospital, engaging six physicians and one data scientist in a formative study. From this collaboration, we propose a framework integrating two-phase interactive visualization systems: one for Human-Led, AI-Assisted Retrospective Analysis and another for AI-Mediated, Human-Reviewed Iterative Modeling. This framework aims to enhance understanding and discussion around effective human-AI collaboration in healthcare.

Online mental health communities (OMHCs) offer rich posts and comments for viewers, who do not directly participate in the communications, to seek social support from others' experience. However, viewers could face challenges in finding helpful posts and comments and digesting the content to get needed support, as revealed in our formative study (N=10). In this work, we present an interactive visual tool named ComViewer to help viewers seek social support in OMHCs. With ComViewer, viewers can filter posts of different topics and find supportive comments via a zoomable circle packing visual component that adapts to searched keywords. Powered by LLM, ComViewer supports an interactive sensemaking process by enabling viewers to interactively highlight, summarize, and question any community content. A within-subjects study (N=20) demonstrates ComViewer's strengths in providing viewers with a more simplified, more fruitful, and more engaging support-seeking experience compared to a baseline OMHC interface without ComViewer. We further discuss design implications for facilitating information-seeking and sense making in online mental health communities.