Multidisciplinary tumor boards (MTBs) bring specialists together to identify therapies for complex cancer cases, but preparing for them is time-intensive. Clinicians must extract key details from extensive records and evaluate treatment options. While large language models (LLMs) show promise in medicine for basic tasks like summarizing notes, little is known about their role in high-stakes tasks like MTB preparation. We conducted a mixed-methods study with 16 oncologists using two AI systems to prepare patient cases for MTB: an off-the-shelf assistant (Copilot) and a task-specific multi-agent system (Healthcare Agent Orchestrator, HAO). We analyzed oncologist prompts, AI responses, and oncologists’ perception of AI. Participants showed greater willingness to adopt HAO but were often overconfident in AI summaries and skeptical of AI-recommended therapies. Trust calibration strategies, such as source links and agent-trajectories, failed to align trust with system capabilities. We conclude with how AI systems should be built to support clinicians in high-stakes tasks.

In this paper we present the results of our investigation into how employees at Microsoft, as early adopters of multi-agent generative AI systems, navigate the complexities of designing, testing, and deploying these technologies to extend the organization's product ecosystem.Through interviews with thirteen developers, we uncover the challenges, use cases, and lessons when designing with and for multi-agent AI frameworks.Our analysis reveals how participants leveraged this advanced emerging technology to enhance collaboration, productivity, customer support, creative processes, and security.Key design strategies include managing agent complexity, fostering transparency, and balancing agent autonomy with human oversight, essential considerations for human-agent interaction design.We provide empirical insights into the capabilities and limitations of multi-agent systems in real-world contexts, informing the design of future AI systems that align AI capabilities with human-centered design.By emphasizing first-person experiences and strategies, our research bridges human needs and AI potentials, advancing both the practice and theory of designing with and for AI systems.

Generative AI has the potential to transform knowledge work, but further research is needed to understand how knowledge workers envision using and interacting with generative AI. We investigate the development of generative AI tools to support domain experts in knowledge work, examining task delegation and the design of human–AI interactions. Our research focused on designing a generative AI assistant to aid genetic professionals in analyzing whole genome sequences (WGS) and other clinical data for rare disease diagnosis. Through interviews with 17 genetics professionals, we identified current challenges in WGS analysis. We then conducted co-design sessions with six genetics professionals to determine tasks that could be supported by an AI assistant and considerations for designing interactions with the AI assistant. From our findings, we identified sensemaking as both a current challenge in WGS analysis and a process that could be supported by AI. We contribute an understanding of how domain experts envision interacting with generative AI in their knowledge work, a detailed empirical study of WGS analysis, and three design considerations for using generative AI to support domain experts in sensemaking during knowledge work.

Computer-Supported Cooperative Work (CSCW) and Human--Computer Interaction (HCI) research is increasingly investigating the roles of caregivers as ancillary stakeholders in patient-centered care. Our research extends this body of work to identify caregivers as key decision-makers and boundary actors in mobilizing and managing care. We draw on qualitative data collected via 20 semi-structured interviews to examine caregiving responsibilities in physical and remote care interactions within households in urban India. Our findings demonstrate the crucial intermediating roles family caregivers take on while situated along the boundaries separating healthcare professionals, patients and other household members, and online/offline communities. We propose design recommendations for supporting caregivers in intermediating patient-centered care, such as through training content and expert feedback mechanisms for remote care, collaborative tracking mechanisms integrating patient- and caregiver-generated health data, and caregiving-centered online health communities. We conclude by arguing for recognizing caregivers as critical stakeholders in patient-centered care who might constitute technologically assisted pathways to care.