Scott Saponas

Ruican Zhong, Jiachen Li, Gary Hsieh et al. · uw

Electronic health records (EHRs) have improved data accessibility but have also introduced cognitive burden for physicians, given the sheer volume and complexity of the data involved. Advances in large language models (LLMs) create new opportunities to rethink how clinicians interact with medical data through dynamic, adaptive interfaces. In this position paper, we explore how generative AI can support physicians' information needs by enabling more dynamic interactions with patient data. Through semi-structured interviews with internal physicians at Microsoft, we identify key challenges in data navigation and synthesis, and characterize clinicians' information needs during diagnostic workflows. We further examine how physicians conceptualize AI can help their work process and how these mental models shape expectations for interaction and trust. Based on these insights, we discuss design considerations for generative user interfaces that support clinician-centered workflows.

Nikita Mehandru, Amanda K. Hall, Olesya Melnichenko et al.

Creating end-to-end bioinformatics workflows requires diverse domain expertise, which poses challenges for both junior and senior researchers as it demands a deep understanding of both genomics concepts and computational techniques. While large language models (LLMs) provide some assistance, they often fall short in providing the nuanced guidance needed to execute complex bioinformatics tasks, and require expensive computing resources to achieve high performance. We thus propose a multi-agent system built on small language models, fine-tuned on bioinformatics data, and enhanced with retrieval augmented generation (RAG). Our system, BioAgents, enables local operation and personalization using proprietary data. We observe performance comparable to human experts on conceptual genomics tasks, and suggest next steps to enhance code generation capabilities.

Angela Mastrianni, Hope Twede, Aleksandra Sarcevic et al.

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. CCS CONCEPTS: Human-centered computing, Human-computer interaction, Empirical studies in HCI Additional Keywords and Phrases: whole genome sequencing, generative AI, large language models, knowledge work, sensemaking, co-design, rare disease Contact Author: Angela Mastrianni (This work was done during the author's internship at Microsoft Research) Ashley Mae Conard and Amanda K. Hall contributed equally

Suchismita Naik, Austin L. Toombs, Amanda Snellinger et al.

With recent advancements in multi-agent generative AI (Gen AI), technology organizations like Microsoft are adopting these complex tools, redefining AI agents as active collaborators in complex workflows rather than as passive tools. In this study, we investigated how early adopters and developers conceptualize multi-agent Gen AI tools, focusing on how they understand human-AI collaboration mechanisms, general collaboration dynamics, and transparency in the context of AI tools. We conducted semi-structured interviews with 13 developers, all early adopters of multi-agent Gen AI technology who work at Microsoft. Our findings revealed that these early adopters conceptualize multi-agent systems as "teams" of specialized role-based and task-based agents, such as assistants or reviewers, structured similar to human collaboration models and ranging from AI-dominant to AI-assisted, user-controlled interactions. We identified key challenges, including error propagation, unpredictable and unproductive agent loop behavior, and the need for clear communication to mitigate the layered transparency issues. Early adopters' perspectives about the role of transparency underscored its importance as a way to build trust, verify and trace errors, and prevent misuse, errors, and leaks. The insights and design considerations we present contribute to CSCW research about collaborative mechanisms with capabilities ranging from AI-dominant to AI-assisted interactions, transparency and oversight strategies in human-agent and agent-agent interactions, and how humans make sense of these multi-agent systems as dynamic, role-diverse collaborators which are customizable for diverse needs and workflows. We conclude with future research directions that extend CSCW approaches to the design of inter-agent and human mediation interactions.