[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83505-en":3,"doc-seo-83505-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83505,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","AI-Centered Grand Challenges in Visual Analytics for Healthcare Synthesizing the VAHC 2025 Community Experience","AI, healthcare, and visualization are rapidly converging, creating cross-disciplinary challenges that are difficult to resolve with single-discipline solutions. The VAHC workshop, held biennially at IEEE VIS and AMIA conferences, was used in 2025 to consolidate community experience and define priority Grand Challenges. The study combines thematic coding of 15 accepted VAHC papers with structured discussions across 40+ participants, revealing recurring AI concerns. Five challenge clusters are distilled, each paired with cross-disciplinary research recommendations: trust and bias, data and infrastructure, explainability and communication, human-AI interaction, and model reliability and validation.","arXiv :2607 .00542v 1 [ cs .HC] 1 Jul 2026  \nAI-Centered Grand Challenges in Visual Analytics for Healthcare: Synthesizing the VAHC 2025 Community Experience  \nJürgen Bernard  , David Gotz  , Robert S Laramee  , Silvia Miksch  , Gabriela Morgenshtern  , Renata G. Raidou , and Alessio Arleo   \nAbstract—The intersection of AI, healthcare, and visualization is evolving rapidly, posing challenges that cut across disciplinary boundaries and resist easy resolution. The Visual Analytics in Healthcare workshop (VAHC), co-located every other year at the IEEE VIS conference and the AMIA (American Medical Informatics Association) annual conference, has served as a forum to connect the visualization and medical informatics community since 2010 . In 2025, to celebrate the 16th edition, we used the workshop as an opportunity to consolidate the community’s collective experience (and expertise) and identify Grand Challenges where the field should prioritize going forward. We combined thematic coding of the 15 accepted VAHC workshop papers with structured group discussions among more than 40 participants, organized around three major themes: “Technical innovation vs. clinical reality\",“Human-centered and scalable VAHC\", and “From foundations to actionable insights\", followed by post-workshop reflexive analysis. Across all three groups, AI emerged as the most consistently recurring concern. In this paper, we report our AI-centered insights from the VAHC 2025 group activity, contextualize them against the broader literature along five Grand Challenges themes, and distill them into five challenge clusters, each concluded with recommendations for future research directions that cross disciplinary boundaries: (1) trust and bias,(2) data and infrastructure, (3) explainability and communication, (4) human-AI interaction, and (5) model reliability and validation.  \nWe share these challenges and their associated research directions as a starting point for discussion and collaboration across the healthcare, AI, and visualization communities. All supplemental materials are available at [https://osf.io/p79uj](https://osf.io/p79uj).  \nIndex Terms—Visual Analytics, Visualization for Healthcare, Challenges and Opportunities, AI in Healthcare, Human-AI Interaction  \n1 INTRODUCTION  \nThe rapid integration of AI into healthcare is reshaping how clinicians, patients, and researchers interact with health data. The visualization community is uniquely well-positioned to support this transition, yet faces the challenge of identifying where to focus its efforts. The introduction of these technologies requires that researchers address unprecedented obstacles: those of scalability and data quality, resulting from the explosion in consumer-collected data, of those same consumers’sudden, unprecedented access to health data about themselves, of clinicians’ skepticism towards AI recommendations in clinical decisionmaking and treatment planning, and of a lack of patient literacy, which complicates physician-patient relationships.  \nThis is not the first time that the visualization community has challenged healthcare research. In 2015, Caban and Gotz [13] recognized visualization and visual analytics (VA) as disciplines capable of realizing the potential behind the adoption of Electronic Health Records (EHR) on a large scale, rejecting paper records as the standard for recording and sharing clinical data. This “call to action\" was written to provoke a reaction in both the visualization and medical informatics communities, pushing the two towards deeper collaboration on research, towards more open, accessible, and personalized care. The Visual Analytics in Healthcare (VAHC) workshop [1] is a forum where the visualization and medical informatics communities can gather to discuss research intersecting visual data analysis and clinical informatics. Topics of interest to the workshop include applications of VA to clinical care, public health data analysis, AI for healthcare data ","cbCaisGX31uUuoFF","https://ap.wps.com/l/cbCaisGX31uUuoFF","pdf",262718,1,5,"English","en",105,"# Introduction\n## VAHC 2025 Grand Challenges Approach\n## Five AI-Centered Challenge Clusters","[{\"question\":\"What prompted VAHC 2025 to focus on Grand Challenges in visual analytics for healthcare?\",\"answer\":\"VAHC 2025 aimed to consolidate the community’s collective experience and expertise to identify Grand Challenges the field should prioritize going forward, celebrating the workshop’s 16th edition.\"},{\"question\":\"How were the Grand Challenges derived from VAHC 2025 activities?\",\"answer\":\"The authors used thematic coding of 15 accepted VAHC workshop papers and structured group discussions with 40+ participants across three major themes, followed by post-workshop reflexive analysis.\"},{\"question\":\"Which AI-centered research areas were consolidated into the five challenge clusters?\",\"answer\":\"The paper distills five clusters: (1) trust and bias, (2) data and infrastructure, (3) explainability and communication, (4) human-AI interaction, and (5) model reliability and 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