Workshop

Data Quality Aware, High-Performance, and Trustworthy AI Systems for Healthcare

In conjuction with IEEE/ACM CHASE 2026, Pittsburgh, PA, USA.

Overview:

The adoption of machine learning (ML) and, more specifically, deep learning (DL) applications into all major areas of our lives is underway. The development of trustworthy AI is especially important in healthcare due to the large implications for patients’ lives. In comparison, trustworthiness concerns various aspects, including ethics, transparency, and safety requirements. One of the most critical parts of an AI is the quality of its input data since it has a fundamental impact on the resulting system. It lays the foundation and inherently provides limitations for the AI application. However, both AI researchers and practitioners overwhelmingly concentrate on models/algorithms while undervaluing data quality. This workshop aims to unite leaders, practitioners, and researchers to explore and discuss novel solutions, the latest techniques, best practices, and future directions for developing high-performance and trustworthy AI systems for healthcare from a data quality perspective. We invite theoretical research, original research, case studies, late-breaking results, preliminary work, dataset papers, and demos that address any of the following themes:

Topics of Interest:

  • Data quality frameworks in healthcare.
  • Data quality assessment and improvement.
  • Data-centric AI approaches for healthcare.
  • Task-driven data quality assurance for healthcare AI.
  • Multi-modal and multi-source data fusion in healthcare.
  • Hallucination and bias mitigation in LLMs for healthcare applications.
  • Demographic bias in training datasets for healthcare datasets.
  • Seemless (“zero-click”) integration of healthcare imaging AI in physicians’ workflow.
  • The role of standards for input and output of healthcare AI.
  • Methodologies to investigate the effect data quality has on medical AI system characteristics.
  • Techniques to validate the trustworthiness of AI in healthcare.
  • Privacy-preserving AI techniques for healthcare.
  • Methods to improve transparency and explainability of LLMs in healthcare applications.
  • Large language models for high-quality synthetic health data generation.
  • Human-in-the-loop systems for data quality control.
  • Case studies of data-quality-aware AI in healthcare.
  • Evaluating the capacity of large language models for different healthcare applications.
  • Retrieval augmented generation for high-performance and trustworthy healthcare AI.
  • Ethical and social implications of AI in healthcare.
  • Addressing bias mitigation and fairness of AI in healthcare.
  • Use of AI (XAI) techniques to promote trust of AI in healthcare.

Submission Guidelines:

We invite submissions that contribute to foundational theory, novel methodologies, and practical applications within the field of Data Quality Aware, High-Performance, and Trustworthy AI Systems for Healthcare. Submissions can take the form of:

  • Research papers should be between four and eight pages, including references, figures, and all other content. Submissions must contain original work not previously published or under consideration elsewhere. We encourage papers that introduce novel technological solutions (including early and in-progress work) and vision or position papers that outline emerging challenges and gaps in the field. Accepted research papers will be presented orally and will be included in the workshop proceedings, archived in the ACM Digital Library.
  • Posters are limited to two pages, plus references, and may describe early-stage work or work in progress. They will be presented as posters and published on the workshop website.
  • Demo proposals should describe a technology or system and outline how it will be demonstrated at the workshop. They are limited to one page, plus references.
Please use the link https://easychair.org/conferences/?conf=tais4h2026 to submit your papers.

Formatting:

Submissions should follow the IEEE conference proceedings format. All submissions will undergo a peer-review process, and accepted papers will be included in the workshop proceedings.

Important Dates:

  • Workshop Paper Submission Deadline: March 16, 2026
  • Workshop Notification of Acceptance: April 10, 2026
  • Workshop Camera-Ready Submission: April 20, 2026
  • Workshop Date: August 6, 2026 (tentative)

Workshop Organizers

  • Dr. Haihua Chen
    Assistant Professor of Data Science, affiliated in Health Informatics
    Department of Data Science, University of North Texas, USA
    Dr. Haihua Chen has the research expertise in the applications of data science and artificial intelligent, specializing in data quality and health informatics. His research is at the forefront of advancing methodologies to enhance data quality, particularly within medical and biomedical informatics. Through his innovative work, Dr. Chen explores the intersections of large language models (LLMs) and medical concept normalization (MCN), evaluating how data quality variations impact MCN performance. He is also developing frameworks that utilize data quality insights to improve LLMs for clinical applications. He has published more than 50 articles in top-tier journals (JBI, IPM, KBS, IEEE Transactions on Reliability, Information Sciences) and conferences proceeding (ACM MM, EMNLP, WWW, IEEE QRS, IEEE ICDM). He is serving as associated editor for The Electronic Library and Data Intelligence, and organizing committee members for several international conferences (AITest, JCDL, MOST).
  • Dr. Ana D. Cleveland
    Regents Professor, Sarah Law Kennerly Endowed Professor, and Director of Health Informatics
    Department of Information Science, University of North Texas, USA
    Dr. Ana D. Cleveland is known for her interdisciplinary, innovative and visionary curriculum development in the areas of information science and health informatics. Her research interests are in the application of large language models to disaster management informatics and dental informatics, medical information retrieval, use of virtual reality to manage stress and anxiety, health information-seeking behavior with a focus on underserved populations, transdisciplinary ancestral genomics, social media and health information sharing. In collaboration with colleagues and doctoral students, her research has received 8 awards from different organizations. In addition, she received the highest awards given by the Medical Library Association, including the Lucretia W. McClure Excellence in Education Award, Janet Doe Lectureship, Marcia C. Noyes Award, and Fellow of the Medical Library Association. In addition, Dr. Cleveland was honored with the President’s Award from the American Medical Informatics Association. She has an extensive list of publications. She has served as a consultant to numerous national and international agencies, including the World Health Organization, Global Health Council, National Institutes of Health, National Library of Medicine, and the Centers for Disease Control and Prevention.
  • Dr. Daqing He
    Professor of Information Science and Intelligent Systems Program and Department Chair
    Department of Informatics and Networked Systems, University of Pittsburgh, USA
    Dr. Daqing He is an interdisciplinary researcher, whose main research interests cover information retrieval and access, natural language processing, adaptive and interactive system design, and their applications in healthcare. Dr. He has been the Principal Investigator (PI) and Co-PI for various research grants funded by the National Science Foundation (NSF), National Institute of Health (NIH), United States Defense Advanced Research Projects Agency (DARPA), Amazon, UPMC, OCLC/ALISE, University of Pittsburgh, and other agencies. He has published more than 200 articles in internationally recognized journals and conferences in these areas, including Journal of Association for Information Science and Technology, Information Processing and Management, ACM Transaction on Information Systems, Journal of Medical Internet Research, JAMIA, Innovation in Aging, Journal of Information Science, IEEE Computers, ACM SIGIR, ACL, EMNLP, ACM CIKM, ACM SIGIR CHIIR, ACM CSCW, ASIST, and so on. He serves as the associate editor of “Aslib Journal of Information Management”.
  • Dr. Chen Li
    Associate Professor
    D3 Center, University of Osaka, Japan
    Dr. Chen Li primarily focuses on artificial intelligence for healthcare and AI-driven drug discovery. His research aims to develop advanced machine learning, multimodal modeling, and representation learning techniques to enable data-driven biomedical understanding, molecular design, and translational medical applications. His work investigates the integration of deep learning, graph learning, and molecular modeling to build scalable and reliable intelligent systems for therapeutic discovery and precision medicine. In particular, he is interested in structure–property modeling and omics-based multimodal learning for complex biomedical data. Dr. Li has published his research in leading international conferences and high-impact journals, and his contributions have been recognized with several prestigious awards, including the AAAI 2024 Outstanding Paper Award and the ADMA Best Paper Award. He actively serves the research community as an Area Chair for ICDM and as a program committee member for leading international conferences and journals, including AAAI, ICLR, ICML, and NeurIPS. He is also actively involved in interdisciplinary collaborations bridging artificial intelligence and biomedical sciences.
  • Dr. Deevakar Rogith
    Assistant Professor
    Department of Clinical and Health Informatics, UTHealth Houston
    Dr. Rogith is a clinical informatician specializing in human-centered design, evaluation, and implementation of digital health and AI-enabled decision support. His research focus on ensuring tools are safe, usable, and effective in routine care. His work emphasizes translating clinical needs into deployable systems by integrating electronic health record data (including longitudinal records and workflow/audit logs) and aligning AI outputs with real-world clinical workflows, governance, and quality and safety objectives. He contributes clinical implementation expertise to interdisciplinary teams developing interoperable, workflow-integrated AI solutions and practical evaluation strategies that support trustworthy adoption in diverse healthcare settings.