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. This is the second version of the workshop on "Data Quality Aware, High-Performance, and Trustworthy AI Systems for Healthcare," reflecting the emerging importance of this interdisciplinary area. 

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.
• Submission site: https://easychair.org/conferences/?conf=tais4h2026

List of Topics

• 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.

Committees

Organizing committee

• Dr. Haihua Chen, Department of Data Science, University of North Texas, USA
• Dr. Ana D. Cleveland, Department of Information Science, University of North Texas, USA
• Dr. Daqing He, Department of Informatics and Networked Systems, University of Pittsburgh, USA
• Dr. Chen Li, D3 Center, University of Osaka, Japan
• Dr. Deevakar Rogith, Department of Clinical and Health Informatics, UTHealth Houston, USA

Publication

Accepted research papers will be included in the IEEE/ACM CHASE 2026 workshop proceedings, archived in the ACM Digital Library.

Venue

The conference will be held in Pittsburgh, PA, USA, August 4-6, 2026

Contact

All questions about submissions should be emailed to Dr. Haihua Chen (haihua.chen@unt.edu)