About us

TAIS4H 2026 explores novel solutions, latest techniques, best practices, and future directions for developing high-performance and trustworthy AI systems for healthcare, with a specific focus on the critical role of input data quality. This interdisciplinary workshop brings together leaders, practitioners, and researchers to address ethics, transparency, and safety in healthcare AI.

Important Dates

Mon, Mar 16, 2026

Submission deadline

Paper Submission

Research Paper Submission Tracks

Trustworthy AI systems

Health informatics

Ai in health

Data centric ai

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.

Call for Papers Description

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 (4-8 pages), posters (2 pages), or demo proposals (1 page).