Artificial Intelligence (AI) has already rapidly transformed healthcare, driving innovations in diagnosis, treatment, workflow optimization, and patient engagement. From large language models that can summarize clinical documentation to deep learning systems that detect subtle patterns in radiology and cardiology images, AI promises to augment clinical expertise and enhance the quality of care. However, we must realize this potential requires bridging the gap between cutting edge research and the operational realities of healthcare delivery.

The tracks interdisciplinary scope welcomes contributions from AI researchers, healthcare practitioners, academic institutions, and industry leaders. Papers may present novel algorithms, apply case studies, system architectures, validation studies, or lessons learned from real world developments. By bringing diverse perspectives, the track aims to foster dialogue across academic research, medical practice, and enterprise IT accelerating the path from algorithmic innovation to clinical adoption.

Papers and contributions are encouraged for any work relating to AI in Healthcare Informatics. Topics of interest may include (but are in no way limited to):

• Medical image processing comment segmentation, and classification
• Natural language processing for electronic health records and clinical narratives
• Deep learning and representation learning for biomedical data
• Predictive analytics for disease progression and treatment outcomes
• Explainable AI and trust in clinical decision making
• Privacy preserving AI and Federated learning and healthcare
• Security, governance, and compliance and healthcare AI systems
• Bias, fairness, and equity in healthcare applications
• Workflow optimization, triage systems, and real time alerting
• Genre of AI and immersive technologies for medical education and training
• Integration of AI into enterprise imaging (radiology, cardiology, laboratory, pathology)

Questions regarding the track should be addressed to: Doug Talbert at dtalbert@tntech.edu.