Artificial intelligence is becoming more and more integrated into our daily lives. AI-basedsystems assist users with decision-making, pattern finding, knowledge acquisition, andrecommendation services. AI-based technologies such as Machine Learning and DeepLearning provide an opaque view of the working model and conceal internal modelinformation from consumers. The user needs some openness, interpretability, andexplainability to understand the functioning model better. This is the domain in whichexplainable AI will supply people with visible artificial intelligence solutions; comprehendingand knowing the rationale or cause for AI decisions is challenging.Explainable AI (XAI) offers a framework to enable thorough comprehension of the machineand deep learning black box model choices locally or globally. In particular fields, such asretaining customers, claims management, insurance pricing, payment exceptions, banking,healthcare, and criminal justice, using XAI for model explainability requires safety anddependability. Therefore, explainable Artificial Intelligence presents a chance to create newbeneficial technologies that provide reasoning, visibility, and explanatory models, allowing forthe creation of better AI systems in the future. This special session calls for innovativeresearch articles on the explainable or interpretable machine and deep learning algorithmsfor intelligent medical image computing applications to solve the shortcomings in medicalimage computing.

Submission Guidelines

All the submitted papers should be unique and novel, previously unpublished, or currently not under review for publication in other journals or conferences. The following paper categories are welcome:

• Full papers
• Short papers 
• Review papers

List of Topics

• Methods and applications of Explainable AI in Healthcare
• Transparent fuzzy based Multimodal medical data fusion
• Design and development of computer-assisted Medical Systems using XAI
• Text Mining and Natural Language Processing in Medical Documents
• Visualization in Medical Imaging
• Transparent Deep Learning applications in healthcare
• Interpretable Medical Expert Systems
• Data Mining and Knowledge Discovery in Healthcare
• Signal analysis and processing using XAI techniques
• Intelligent and Transparent AI-based systems for telemedicine

Committees

Program Committee : IHIC-2023 (Special Session on:Explainable Artificial Intelligence for Analysis and Diagnosis ofHealth Data (EAIADH 2023))

• Dr. S.P.Shantharajah, Professor, SITE, Vellore Institute of Technology, Vellore, India. 
• Dr. D.Karthikeyan, Assistant Professor, SITE, Vellore Institute of Technology, Vellore,India.
• Dr. G. Mohanraj, Assistant Professor, SITE, Vellore Institute of Technology, Vellore,India.
• Dr. Surej Mouli, Professor, Department of Biomedical and Design Engineering, AstonUniversity, Birmingham, UK.

Organizing committee

• Dr. S.P.Shantharajah, Professor, SITE, Vellore Institute of Technology, Vellore, India. 
• Dr. D.Karthikeyan, Assistant Professor, SITE, Vellore Institute of Technology, Vellore,India.
• Dr. G. Mohanraj, Assistant Professor, SITE, Vellore Institute of Technology, Vellore,India.
• Dr. Surej Mouli, Professor, Department of Biomedical and Design Engineering, AstonUniversity, Birmingham, UK.

Publication

All accepted and registered papers of EAIADH 2023 will be published with Springer in their prestigious "Lecture Notes in Electrical Engineering" series. LNEE Proceedings (https://www.springer.com/series/7818) will be made available to the following indexing services: Scopus, Scimago Journal & Country Rank, and Google Scholar; and will also be submitted to dblp computer science bibliography for inclusion

Venue

Management Education & Research Institute,Delhi, India 

Contact

All questions about submissions should be emailed to karthikeyan.duraisamy@vit.ac.in