Explainable-AI Driven Secure Healthcare Systems for Multimodal Clinical Data
This special session is organised and supported by the following educational partners
Universiti Teknologi PETRONAS, Malaysia and Manav Rachna International Institute of Research and Studies, Faridabad, India
Session Chair:
- Dr Ganesh Kumar, Universiti Teknologi Petronas, Malaysia
Dr Rachna Behl, Manav Rachna International Institute of Research and Studies, India
Session Co-Chairs:
Prof Indu Kashyap, Manav Rachna International Institute of Research and Studies, India
- AP Dr Ts Mohammad Hilmi Hassan, Universiti Teknologi Petronas, Malaysia
Synopsis:
Artificial Intelligence (AI) is revolutionizing healthcare through intelligent diagnosis, predictive analytics, personalized medicine, and smart healthcare monitoring. However, challenges related to explainability, trust, privacy, cybersecurity, and secure integration of multimodal clinical data remain critical.
This special session provides a platform for researchers, academicians, clinicians, and industry practitioners to discuss recent advances in Explainable AI (XAI), secure healthcare systems, and intelligent clinical data analytics. The session aims to foster collaboration toward trustworthy, privacy-preserving, and AI-enabled healthcare solutions aligned with Industry 4.0, IoMT, and smart eSystems.
The objectives are:
- Discuss recent advancements in XAI-driven secure healthcare systems.
- Explore challenges and emerging solutions for multimodal clinical data analytics.
- Promote collaboration in developing trustworthy and intelligent healthcare technologies.
Topics:
Topics of interest include, but are not limited to:
- Explainable AI (XAI) for healthcare applications
- Interpretable and trustworthy deep learning models for clinical decision-making
- Multimodal clinical data analytics and data fusion
- Medical image processing and biomedical signal analysis
- Federated learning and privacy-preserving healthcare AI
- Blockchain-enabled healthcare systems and security frameworks
- Secure healthcare data management and cybersecurity
- AI-driven disease prediction, diagnosis, and prognosis systems
- Smart hospitals and intelligent healthcare automation
- Edge AI and real-time healthcare analytics
- AI for personalized and precision medicine
- Ethical, transparent, and trustworthy AI in healthcare
- Deep learning applications in biomedical and healthcare systems
- AI-enabled healthcare recommendation and decision support systems
- Intelligent eSystems and IoMT-based healthcare applications
- Big data analytics, medical informatics, and healthcare data intelligence
Paper Submission
Prospective authors are invited to submit full-length papers (not exceeding 6 pages) conform to the IEEE format . All papers will be handled and processed electronically via the EDAS online submission system