Workshop on Web-Enabled Trustworthy AI for Healthcare: From Data Ecosystems to Clinical Impact (WebHealthAI 2026)
Artificial Intelligence coupled with Web-based information systems is revolutionizing health care into a data-driven, interconnected ecosystem. This workshop will concentrate on state-of-the-art research at the crossroads of AI, Web technologies and healthcare applications, with a focus on trustworthy, scalable and interoperable solutions. It aims at tackling key challenges like the integration of heterogeneous health data sources, the deployment of AI models into real-world clinical workflows, explainability and fairness, and the use of Web-based infrastructures for largescale healthcare intelligence.
The workshop will not address traditional AI-for-health venues, but will focus on Web-centric paradigms such as linked health data, semantic interoperability, Web services, and distributed AI systems. It will promote collaborations between disciplines of researchers, clinicians and industry practitioners for the design of next generation intelligent healthcare systems that are reliable, ethical and accessible.
At the end, the workshop seeks to fill the gap between advanced AI models and their safe and effective adoption in real-world healthcare ecosystems.
AI & Data Intelligence for Healthcare
- Machine learning and deep learning for diagnosis, prognosis, and treatment recommendation
- Large Language Models (LLMs) for clinical decision support
- Multimodal healthcare data fusion (EHR, imaging, genomics, IoT)
Web-Based Healthcare Information Systems
- Semantic Web technologies for healthcare (ontologies, RDF, linked health data)
- Web services and APIs for interoperable health systems
- Federated and distributed learning over Web infrastructures
Trustworthy and Ethical AI
- Explainable AI (XAI) in clinical settings
- Bias detection and fairness in medical AI
- Privacy-preserving AI (federated learning, differential privacy)
Data Engineering & Knowledge Management
- Healthcare knowledge graphs and knowledge extraction
- Data integration, quality, and provenance in health data ecosystems
- Real-time health data analytics and stream processing
Applications & Emerging Paradigms
- AI for telemedicine and remote patient monitoring
- Smart hospitals and IoT-enabled healthcare systems
- Digital twins and personalized medicine
- AI-driven public health surveillance using Web data
All submissions for this workshop must be written in English and conform to the Springer proceedings format with the following page limits: 12 pages for papers including references. Submitted papers will undergo a “double-blind” review process, coordinated by the Program Committee. To ensure anonymity of authorship, authors must ensure that authors’ names, affiliations, funding, and any other identifying information of authorship do not appear on the title page or elsewhere in the paper. Authors must provide the complete and final list of authors at the submission stage. No addition, removal, or change in the order of authors is allowed after submission.
Please use one of the following templates for the LNCS (Lecture Notes in Computer Science) format: https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines
All paper submissions for this workshop will be via Microsoft CMT (under “Workshop on Web-Enabled Trustworthy AI for Healthcare: From Data Ecosystems to Clinical Impact”).
The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.
| Submission Deadline (extended) | |
| Authors Notification (revised) | |
| Final Manuscript Due | 25 August 2026 |
*All deadlines are 23:59 Anywhere on Earth.
- Sami Naouali (King Faisal University, KSA)
- Oussama Othmani (Centre de Recherches Militaire de Tunis, Tunisia)
- Majed Kahouli (Centre de Recherches Militaires de Tunis, Tunisia)
- Majd Kahouli (Centre de Recherches Militaire de Tunis, Tunisia)
Sami Naouali (King Faisal University, KSA) <[email protected]>
