The Bulletin of the National Research Centre invites original research papers for a Scopus-indexed special issue titled “AI and Data Science for Health”.
The AI and Data Science for Health collection focuses on research applying artificial intelligence and advanced data science techniques to healthcare and health-related challenges.
The journal is published by Springer Nature and operates as a fully open-access journal.
Bulletin of the National Research Centre has been accepted for inclusion in the Scopus database. Moreover, there is no publication fee for publishing in this journal.
Interested authors can submit their papers by 9 June 2027.
| Details | Information |
|---|---|
| Journal | Bulletin of the National Research Centre |
| Collection | AI and Data Science for Health |
| Publication Model | Open Access |
| APC | No author-paid APC |
| Deadline | 9 June 2027 |
| Publisher | Springer Nature |
| Subject Areas | AI, Machine Learning, Data Science, Healthcare, Medical Imaging, IoT, Generative AI and more |
Free Open Access Publication
One of the key features of this call is that authors do not have to pay an Article Processing Charge (APC) for publication in the Bulletin of the National Research Centre.
According to the journal, the APC is covered through an agreement between Springer Nature and the Specialized Presidential Council for Education and Scientific Research, Government of Egypt.
Therefore, eligible articles accepted for publication can be made openly accessible without an author-paid publication fee.
Topics Covered in the Special Collection
The collection welcomes research covering a broad range of AI and data science applications in healthcare.
Topics of interest include, but are not limited to:
- Machine learning applications in healthcare
- Deep learning for medical imaging
- Predictive modeling in clinical settings
- Multi-omics integration for personalized medicine
- Explainable AI in clinical decision support
- Generative AI and large language models in healthcare
- Healthcare data privacy, security, and federated learning
- Ethical, responsible, and explainable AI
- Bias, fairness, and transparency in medical AI
- Natural language processing and electronic health records
- Wearable devices, remote patient monitoring, and the Internet of Medical Things
- Clinical validation and real-world implementation of AI systems
- AI applications in public health, epidemiology, and drug discovery
- Integration of Generative AI and Large Language Models with Internet of Things (IoT) for Smart Healthcare
- Generative AI and LLM-Based Intelligent IoT Healthcare Systems
Submission Guidelines
- Manuscripts must be original and unpublished
- All submissions will undergo double-blind peer review
- Authors must follow JUMP journal formatting guidelines
- Interdisciplinary and comparative studies are welcome
Submission Link: https://link.springer.com/collections/gdhdciccjb