Intelligent Healthcare Systems: Empowered by Data Science and Artificial Intelligence

Dear Colleagues, 

Deep Learning (DL) and Data Science are becoming the driving forces behind the transformation of modern healthcare. By enabling the analysis of massive clinical, imaging, and molecular datasets, these technologies are unlocking breakthroughs in diagnosis, prognosis, and therapy. From medical image interpretation and disease prediction to personalized medicine and hospital operations optimization, deep neural networks and advanced data analytics play a pivotal role in improving the effectiveness, accuracy, and accessibility of healthcare systems.

This Special Issue aims to bring together the latest research and practical developments in integrating Deep Learning and Data Science into intelligent medical systems. We invite original research articles, reviews, and case studies that present innovative solutions, address real-world challenges, and provide insights into the benefits and limitations of DL- and data-driven approaches in healthcare.

Potential Topics:

-Deep Learning in Medical Imaging and Diagnostics: Applications of CNNs, GANs, and transformers for automated analysis of radiology, pathology, and other clinical images.

-Data Science for Predictive Healthcare: Machine learning and deep learning models for disease risk prediction, early diagnosis, and personalized treatment planning.

-Intelligent Clinical Decision Support Systems: DL- and data-driven platforms supporting physicians in diagnosis, therapy selection, and medication management.

-Wearable Devices and Telemedicine Analytics: DL algorithms for biometric signal interpretation and integration of heterogeneous health data sources.

-Clinical Data Management: Data pipelines, big data analytics, and AI models for effective utilization of electronic health records.

-Natural Language Processing in Healthcare: Deep Learning methods for clinical text mining, electronic health records analysis, and medical knowledge extraction.

-Ethics and Transparency of Deep Learning in Medicine: Explainable AI, privacy-preserving models, and mitigation of algorithmic bias in healthcare applications.

-Deep Learning for Public Health: Epidemic prediction, population health modeling, and real-time health data analysis.

This Special Issue will unite researchers, clinicians, and industry experts to explore the transformative potential of Deep Learning and Data Science in healthcare. It aims to foster knowledge exchange, support interdisciplinary collaboration, and contribute to the development of intelligent, ethical, and patient-centered medical systems of the future.

Prof. Dr. Michal Tomaszewski
Prof. Dr. Serhii Lupenko
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.