AI-Driven Approaches to Diseases Detection and Diagnosis
A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 11
Special Issue Editors
Interests: genetics
Interests: medical imaging; intelligent systems; bioengineering; signal processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of artificial intelligence (AI) into healthcare is transforming the way that diseases are detected, diagnosed, and managed. Advances in machine learning, deep learning, and data-driven modeling are enabling earlier detection, higher accuracy, and more personalized decision support across a wide range of clinical contexts. From medical imaging and genomics to digital biomarkers and wearable sensors, AI offers unprecedented opportunities to accelerate diagnosis, reduce human error, and improve patient outcomes.
This Special Issue of Bioengineering aims to bring together cutting-edge research and review articles that explore the development, validation, and application of AI-driven approaches for disease detection and diagnosis. We welcome contributions spanning algorithm design, clinical implementation, multi-modal data integration, interpretability, ethical considerations, and real-world benchmarks. Submissions may include original research, methodological advances, comprehensive reviews, and case studies that highlight both opportunities and challenges in the field.
We particularly encourage interdisciplinary studies that bridge engineering, computer science, biomedical research, and clinical practice. By assembling diverse perspectives, this Special Issue seeks to advance the understanding and responsible adoption of AI technologies that have the potential to shape the future of medicine.
Kind regards,
Dr. Yannis Trakadis
Dr. Antonio Brunetti
Guest Editors
Manuscript Submission Information
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Keywords
- AI-driven diagnostics
- disease detection
- machine learning
- deep learning
- medical imaging
- digital biomarkers
- clinical decision support
- multi-modal data
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