AI and Machine Learning in Medical Image Processing: Innovations and Applications—2nd Edition
A Special Issue of Bioengineering (ISSN 2306-5354) belonging to the section "Biosignal Processing".
Deadline for manuscript submissions: 30 April 2027 | Viewed by 1472
Editors
Interests: artificial intelligence in dermatology; deep learning for skin cancer detection and classification; dermatologic image analysis (dermoscopy and clinical photography); computational dermatopathology and histopathology image analysis; computer vision in cutaneous oncology; digital pathology in Mohs micrographic surgery; teledermatology and store-and-forward imaging; large language models and generative AI in dermatology; clinical decision support systems
Interests: artificial intelligence and machine learning in dermatology; skin cancer image analysis (melanoma and keratinocyte carcinoma); cutaneous oncology and risk stratification; large language models in clinical dermatology; image-based dermatologic diagnosis and triage; medical education and simulation in dermatology
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) and machine learning (ML) have brought unprecedented changes to the field of medical image processing, offering new opportunities to improve diagnosis, treatment planning, and patient outcomes. This Special Issue, titled ‘AI and Machine Learning in Medical Image Processing: Innovations and Applications—2nd Edition’, aims to provide an overview of cutting-edge research, emerging methodologies, and impactful applications at the intersection of AI, ML, and medical imaging.
The goal of this Special Issue is to showcase innovative approaches that address key challenges in medical image acquisition, analysis, interpretation, and clinical integration. Contributions should highlight breakthroughs across a range of imaging modalities, including MRI, CT, ultrasound, and histopathology imaging, and cover topics such as segmentation, classification, detection, prediction models, and AI-augmented decision support systems.
By bringing together a diverse collection of theoretical advancements, novel algorithms, and real-world case studies, this Special Issue seeks to foster interdisciplinary collaboration among researchers, clinicians, and industry practitioners. Ultimately, it aims to illuminate how AI and ML technologies are used in medical imaging, paving the way for more accurate diagnoses, personalized therapies, and improved healthcare delivery.
We invite contributions that advance the understanding and application of AI and ML in medical imaging, address ethical and regulatory challenges, and propose visions for the future of this rapidly evolving field.
Dr. Neil K. Jairath
Dr. Vartan Pahalyants
Guest Editors
Mr. Joshua Mijares
Guest Editor Assistant
Email: josmijar@iu.edu
Department of Dermatology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
Research Interest: AI applications in dermatology; dermatologic triage and risk stratification; cutaneous oncology
Manuscript Submission Information
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Keywords
- artificial intelligence (AI) in medical image processing
- machine learning (ML) in medical image processing
- deep learning in medical image processing
- computer vision in medical imaging
- medical image segmentation
- diagnostic imaging
- disease detection in medical images
- clinical decision support systems
- histopathological image analysis
- advanced techniques for cancer detection, segmentation, and classification
- applications in breast, prostate, brain, and other cancer imaging using AI
- detection and pattern recognition in medical imaging
- imaging modalities (MRI, CT, ultrasound, X-ray)
- explainable AI (XAI) in healthcare
- healthcare technology and innovation
- predictive modeling in medical imaging
- generative AI and large language models (LLMs) for medical imaging
- big data analytics in healthcare
- automated diagnosis systems
- biomedical engineering applications
- ethics and fairness in medical AI
- systematic reviews in medical image analysis
- medical imaging datasets and benchmarking
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