Advancing Cancer Radiodiagnostics Through Artificial Intelligence: Techniques, Applications, and Challenges
A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Medical Research".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 318
Special Issue Editors
Interests: machine learning; deep learning; artificial intelligence; health; medical informatics; radiotheapy; cancer radiodiagnostics
Interests: biomedical engineering; artificial intelligence; medical informatics; radiation applications
Special Issues, Collections and Topics in MDPI journals
Interests: biomedical Informatics; health data representation; medical informatics
Interests: radiology; interventional radiology; interventional oncology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) has emerged as a transformative tool in cancer radiodiagnostics, offering novel approaches for improving the accuracy, efficiency, and accessibility of cancer detection and treatment planning. This Special Issue, titled “Advancing Cancer Radiodiagnostics Through Artificial Intelligence: Techniques, Applications, and Challenges”, invites researchers, clinicians, and industry experts to contribute their work in this rapidly evolving field.
This Special Issue focuses on AI-driven techniques that leverage advanced algorithms, data analysis, and machine learning methodologies to optimize cancer diagnostics. Emphasis will be placed on real-world applications, novel AI methodologies, and overcoming challenges in clinical implementation. Topics of interest include, but are not limited to, the following:
- The development and application of AI algorithms for tumor detection and diagnosis;
- AI integration with imaging modalities such as MRI, CT, PET, and ultrasound;
- Radiomics-based machine learning and deep learning models for cancer prediction;
- Multimodal data integration, including radiomics, genomics, and clinical data;
- Addressing challenges in AI model interpretability, validation, and generalization;
- Ethical and regulatory considerations for implementing AI in radiodiagnostics;
- Case studies showcasing AI implementation in clinical radiology.
This Special Issue aims to advance the knowledge and application of AI in radiodiagnostics, fostering interdisciplinary collaboration and accelerating the adoption of AI in oncology to enhance patient outcomes.
Dr. Pei-Ju Chao
Prof. Dr. Tsair-Fwu Lee
Prof. Dr. Peter Elkin
Dr. Roberto Iezzi
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- cancer radiodiagnostics
- machine learning
- tumor detection
- radiomics integration
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