AI-Enhanced Medical Imaging for Cancer Diagnosis
A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Oncology".
Deadline for manuscript submissions: 15 April 2026 | Viewed by 81
Special Issue Editors
2. Barts Cancer Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, London, UK
3. Barts Cancer Institute, Queen Mary University of London, London, UK
4. European Interdisciplinary Society for AI in Cancer Research (ESAC), Milan, Italy
Interests: artificial intelligence; cancer diagnosis; precision oncology; AI-driven imaging; MRI
2. School of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
3. Curenetics, London, UK
Interests: low field MRI; affordable MRI; whole body MRI; MRI finger-printing; oncological imaging
Special Issues, Collections and Topics in MDPI journals
2. Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
3. AELIA Organization, 9th Km Thessaloniki-Thermi, 57001 Thessaloniki, Greece
4. Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, Canterbury, UK
5. School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
6. Kent and Medway Medical School, University of Kent, Canterbury, UK
7. Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham, UK
Interests: prostate cancer; renal cancer; ovarian cancer; homologous recombination of DNA; PARP inhibitors; cervical cancer; carcinoma of unknown primary; colorectal cancer; cancer and autoimmune diseases; biomarkers
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Advances in artificial intelligence (AI) are revolutionizing the field of medical imaging, offering transformative potential for early and accurate cancer diagnosis. This Special Issue explores how AI-driven technologies, including deep learning algorithms, convolutional neural networks (CNNs), and computer-aided detection systems, are enhancing the interpretation of radiological data from modalities such as MRI, CT, PET, and X-ray. By automating tumour detection, segmentation, and classification, these tools minimize human error, accelerate diagnostic workflows, and uncover subtle patterns imperceptible to the human eye. Key studies highlight AI's ability to improve sensitivity and specificity in identifying malignancies, particularly in breast, lung, and prostate cancers, while reducing false positives and unnecessary biopsies.
Dr. Aruni Ghose
Dr. Sola Adeleke
Dr. Stergios Boussios
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- imaging
- MRI
- precise diagnosis
- oncology
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