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Artificial Intelligence (AI) and Deep Learning (DL) in Medical Imaging
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Artificial Intelligence (AI) and Deep Learning (DL) in Medical Imaging

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Nuclear Medicine & Radiology".

Deadline for manuscript submissions: 20 January 2026 | Viewed by 984

Special Issue Editor

Dr. Francesco Pucciarelli

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Guest Editor
Department of Surgical and Medical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy
Interests: artificial intelligence; oncologic imaging; chest imaging; muscoloskeletal imaging; abdominal imaging

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) and Deep Learning (DL) are transforming medical imaging, enabling more accurate diagnoses, improved disease detection, and enhanced workflow efficiency. This Special Issue aims to explore the latest advancements, applications, and challenges in AI-driven medical imaging, with particular attention given to the development and validation of novel approaches and diagnostic models across diverse imaging settings, as well as their clinical and practical implications. Despite significant progress, core challenges, such as data scarcity, model interpretability, and integration into clinical workflows, remain critical.

This Special Issue welcomes original research and reviews on AI methodologies applied to medical imaging tasks such as diagnostic and histological classification, anomaly detection, and radiomics. Submissions that provide insights into translational applications—from experimental models to real-world clinical practice, as well as those exploring AI-based clinical decision support systems—are particularly valued.

We invite researchers, clinicians, and AI experts to contribute their latest findings, fostering interdisciplinary collaboration and innovation. By bringing together cutting-edge research, this Special Issue seeks to accelerate AI adoption in medical imaging and improve patient care.

Dr. Francesco Pucciarelli
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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.

Keywords

  • artificial intelligence
  • deep learning
  • imaging
  • diagnosis
  • precision medicine
  • oncologic

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Published Papers (1 paper)

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Review

27 pages, 1970 KB  
Review
Artificial Intelligence in Alzheimer’s Disease Diagnosis and Prognosis Using PET-MRI: A Narrative Review of High-Impact Literature Post-Tauvid Approval
by Rafail C. Christodoulou, Amanda Woodward, Rafael Pitsillos, Reina Ibrahim and Michalis F. Georgiou
J. Clin. Med. 2025, 14(16), 5913; https://doi.org/10.3390/jcm14165913 - 21 Aug 2025
Viewed by 755
Abstract
Background: Artificial intelligence (AI) is reshaping neuroimaging workflows for Alzheimer’s disease (AD) diagnosis, particularly through PET and MRI analysis advances. Since the FDA approval of Tauvid, a PET tracer targeting tau pathology, there has been a notable increase in studies applying AI to [...] Read more.
Background: Artificial intelligence (AI) is reshaping neuroimaging workflows for Alzheimer’s disease (AD) diagnosis, particularly through PET and MRI analysis advances. Since the FDA approval of Tauvid, a PET tracer targeting tau pathology, there has been a notable increase in studies applying AI to neuroimaging data. This narrative review synthesizes recent, high-impact literature to highlight clinically relevant AI applications in AD imaging. Methods: This review examined peer-reviewed studies published between January 2020 and January 2025, focusing on the use of AI, including machine learning, deep learning, and hybrid models for diagnostic and prognostic tasks in AD using PET and/or MRI. Studies were identified through targeted PubMed, Scopus, and Embase searches, emphasizing methodological diversity and clinical relevance. Results: A total of 109 studies were categorized into five thematic areas: Image preprocessing and segmentation, diagnostic classification, prognosis and disease staging, multimodal data fusion, and emerging innovations. Deep learning models such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer-based architectures were widely employed by the research community in the field of AD. At the same time, several models reported strong diagnostic performance, but methodological challenges such as reproducibility, small sample sizes, and lack of external validation limit clinical translation. Trends in explainable AI, synthetic imaging, and integration of clinical biomarkers are also discussed. Conclusions: AI is rapidly advancing the field of AD imaging, offering tools for enhanced segmentation, staging, and early diagnosis. Multimodal approaches and biomarker-guided models show particular promise. However, future research must focus on reproducibility, interpretability, and standardized validation to bridge the gap between research and clinical practice. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Deep Learning (DL) in Medical Imaging)
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