Current Medical Imaging

Journal Impact Factor: 1.1
Scopus Cite Score: 1.9

Indexed in: Scopus, SCI Expanded, MEDLINE/PubMed

View All
Aims and Scope:
Current Medical Imaging publishes frontier review articles, original research articles, case reports, drug clinical trial studies, and guest- edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation, and therapeutic applications related to all modern medical imaging techniques including but not limited to:

  • Cardiac Imaging
  • Computed Tomography
  • Computer-aided Diagnosis
  • Machine Vision in Medicine
  • Magnetic Resonance
  • Medical Image Visualization
  • Medical Imaging and Analysis
  • Molecular Imaging
  • Musculoskeletal Imaging
  • Nuclear Medicine
  • Pattern Recognition in Medical Images
  • Pre-clinical Imaging
  • Vascular and Interventional Radiology
  • Women's and Pediatric Imaging
  • X-ray and Abdominal Imaging
  • Other related areas
The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.
Read more...
Start Submission Button Icon

Editor-in-Chief:

  • Euishin E. Kim Department of Radiological Sciences
    University of California
    Irvine, CA
    United States of America

ISSN: 1875-6603

Special Issues With Active Call for Papers

Submission closes on: Nov 21, 2026
Artificial Intelligence and Sustainability in Modern Medical Imaging: Opportunities, Challenges, and Future Directions

Medical imaging is at the forefront of healthcare innovation, with artificial intelligence (AI) and sustainability emerging as two defining themes for the future of clinical practice. AI applications are transforming diagnostic accuracy, workflow efficiency, and personalised care, while sustainability frameworks emphasise reducing energy consumption, carbon footprint, and environmental impact of imaging technologies. This thematic issue aims to integrate these parallel streams by presenting cutting-edge research, reviews, and case studies on AI in imaging, sustainable imaging... see more

Impact Factor: 1.1
Guest Editor(s): Dr. Mohamed M Abuzaid
Co-Guest Editor(s): Dr. Christina Malamateniou,Dr. Theophilus Akudjedu,Dr. Wiam Elshami
Start Submission
Submission closes on: Nov 08, 2026
AI and Deep Learning Applications in Healthcare Disease Diagnosis and Management

The integration of Artificial Intelligence (AI) and Deep Learning (DL) technologies into healthcare has brought transformative advancements in disease diagnosis, treatment planning, and patient management. The increasing volume of complex biomedical data including medical imaging, genomics, electronic health records (EHRs), and wearable device outputs has necessitated the use of intelligent computational approaches for accurate and efficient analysis. This special issue on AI and Deep Learning Applications in Healthcare Disease Diagnosis and Management aims to present... see more

Impact Factor: 1.1
Guest Editor(s): Dr. Sandeep Kumar Mathivanan
Co-Guest Editor(s): Manjula Sanjay Koti,Sathishkumar V E
Start Submission
Submission closes on: Aug 03, 2026
AI-Augmented Multimodal Imaging for Early Diagnosis and Precision Medicine

This mini-thematic issue focuses on the convergence of artificial intelligence (AI) and multimodal medical imaging technologies to facilitate early and accurate disease diagnosis, enable precision medicine, and reduce diagnostic delays. With the growing complexity of image-based data in clinical settings, conventional techniques often fall short in delivering accurate and timely insights. This issue invites original research and review papers showcasing the use of AI—especially deep learning, federated learning, and explainable models—for enhancing image fusion, segmentation,... see more

Impact Factor: 1.1
Guest Editor(s): Dr. Prabu S
Co-Guest Editor(s): Uma Maheshwari,Karthikeyan Kaliyaperumal,Dr. Manoj Kumar,Dr. Achyut Shankar
Start Submission
Submission closes on: Jul 10, 2026
Quantum AI in Medical Image Analysis

The integration of quantum computing with artificial intelligence (AI) is reshaping the future of medical image analysis, introducing unprecedented computational capabilities and diagnostic precision. This special issue explores the transformative synergy between quantum algorithms—particularly quantum generative models—and state-of-the-art AI techniques in advancing healthcare diagnostics. From enhancing image resolution to enabling early disease detection and individualized treatment planning, contributions in this issue examine how quantum AI is revolutionizing biomedical data interpretation. By showcasing theoretical innovations, algorithmic... see more

Impact Factor: 1.1
Guest Editor(s): Dr. Anas Bilal
Co-Guest Editor(s): Dr. Raheem Sarwar,Faudillah Alhumairah
Start Submission
Submission closes on: Jul 05, 2026
Quantum AI in Medical Diagnostics: Transforming Precision, Prediction, and the Healthcare System

In recent years, the intersection of Quantum Computing and Artificial Intelligence (AI)—commonly referred to as Quantum AI (QAI)—has emerged as a transformative force in various domains, with healthcare being one of the most promising areas of application. Medical diagnostics is increasingly dependent on large-scale, high-dimensional data such as medical images, genomic sequences, electronic health records, and sensor data from wearable devices. Traditional AI models, although powerful, face computational bottlenecks when dealing with such complex and... see more

Impact Factor: 1.1
Guest Editor(s): Dr. Chaitanya Singla
Co-Guest Editor(s): Deepika Koundal,Gurpreet Singh,Ravneet Kaur,Weiwei Jiang,Jinchao Chen
Start Submission
Submission closes on: May 31, 2026
Advances in Medical Imaging for Women’s Diseases: Diagnostics, AI, and Personalized Care

This special issue will highlight cutting-edge advances in medical imaging that specifically target diseases disproportionately or exclusively affecting women. Despite significant technological progress, many conditions such as breast cancer, ovarian cancer, endometriosis, and pregnancy-related complications remain underdiagnosed or misdiagnosed due to gender-blind imaging approaches and limited availability of sex-specific biomarkers. With the integration of artificial intelligence (AI), radiomics, and multimodal imaging, there is a critical need for gender-sensitive innovations that improve early detection, diagnostic precision,... see more

Impact Factor: 1.1
Guest Editor(s): Dr. Akshi Kumar
Co-Guest Editor(s): M.P.S Bhatia,Dr. Saurabh Sangwan
Start Submission
Submission closes on: Apr 02, 2026
Advanced Feature Extraction Techniques in Medical Imaging for Early Diagnosis of Complex Diseases.

Medical images are located and retrieved by convolution neural networks. It extracts powerful picture features for label description using deep learning technology. It then sends the relevant parameters to execute tag matching and identify the photos as queries. A variety of techniques, including statistical analysis, pre trained models, and mathematical transformations, can be used for feature extraction. Standard methods for processing images include texture analysis, edge detection, and colour based approaches. It obtains new features... see more

Impact Factor: 1.1
Guest Editor(s): Dr. Rabbia Mahum
Co-Guest Editor(s): Dr. Mohammed A. Elmeligy,Dr. Muhammad Awais,Dr. Haseeb Hassan
Start Submission
Submission closes on: Mar 21, 2026
Deep Learning in Neuroimaging

Introduction – Deep learning has revolutionized medical imaging by offering automated, high-precision analytical methods that transform diagnostic processes. This proposal outlines a topical collection dedicated to “Deep Learning in Neuroimaging,” emphasizing its critical role in enhancing diagnostic accuracy, early disease detection, and personalized treatment strategies in neurological disorders. Importance of the Topic - Neuroimaging is integral to the early diagnosis and management of conditions such as brain tumors, Alzheimer’s disease, and multiple sclerosis. Recent advances... see more

Impact Factor: 1.1
Guest Editor(s): Dr. Sandeep Kautish
Co-Guest Editor(s): Dr. Ali Wagdy Mohamed,Dr. Weiwei Jiang,Dr. Alaa Sheta
Start Submission
Submission closes on: Mar 21, 2026
Generative AI and Machine Learning for High-Resolution Medical Image Analysis

Generative AI is revolutionizing medical imaging by producing high-resolution, synthetic yet realistic images that enhance disease diagnosis, monitoring, and treatment planning. By leveraging deep learning and neural networks, these models improve image quality, reduce noise, and support anomaly detection. Additionally, AI-driven approaches enable predictive analysis, aiding in disease progression forecasting and automated image interpretation. Beyond imaging, generative AI enhances natural language processing (NLP) applications, including medical report generation and multilingual healthcare accessibility. However, ethical and... see more

Impact Factor: 1.1
Guest Editor(s): Dr. Mustafa Bin Man
Co-Guest Editor(s): Dr. Henderi,Dr. Bishwajeet Kumar Pandey
Start Submission
Submission closes on: Mar 19, 2026
Ultrahigh Field Magnetic Resonance Imaging for GIST Interpretation and Diagnostic Measurements

A non-invasive imaging technique termed magnetic resonance imaging (MRI) creates detailed, three-dimensional anatomy images. It is frequently employed in the diagnosis, monitoring, and detection of diseases. MRIs use magnets to create three-dimensional images of the body’s internal structures. It can draw attention to alterations in bodily tissue that point to damage. In contrast, an ultrasound creates images of the body’s internal organs and structures using high-frequency sound waves. A diagnostic examination called magnetic resonance imaging... see more

Impact Factor: 1.1
Guest Editor(s): Dr. Rolando Quintero
Co-Guest Editor(s): Dr. Miguel Torres-Ruiz,Dr. Rustam Asnawi
Start Submission
Submission closes on: Mar 19, 2026
Generative AI for Data Augmentation in Small Medical Imaging Datasets

Opportunities exist to automate and enhance the precision of medical picture analysis through the use of generative AI. Picture segmentation, picture synthesis, patient outcome prediction, and anomaly detection are among the applications of Generation AI. Data security and privacy are two major issues that businesses may run into when generative AI is being implemented. Large data sets are essential for generative AI models to produce precise and significant results. But managing such vast amounts of... see more

Impact Factor: 1.1
Guest Editor(s): Dr. Azree Nazri
Co-Guest Editor(s): Dr. Gary Loh Chee Wyai,Dr. Bartholomew Odinaka Ogbonna
Start Submission
Submission closes on: Mar 17, 2026
Deep Learning for Early Disease Detection in Medical Images

In the medical area, computer-aided detection through Deep Learning (DL) and Machine Learning (ML) is rapidly expanding. Medical pictures are thought to be the real source of the relevant data needed for disease diagnosis. One of the most crucial things to reduce the death rate from cancer and tumours is early disease detection using a variety of modalities. Radiologists and medical professionals can better understand the internal anatomy of a discovered disease by using modalities... see more

Impact Factor: 1.1
Guest Editor(s): Dr. Muhammad Asim Saleem
Co-Guest Editor(s): Dr. Uzair Aslam Bhatti,Dr. Zahid Ullah
Start Submission
Submission closes on: Mar 06, 2026
Integration of Artificial Intelligence in Diagnostic Imaging in health care

The integration of Artificial Intelligence (AI) into diagnostic medical imaging is revolutionizing healthcare through improving the accuracy as well as efficiency of medical diagnoses. AI-based algorithms can swiftly analyze the complex biomedical images, viz., X-rays, MRIs, and CT scans for detecting patterns and abnormalities that might be challenging for human eyes to determine, facilitating early disease detection along with intervention. This advancement not only enhances the patient outcomes but also streamlines the workflow within the... see more

Impact Factor: 1.1
Guest Editor(s): Dr. Saurav Mallik
Co-Guest Editor(s): Dr. Sandeep Kumar M,Dr. Hong Qin,Dr. Sudeshna Rakshit
Start Submission
Submission closes on: Mar 06, 2026
Multimodal Imaging: Integrating Techniques for Comprehensive Diagnostics

The anatomical, physiological, biochemical, and genetic data provided by today's medical imaging technology should aid in precise illness diagnosis, treatment response prediction, and the creation of extremely sensitive and targeted medications and imaging agents. Nevertheless, comprehensive medical imaging is not provided by any of the existing human imaging techniques. Multimodality imaging has emerged as a compelling approach for a way to use the advantages of various imaging modalities. Combining radio, nuclear, and optical techniques is... see more

Impact Factor: 1.1
Guest Editor(s): Dr. Norita Md Norwawi
Co-Guest Editor(s): Dr. Zahriddin Muminov,Dr. Fatih Gokce
Start Submission
Submission closes on: Jan 24, 2026
Next-Generation Healthcare and Secure Medical Imaging: Advances in Authentication, Privacy, and Data Management

Background and Motivation: The rapid digitization of healthcare systems has driven an unprecedented reliance on Medical Imaging (MI) technologies such as X-rays, Ultrasound, Computed Tomography (CT), and Medical Resonance Imaging (MRI) for accurate diagnosis, treatment planning, and disease monitoring. With the proliferation of telemedicine and Internet of Medical Things (IoMT) devices, medical images are increasingly transmitted, analyzed, and stored across interconnected networks. While this shift enables real-time collaboration and improved patient outcomes, it also raises... see more

Impact Factor: 1.1
Guest Editor(s): Dr. Sushil Kumar Singh
Co-Guest Editor(s): Dr. Manish Kumar,Dr. Rajendrasinh Jadeja
Start Submission
Submission closes on: Jan 24, 2026
Deep Learning for 3D Medical Image Reconstruction in Surgical Assistance

Deep learning, a subset of machine learning, utilizes artificial neural networks to analyze complex data, drawing inspiration from the human brain. In medical imaging, deep learning is pivotal for tasks like diagnosing diseases, classifying lesions, and predicting cancer risks. Techniques like convolutional neural networks (CNNs) enhance precision in medical image processing, overcoming challenges such as inconsistent lighting, low resolution, and radiation risks in imaging modalities like X-rays, CT scans, and MRIs. Deep learning models can... see more

Impact Factor: 1.1
Guest Editor(s): Dr. Riza Sulaiman
Co-Guest Editor(s): Dr. Bilkisu Larai Muhammad Bello,Dr.Bibhu Kalyan Mishra
Start Submission
Submission closes on: Jan 08, 2026
Distributed Edge Networks: Federated Learning for Safe and Effective Medical Image Diagnosis

The cooperative and decentralized technique of federated learning removes the need for local data exchange. Instead, it is used for local model training, which sends only the model parameters to a central server. By building an objective global model that conforms to local models while maintaining user privacy, Federated Learning (FL), a distributed machine learning model, preserves user privacy. In order to improve the efficiency and privacy of heterogeneous information in public health records and... see more

Impact Factor: 1.1
Guest Editor(s): Dr. Samir Ghouali
Co-Guest Editor(s): Dr. Mokhtar Besseghier,Dr. Bipin Thapa Magar,Dr. Roseline Oluwaseun Ogundokun
Start Submission
Submission closes on: Jan 03, 2026
Multimedia and Image/Video Processing for Advanced Medical Education and Training

In the scope of developed medical education and training, multimedia and image or video processing plays an important role in improving learning experiences, enhancing practical skills and understanding. Legacy methods of medical education often trusted on textbooks, lectures and hands-on experiences, but the incorporation of multimedia and image/video processing provides powerful and interactive tools to increase these approaches. Intricate anatomical structures and physiological processes can be understood with the help of interactive visualizations and simulations.... see more

Impact Factor: 1.1
Guest Editor(s): Dr. Muhammad Munim Ahmad Zabidi
Co-Guest Editor(s): Dr. Zaid Omar,Dr. Stanley Adiele Okolie
Start Submission