AI in Medical Imaging: From Algorithms to Clinical Practice

Dear Colleagues,

Following the success of the first edition of our Special Issue—featuring over 10 published contributions—we are pleased to announce the second edition of "AI in Medical Imaging: From Algorithms to Clinical Practice".

[First Edition: https://www.mdpi.com/journal/technologies/special_issues/6J0JKQGN6L]

This Special Issue highlights the transformative potential of artificial intelligence (AI), data science, and digital technologies in advancing healthcare and clinical decision-making. We invite researchers, clinicians, and industry partners to contribute to this dynamic and rapidly evolving field.

Recent advances in AI have demonstrated remarkable potential not only in medical imaging, but also in the broader analysis of healthcare data, clinical workflows, and patient outcomes. However, the successful translation of these technologies into clinical practice requires rigorous validation using real-world clinical data and a clear understanding of their practical value in healthcare settings.

This Special Issue aims to provide a platform for innovative studies that bridge the gap between technological development and clinical implementation. We particularly welcome contributions that evaluate clinical hypotheses using real-world data, including retrospective and prospective studies, pilot investigations, and proof-of-concept analyses. While large-scale studies are encouraged, well-designed small-scale clinical studies that provide meaningful insights into real-world healthcare challenges are also highly valued.

By bringing together researchers from academia, healthcare institutions, and industry, we hope to foster interdisciplinary collaboration and accelerate the responsible translation of AI-driven innovations into clinical practice.

We invite researchers, clinicians, and technologists to contribute original research articles, clinical validation studies, real-world data investigations, methodological papers, or comprehensive review articles on topics including, but not limited to, the following:

• AI-driven diagnostic tools and predictive models in medical imaging and healthcare data: the development and application of artificial intelligence, including deep learning and machine learning, to enhance diagnostic accuracy and predictive capabilities in clinical practice.
• Radiomics and AI integration: combining radiomics with AI methodologies to extract quantitative imaging features and improve disease characterization, prognosis, and treatment response prediction.
• Automated image segmentation, medical image reconstruction, and healthcare data analysis: leveraging AI-driven algorithms for precise image analysis, advanced reconstruction techniques, and efficient utilization of clinical data to improve diagnostic reliability and patient care.
• Multi-modal data analysis for enhanced clinical decision-making: integrating information from medical imaging modalities (e.g., CT, MRI, US, PET) together with clinical, laboratory, physiological, and other healthcare data using AI to provide comprehensive insights and support clinical decision-making.
• Applications of deep learning and machine learning in medical imaging and healthcare data science: advancements in neural network architectures and their implementation in tasks such as classification, anomaly detection, feature extraction, and outcome prediction.
• Real-world data (RWD) science in AI-driven healthcare: harnessing real-world imaging and clinical datasets to develop, validate, and implement AI models while addressing challenges such as data heterogeneity, generalizability, and clinical applicability.
• Explainable and trustworthy AI, validation, and standardization of AI technologies: advancing transparency, interpretability, fairness, reproducibility, and clinical relevance in AI model development and deployment, while addressing regulatory, ethical, and implementation challenges in healthcare.

The scope of this Special Issue reflects the diversity and complexity of AI applications in medical imaging and healthcare data science, encompassing advancements in deep learning, machine learning, data-driven clinical research, and AI-assisted decision support. It addresses critical challenges such as data quality, model interpretability, validation, reproducibility, and the translation of AI technologies into routine clinical practice.

Particular emphasis is placed on studies that leverage real-world clinical data to evaluate clinically meaningful hypotheses, validate emerging technologies, and demonstrate practical healthcare applications. Contributions involving medical imaging, electronic health records, laboratory data, physiological signals, and other multimodal healthcare datasets are all welcomed.

By highlighting innovations in quantitative imaging, radiomics, multimodal data integration, explainable AI, and real-world data science, this Special Issue aims to bridge the gap between methodological development and clinical implementation.

Through contributions from interdisciplinary fields, we aspire to present a holistic perspective on the current state and future prospects of AI-driven healthcare, fostering collaboration among researchers, clinicians, and technologists and accelerating the responsible translation of innovation into patient care.

We look forward to receiving your contributions and collaborating with you to make this Special Issue a valuable platform for advancing clinically relevant AI and healthcare data science.

Dr. Masateru Kawakubo
Prof. Dr. Tamás Haidegger
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies is an international peer-reviewed open access monthly 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 1800 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.

• artificial intelligence in healthcare
• real-world data science
• clinical implementation
• clinical validation
• explainable and trustworthy AI
• quantitative imaging and radiomics
• multimodal healthcare data analysis
• clinical decision support