We examine the transformative potential of artificial intelligence (AI) in medical imaging diagnosis, focusing on improving diagnostic accuracy and efficiency through advanced algorithms. It addresses the significant challenges preventing immediate clinical adoption of AI, specifically from technical, ethical, and legal perspectives. The aim is to highlight the current state of AI in medical imaging and outline the necessary steps to ensure safe, effective, and ethically sound clinical implementation. We conduct a comprehensive discussion, with special emphasis on the technical requirements for robust AI models, the ethical frameworks needed for responsible deployment, and the legal implications, including data privacy and regulatory compliance. Explainable artificial intelligence (XAI) is examined as a means to increase transparency and build trust among healthcare professionals and patients. The analysis reveals key challenges to AI integration in clinical settings, including the need for extensive high-quality datasets, model reliability, advanced infrastructure, and compliance with regulatory standards. The lack of explainability in AI outputs remains a barrier, with XAI identified as crucial for meeting transparency standards and enhancing trust among end users. Overcoming these barriers requires a collaborative, multidisciplinary approach to integrate AI into clinical practice responsibly. Addressing technical, ethical, and legal issues will support a softer transition, fostering a more accurate, efficient, and patient-centered healthcare system where AI augments traditional medical practices.

The study aimed to review the applicability and performance of various Convolutional Neural Network (CNN) models for the identification of periodontal bone loss (PBL) in digital periapical radiographs achieved through classification, detection, and segmentation approaches. We searched the PubMed, IEEE Xplore, and SCOPUS databases for articles published up to June 2024. After the selection process, a total of 11 studies were included in this review. The reviewed studies demonstrated that CNNs have a significant potential application for automatic identification of PBL on periapical radiographs through classification and segmentation approaches. CNN architectures can be utilized to classify the presence or absence of PBL, the severity or degree of PBL, and PBL area segmentation. CNN showed a promising performance for PBL identification on periapical radiographs. Future research should focus on dataset preparation, proper selection of CNN architecture, and robust performance evaluation to improve the model. Utilizing an optimized CNN architecture is expected to assist dentists by providing accurate and efficient identification of PBL.

The integration of artificial intelligence (AI) into clinical practice, particularly within radiology, nuclear medicine and radiation oncology, is transforming diagnostic and therapeutic processes. AI-driven tools, especially in deep learning and machine learning, have shown remarkable potential in enhancing image recognition, analysis and decision-making. This technological advancement allows for the automation of routine tasks, improved diagnostic accuracy, and the reduction of human error, leading to more efficient workflows. Moreover, the successful implementation of AI in healthcare requires comprehensive education and training for young clinicians, with a pressing need to incorporate AI into residency programmes, ensuring that future specialists are equipped with traditional skills and a deep understanding of AI technologies and their clinical applications. This includes knowledge of software, data analysis, imaging informatics and ethical considerations surrounding AI use in medicine. By fostering interdisciplinary integration and emphasising AI education, healthcare professionals can fully harness AI's potential to improve patient outcomes and advance the field of medical imaging and therapy. This review aims to evaluate how AI influences radiology, nuclear medicine and radiation oncology, while highlighting the necessity for specialised AI training in medical education to ensure its successful clinical integration.

Computed tomography perfusion (CTP) represents one of the main determinants in the decision-making strategy of stroke patients, being very useful in triaging these patients. The aim of this review is to describe the current knowledge and the future applications of AI in CTP. This review contains a short technical description of the CTP technique and how perfusion parameters are currently estimated and applied in clinical practice. We then provided a comprehensive literature review on the performance of CTP analysis software aimed at understanding whether possible differences between commercially available software might have a direct implication on neuroradiological patient stratification, and therefore on their clinical outcomes. An overview of past, present, and future of software used for CTP estimation, with an emphasis on those AI-based, is provided. Finally, future challenges regarding technical aspects and ethical considerations are discussed. In the current state, most of the use of AI in CTP estimation is limited to some technical steps of the processing pipeline, and especially in the correction of motion artifacts, with deconvolution methods that are still widely used to generate CTP-derived variables. Major drawbacks in AI implementation are still present, especially regarding the "black-box" nature of some models, technical workflow implementations, and the economic costs. In the future, the integration of AI with all the information available in clinical practice should fulfill the aim of developing patient-specific CTP maps, which will overcome the current limitations of threshold-based decision-making processes and will lead physicians to better patient selection and earlier and more efficient treatments. KEY POINTS: Question AI is a widely investigated field in neuroradiology, yet no comprehensive review is yet available on its role in CT perfusion (CTP) in stroke patients. Findings AI in CTP is mainly used for motion correction; future integration with clinical data could enable personalized stroke treatment, despite ethical and economic challenges. Clinical relevance To date, AI in CTP mainly finds applications in image motion correction; although some ethical, technical, and vendor standardization issues remain, integrating AI with clinical data in stroke patients promises a possible improvement in patient outcomes.

Cardiac sarcoidosis is a form of inflammatory cardiomyopathy that varies in its clinical presentation. It is associated with significant clinical complications such as high-degree atrioventricular block, ventricular tachycardia, heart failure and sudden cardiac death. It is challenging to diagnose clinically, and its increasing detection rate may represent increasing awareness of the disease by clinicians as well as a rising incidence. Prompt diagnosis and risk stratification reduces morbidity and mortality from cardiac sarcoidosis. Noninvasive diagnostic modalities such as ECG, echocardiography, PET/computed tomography (PET/CT) and cardiac MRI (cMRI) are increasingly playing important roles in cardiac sarcoidosis diagnosis. Artificial intelligence driven applications are increasingly being applied to these diagnostic modalities to improve the detection of cardiac sarcoidosis. Review of the recent literature suggests artificial intelligence based algorithms in PET/CT and cMRIs can predict cardiac sarcoidosis as accurately as trained experts, however, there are few published studies on artificial intelligence based algorithms in ECG and echocardiography. The impressive advances in artificial intelligence have the potential to transform patient screening in cardiac sarcoidosis, aid prompt diagnosis and appropriate risk stratification and change clinical practice.

In recent years, hip arthroscopy has made great progress and has been extended to the treatment of intra-articular or periarticular diseases. However, the complex structure of the hip joint, high technical operation requirements and relatively long learning curve have hindered the popularization and development of hip arthroscopy in China. Therefore, on the one hand, it is necessary to promote the research and training of standardized techniques for the diagnosis of hip disease and the treatment of arthroscopic surgery, so as to improve the safety, effectiveness and popularization of the technology. On the other hand, our organization proactively leverages cutting-edge digitalization and intelligentization technologies, including medical image digitalization, medical big data analytics, artificial intelligence, surgical navigation and robotic control, virtual reality, telemedicine, and 5G communication technology. We conduct a range of innovative research and development initiatives such as intelligent-assisted diagnosis of hip diseases, digital preoperative planning, surgical intelligent navigation and robotic procedures, and smart rehabilitation solutions. These efforts aim to facilitate a digitalization and intelligentization leap in technology and continuously enhance the precision of diagnosis and treatment. In conclusion, standardization promotes the homogenization of diagnosis and treatment, while digitalization and intelligentization facilitate the precision of operations. The synergy of the two lays the foundation for personalized diagnosis and treatment and continuous innovation, ultimately driving the rapid development of hip arthroscopy technology.

Biomedical image classification is of paramount importance in enhancing diagnostic precision and improving patient outcomes across diverse medical disciplines. In recent years, the advent of deep learning methodologies has significantly transformed this domain by facilitating notable advancements in image analysis and classification endeavors. This paper provides a thorough overview of the application of deep learning techniques in biomedical image classification, encompassing various types of healthcare data, including medical images derived from modalities such as mammography, histopathology, and radiology. A detailed discourse on deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and advanced models such as generative adversarial networks (GANs), is presented. Additionally, we delineate the distinctions between supervised, unsupervised, and reinforcement learning approaches, along with their respective roles within the context of biomedical imaging. This study systematically investigates 50 deep learning methodologies employed in the healthcare sector, elucidating their effectiveness in various tasks, including disease detection, image segmentation, and classification. It particularly emphasizes models that have been trained on publicly available datasets, thereby highlighting the significant role of open-access data in fostering advancements in AI-driven healthcare innovations. Furthermore, this review accentuates the transformative potential of deep learning in the realm of biomedical image analysis and delineates potential avenues for future research within this rapidly evolving field.

This article reviews advancements in fully automated abdominal CT interpretation over the past decade, with a focus on automated image analysis techniques such as quantitative analysis, computer-aided detection, and disease classification. For each abdominal organ, we review segmentation techniques, assess clinical applications and performance, and explore methods for detecting/classifying associated pathologies. We also highlight cutting-edge AI developments, including foundation models, large language models, and multimodal image analysis. While challenges remain in integrating AI into radiology practice, recent progress underscores its growing potential to streamline workflows, reduce radiologist burnout, and enhance patient care.

Foundation models (FMs) represent a significant evolution in artificial intelligence (AI), impacting diverse fields. Within radiology, this evolution offers greater adaptability, multimodal integration, and improved generalizability compared with traditional narrow AI. Utilizing large-scale pre-training and efficient fine-tuning, FMs can support diverse applications, including image interpretation, report generation, integrative diagnostics combining imaging with clinical/laboratory data, and synthetic data creation, holding significant promise for advancements in precision medicine. However, clinical translation of FMs faces several substantial challenges. Key concerns include the inherent opacity of model decision-making processes, environmental and social sustainability issues, risks to data privacy, complex ethical considerations, such as bias and fairness, and navigating the uncertainty of regulatory frameworks. Moreover, rigorous validation is essential to address inherent stochasticity and the risk of hallucination. This international collaborative effort provides a comprehensive overview of the fundamentals, applications, opportunities, challenges, and prospects of FMs, aiming to guide their responsible and effective adoption in radiology and healthcare.

Idiopathic inflammatory myopathies (IIM) are a group of autoimmune rheumatic diseases characterized by proximal muscle weakness and extra muscular manifestations. Since 1975, these IIM have been classified into different clinical phenotypes. Each clinical phenotype is associated with a better or worse prognosis and a particular physiopathology. Machine learning (ML) is a fascinating field of knowledge with worldwide applications in different fields. In IIM, ML is an emerging tool assessed in very specific clinical contexts as a complementary tool for research purposes, including transcriptome profiles in muscle biopsies, differential diagnosis using magnetic resonance imaging (MRI), and ultrasound (US). With the cancer-associated risk and predisposing factors for interstitial lung disease (ILD) development, this systematic review evaluates 23 original studies using supervised learning models, including logistic regression (LR), random forest (RF), support vector machines (SVM), and convolutional neural networks (CNN), with performance assessed primarily through the area under the curve coupled with the receiver operating characteristic (AUC-ROC).