Modern medical imaging technologies have greatly advanced neuroscience research and clinical diagnostics. However, imaging data collected across different scanners, acquisition protocols, or imaging sites often exhibit substantial heterogeneity, known as "batch effects" or "site effects". These non-biological sources of variability can obscure true biological signals, reduce reproducibility and statistical power, and severely impair the generalizability of learning-based models across datasets. Image harmonization aims to eliminate or mitigate such site-related biases while preserving meaningful biological information, thereby improving data comparability and consistency. This review provides a comprehensive overview of key concepts, methodological advances, publicly available datasets, current challenges, and future directions in the field of medical image harmonization, with a focus on magnetic resonance imaging (MRI). We systematically cover the full imaging pipeline, and categorize harmonization approaches into prospective acquisition and reconstruction strategies, retrospective image-level and feature-level methods, and traveling-subject-based techniques. Rather than providing an exhaustive survey, we focus on representative methods, with particular emphasis on deep learning-based approaches. Finally, we summarize the major challenges that remain and outline promising avenues for future research.

MRI is an indispensable clinical tool, offering a rich variety of tissue contrasts to support broad diagnostic and research applications. Clinical exams routinely acquire multiple structural sequences that provide complementary information for differential diagnosis, while research protocols often incorporate advanced functional, diffusion, spectroscopic, and relaxometry sequences to capture multidimensional insights into tissue structure and composition. However, these capabilities come at the cost of prolonged scan times, which reduce patient throughput, increase susceptibility to motion artifacts, and may require trade-offs in image quality or diagnostic scope. Over the last two decades, advances in image reconstruction algorithms--alongside improvements in hardware and pulse sequence design--have made it possible to accelerate acquisitions while preserving diagnostic quality. Central to this progress is the ability to incorporate prior information to regularize the solutions to the reconstruction problem. In this tutorial, we overview the basics of MRI reconstruction and highlight state-of-the-art approaches, beginning with classical methods that rely on explicit hand-crafted priors, and then turning to deep learning methods that leverage a combination of learned and crafted priors to further push the performance envelope. We also explore the translational aspects and eventual clinical implications of these methods. We conclude by discussing future directions to address remaining challenges in MRI reconstruction. The tutorial is accompanied by a Python toolbox (https://github.com/tutorial-MRI-recon/tutorial) to demonstrate select methods discussed in the article.

Pulmonary nodules are critical indicators for the early detection of lung cancer; however, their diagnosis and management pose significant challenges due to the variability in nodule characteristics, reader fatigue, and limited clinical expertise, often leading to diagnostic errors. The rapid advancement of artificial intelligence (AI) presents promising solutions to address these issues. This review compares traditional rule-based methods, handcrafted feature-based machine learning, radiomics, deep learning, and hybrid models incorporating Transformers or attention mechanisms. It systematically compares their methodologies, clinical applications (diagnosis, treatment, prognosis), and dataset usage to evaluate performance, applicability, and limitations in pulmonary nodule management. AI advances have significantly improved pulmonary nodule management, with transformer-based models achieving leading accuracy in segmentation, classification, and subtyping. The fusion of multimodal imaging CT, PET, and MRI further enhances diagnostic precision. Additionally, AI aids treatment planning and prognosis prediction by integrating radiomics with clinical data. Despite these advances, challenges remain, including domain shift, high computational demands, limited interpretability, and variability across multi-center datasets. Artificial intelligence (AI) has transformative potential in improving the diagnosis and treatment of lung nodules, especially in improving the accuracy of lung cancer treatment and patient prognosis, where significant progress has been made.

Hepatocellular carcinoma (HCC) is the most common subtype of primary liver cancer, with significant variability in patient outcomes even within the same stage according to the Barcelona Clinic Liver Cancer staging system. Accurately predicting patient prognosis and potential treatment response prior to therapy initiation is crucial for personalized clinical decision-making. This review focuses on the application of artificial intelligence (AI) in magnetic resonance imaging for guiding individualized treatment strategies in HCC management. Specifically, we emphasize AI-based tools for pre-treatment prediction of therapeutic response and prognosis. AI techniques such as radiomics and deep learning have shown strong potential in extracting high-dimensional imaging features to characterize tumors and liver parenchyma, predict treatment outcomes, and support prognostic stratification. These advances contribute to more individualized and precise treatment planning. However, challenges remain in model generalizability, interpretability, and clinical integration, highlighting the need for standardized imaging datasets and multi-omics fusion to fully realize the potential of AI in personalized HCC care. Evidence level: 5. Technical efficacy: 4.

Medical radiology reports play a crucial role in diagnosing various diseases, yet generating them manually is time-consuming and burdens clinical workflows. Medical radiology report generation aims to automate this process using deep learning to assist radiologists and reduce patient wait times. This study presents the most comprehensive systematic review to date on deep learning-based MRRG, encompassing recent advances that span traditional architectures to large language models. We focus on available datasets, modeling approaches, and evaluation practices. Following PRISMA guidelines, we retrieved 323 articles from major academic databases and included 78 studies after eligibility screening. We critically analyze key components such as model architectures, loss functions, datasets, evaluation metrics, and optimizers - identifying 22 widely used datasets, 14 evaluation metrics, around 20 loss functions, over 25 visual backbones, and more than 30 textual backbones. To support reproducibility and accelerate future research, we also compile links to modern models, toolkits, and pretrained resources. Our findings provide technical insights and outline future directions to address current limitations, promoting collaboration at the intersection of medical imaging, natural language processing, and deep learning to advance trustworthy AI systems in radiology.

Magnetic resonance imaging (MRI) has become a pivotal non-invasive tool in the evaluation and management of lymphedema. This review systematically summarizes its current applications, highlighting imaging techniques, comparative advantages over other modalities, MRI-based staging systems, and emerging clinical roles. A comprehensive literature review was conducted, covering comparisons with lymphoscintigraphy, ultrasound, and computed tomography (CT), as well as studies on the feasibility of multiparametric MRI sequences. Compared to conventional imaging, MRI offers superior soft tissue contrast and enables detailed assessment of lymphatic anatomy, tissue composition, and fluid distribution through sequences such as T2-weighted imaging, diffusion-weighted imaging (DWI), and magnetic resonance lymphangiography (MRL). Standardized grading systems have been proposed to support clinical staging. MRI is increasingly applied in preoperative planning and postoperative surveillance.These findings underscore MRI's diagnostic precision and clinical utility. Future research should focus on protocol standardization, incorporation of quantitative biomarkers, and development of AI-driven tools to enable personalized, scalable lymphedema care.

Magnetic resonance imaging (MRI) is a non-invasive method widely used to evaluate abnormal tissues, especially in the brain. While many studies have examined brain tumor classification using MRI, a comprehensive scientometric analysis remains limited. This study aimed to investigate brain tumor classification based on magnetic resonance imaging (MRI), using scientometric approaches, from 2015 to 2024. A total of 348 peer-reviewed articles were extracted from the Scopus database. Tools such as CiteSpace and VOSviewer were employed to analyze key metrics, including citation frequency, author collaboration, and publication trends. The analysis revealed top authors, top-cited journals, and international collaborations. Co-occurrence networks identified the top research topics and bibliometric coupling revealed knowledge advancements in the domain. Deep learning methods are increasingly used in brain tumor classification research. This study outlines the current trends, uncovers research gaps, and suggests future directions for researchers in the domain of MRI-based brain tumor classification.

This study aimed to systematically map the development methods, scope, and limitations of existing artificial intelligence (AI) reporting guidelines in medicine and to explore their applicability to generative AI (GAI) tools, such as large language models (LLMs). We reported a scoping review adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). Five information sources were searched, including MEDLINE (via PubMed), EQUATOR Network, CNKI, FAIRsharing, and Google Scholar, from inception to December 31, 2024. Two reviewers independently screened records and extracted data using a predefined Excel template. Data included guideline characteristics (e.g., development methods, target audience, AI domain), adherence to EQUATOR Network recommendations, and consensus methodologies. Discrepancies were resolved by a third reviewer. 68 AI reporting guidelines were included. 48.5% focused on general AI, while only 7.4% addressed GAI/LLMs. Methodological rigor was limited: 39.7% described development processes, 42.6% involved multidisciplinary experts, and 33.8% followed EQUATOR recommendations. Significant overlap existed, particularly in medical imaging (20.6% of guidelines). GAI-specific guidelines (14.7%) lacked comprehensive coverage and methodological transparency. Existing AI reporting guidelines in medicine have suboptimal methodological rigor, redundancy, and insufficient coverage of GAI applications. Future and updated guidelines should prioritize standardized development processes, multidisciplinary collaboration, and expanded focus on emerging AI technologies like LLMs.

The systematic literature review was performed on the use of artificial intelligence (AI) algorithms in nonsmall cell lung cancer (NSCLC) prognostication. Studies were evaluated for the type of input data (histology and whether CT, PET, and MRI were used), cancer therapy intervention, prognosis performance, and comparisons to clinical prognosis systems such as TNM staging. Further comparisons were drawn between different types of AI, such as machine learning (ML) and deep learning (DL). Syntheses of therapeutic interventions and algorithm input modalities were performed for comparison purposes. The review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The initial database identified 3880 results, which were reduced to 513 after the automatic screening, and 309 after the exclusion criteria. The prognostic performance of AI for NSCLC has been investigated using histology and genetic data, and CT, PET, and MR imaging for surgery, immunotherapy, and radiation therapy patients with and without chemotherapy. Studies per therapy intervention were 13 for immunotherapy, 10 for radiotherapy, 14 for surgery, and 34 for other, multiple, or no specific therapy. The results of this systematic review demonstrate that AI-based prognostication methods consistently present higher prognostic performance for NSCLC, especially when directly compared with traditional prognostication techniques such as TNM staging. The use of DL outperforms ML-based prognostication techniques. DL-based prognostication demonstrates the potential for personalized precision cancer therapy as a supplementary decision-making tool. Before it is fully utilized in clinical practice, it is recommended that it be thoroughly validated through well-designed clinical trials.

Chronological age, although commonly used in clinical practice, fails to capture individual variations in rates of ageing and physiological decline. Recent advances in artificial intelligence (AI) have transformed the estimation of biological age using various imaging techniques. This Review consolidates AI developments in age prediction across brain, chest, abdominal, bone, and facial imaging using diverse methods, including MRI, CT, x-ray, and photographs. The difference between predicted and chronological age-often referred to as age deviation-is a promising biomarker for assessing health status and predicting disease risk. In this Review, we highlight consistent associations between age deviation and various health outcomes, including mortality risk, cognitive decline, and cardiovascular prognosis. We also discuss the technical challenges in developing unbiased models and ethical considerations for clinical application. This Review highlights the potential of AI-based age estimation in personalised medicine as it offers a non-invasive, interpretable biomarker that could transform health risk assessment and guide preventive interventions.