Artificial intelligence (AI) is already having a significant impact on healthcare. For example, AI-guided imaging can improve the diagnosis/treatment of vascular diseases, which affect over 200 million people globally. Recently, Chiu and colleagues (2024) developed an AI algorithm that supports nurses with no ultrasound training in diagnosing abdominal aortic aneurysms (AAA) with similar accuracy as ultrasound-trained physicians. This technology can therefore improve AAA screening; however, achieving clinical impact with new AI technologies requires careful consideration of commercialization strategies, including funding, compliance with safety and regulatory frameworks, health technology assessment, regulatory approval, reimbursement, and clinical guideline integration.

This study sought to develop a radiomics model capable of predicting axillary lymph node metastasis (ALNM) in patients with invasive breast cancer (IBC) based on dual-sequence magnetic resonance imaging(MRI) of diffusion-weighted imaging (DWI) and dynamic contrast enhancement (DCE) data. The interpretability of the resultant model was probed with the SHAP (Shapley Additive Explanations) method. Established inclusion/exclusion criteria were used to retrospectively compile MRI and matching clinical data from 183 patients with pathologically confirmed IBC from our hospital evaluated between June 2021 and December 2023. All of these patients had undergone plain and enhanced MRI scans prior to treatment. These patients were separated according to their pathological biopsy results into those with ALNM (n = 107) and those without ALNM (n = 76). These patients were then randomized into training (n = 128) and testing (n = 55) cohorts at a 7:3 ratio. Optimal radiomics features were selected from the extracted data. The random forest method was used to establish three predictive models (DWI, DCE, and combined DWI + DCE sequence models). Area under the curve (AUC) values for receiver operating characteristic (ROC) curves were utilized to assess model performance. The DeLong test was utilized to compare model predictive efficacy. Model discrimination was assessed based on the integrated discrimination improvement (IDI) method. Decision curves revealed net clinical benefits for each of these models. The SHAP method was used to achieve the best model interpretability. Clinicopathological characteristics (age, menopausal status, molecular subtypes, and estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and Ki-67 status) were comparable when comparing the ALNM and non-ALNM groups as well as the training and testing cohorts (P > 0.05). AUC values for the DWI, DCE, and combined models in the training cohort were 0.793, 0.774, and 0.864, respectively, with corresponding values of 0.728, 0.760, and 0.859 in the testing cohort. The predictive efficacy of the DWI and combined models was found to differ significantly according to the DeLong test, as did the predictive efficacy of the DCE and combined models in the training groups (P < 0.05), while no other significant differences were noted in model performance (P > 0.05). IDI results indicated that the combined model offered predictive power levels that were 13.5% (P < 0.05) and 10.2% (P < 0.05) higher than those for the respective DWI and DCE models. In a decision curve analysis, the combined model offered a net clinical benefit over the DCE model. The combined dual-sequence MRI-based radiomics model constructed herein and the supporting interpretability analyses can aid in the prediction of the ALNM status of IBC patients, helping to guide clinical decision-making in these cases.

Large language models (LLMs) hold great promise for optimizing and supporting radiology workflows amidst rising workloads. This review examines potential applications in daily radiology practice, as well as remaining challenges and potential solutions.Presentation of potential applications and challenges, illustrated with practical examples and concrete optimization suggestions.LLM-based assistance systems have potential applications in almost all language-based process steps of the radiological workflow. Significant progress has been made in areas such as report generation, particularly with retrieval-augmented generation (RAG) and multi-step reasoning approaches. However, challenges related to hallucinations, reproducibility, and data protection, as well as ethical concerns, need to be addressed before widespread implementation.LLMs have immense potential in radiology, particularly for supporting language-based process steps, with technological advances such as RAG and cloud-based approaches potentially accelerating clinical implementation. · LLMs can optimize reporting and other language-based processes in radiology with technologies such as RAG and multi-step reasoning approaches.. · Challenges such as hallucinations, reproducibility, privacy, and ethical concerns must be addressed before widespread adoption.. · RAG and cloud-based approaches could help overcome these challenges and advance the clinical implementation of LLMs.. · Fink A, Rau S, Kästingschäfer K et al. From Referral to Reporting: The Potential of Large Language Models in the Radiological Workflow. Rofo 2025; DOI 10.1055/a-2641-3059.

Major depressive disorder (MDD) is a highly prevalent mental health condition with significant public health implications. Early detection is crucial for timely intervention, but current diagnostic methods often rely on subjective clinical assessments, leading to delayed or inaccurate diagnoses. Advances in neuroimaging and machine learning (ML) offer the potential for objective and accurate early detection. This study aimed to develop and validate ML models using multisite functional magnetic resonance imaging data for the early detection of MDD, compare their performance, and evaluate their clinical applicability. We used functional magnetic resonance imaging data from 1200 participants (600 with early-stage MDD and 600 healthy controls) across 3 public datasets. In total, 4 ML models-support vector machine, random forest, gradient boosting machine, and deep neural network-were trained and evaluated using a 5-fold cross-validation framework. Models were assessed for accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve. Shapley additive explanations values and activation maximization techniques were applied to interpret model predictions. The deep neural network model demonstrated superior performance with an accuracy of 89% (95% CI 86%-92%) and an area under the receiver operating characteristic curve of 0.95 (95% CI 0.93-0.97), outperforming traditional diagnostic methods by 15% (P<.001). Key predictive features included altered functional connectivity between the dorsolateral prefrontal cortex, anterior cingulate cortex, and limbic regions. The model achieved 78% sensitivity (95% CI 71%-85%) in identifying individuals who developed MDD within a 2-year follow-up period, demonstrating good generalizability across datasets. Our findings highlight the potential of artificial intelligence-driven approaches for the early detection of MDD, with implications for improving early intervention strategies. While promising, these tools should complement rather than replace clinical expertise, with careful consideration of ethical implications such as patient privacy and model biases.

To improve precision medicine in breast cancer (BC) decision-making, radio-genomics is an emerging branch of artificial intelligence (AI) that links cancer characteristics assessed radiologically with the histopathology and genomic properties of the tumour. By employing MRIs, mammograms, and ultrasounds to uncover distinctive radiomics traits that potentially predict genomic abnormalities, this review attempts to find literature that links AI-based models with the genetic mutations discovered in BC patients. The review's findings can be used to create AI-based population models for low and middle-income countries (LMIC) and evaluate how well they predict outcomes for our cohort.Magnetic resonance imaging (MRI) appears to be the modality employed most frequently to research radio-genomics in BC patients in our systemic analysis. According to the papers we analysed, genetic markers and mutations linked to imaging traits, such as tumour size, shape, enhancing patterns, as well as clinical outcomes of treatment response, disease progression, and survival, can be identified by employing AI. The use of radio-genomics can help LMICs get through some of the barriers that keep the general population from having access to high-quality cancer care, thereby improving the health outcomes for BC patients in these regions. It is imperative to ensure that emerging technologies are used responsibly, in a way that is accessible to and affordable for all patients, regardless of their socio-economic condition.

Lymphomas are a diverse group of disorders characterized by the clonal proliferation of lymphocytes. While definitive diagnosis of lymphoma relies on histopathology, immune-phenotyping and additional molecular analyses, imaging modalities such as PET/CT, CT, and MRI play a central role in the diagnostic process and management, from assessing disease extent, to evaluation of response to therapy and detecting recurrence. Artificial intelligence (AI), particularly deep learning models like convolutional neural networks (CNNs), is transforming lymphoma imaging by enabling automated detection, segmentation, and classification. This review elaborates on recent advancements in deep learning for lymphoma imaging and its integration into clinical practice. Challenges include obtaining high-quality, annotated datasets, addressing biases in training data, and ensuring consistent model performance. Ongoing efforts are focused on enhancing model interpretability, incorporating diverse patient populations to improve generalizability, and ensuring safe and effective integration of AI into clinical workflows, with the goal of improving patient outcomes.

This review paper comprehensively summarizes healthcare provider (HCP) evaluation of explanations produced by explainable artificial intelligence methods to support point-of-care, patient-specific, clinical decision-making (CDM) within medical settings. It highlights the critical need to incorporate human-centered (HCP) evaluation approaches based on their CDM needs, processes, and goals. The review was conducted in Ovid Medline and Scopus databases, following the Institute of Medicine's methodological standards and PRISMA guidelines. An individual study appraisal was conducted using design-specific appraisal tools. MaxQDA software was used for data extraction and evidence table procedures. Of the 2673 unique records retrieved, 25 records were included in the final sample. Studies were excluded if they did not meet this review's definitions of HCP evaluation (1156), healthcare use (995), explainable AI (211), and primary research (285), and if they were not available in English (1). The sample focused primarily on physicians and diagnostic imaging use cases and revealed wide-ranging evaluation measures. The synthesis of sampled studies suggests a potential common measure of clinical explainability with 3 indicators of interpretability, fidelity, and clinical value. There is an opportunity to extend the current model-centered evaluation approaches to incorporate human-centered metrics, supporting the transition into practice. Future research should aim to clarify and expand key concepts in HCP evaluation, propose a comprehensive evaluation model positioned in current theoretical knowledge, and develop a valid instrument to support comparisons.

Severity scores, which often refer to the likelihood or probability of a pathology, are commonly provided by artificial intelligence (AI) tools in radiology. However, little attention has been given to the use of these AI scores, and there is a lack of transparency into how they are generated. In this comment, we draw on key principles from psychological science and statistics to elucidate six human factors limitations of AI scores that undermine their utility: (1) variability across AI systems; (2) variability within AI systems; (3) variability between radiologists; (4) variability within radiologists; (5) unknown distribution of AI scores; and (6) perceptual challenges. We hypothesize that these limitations can be mitigated by providing the false discovery rate and false omission rate for each score as a threshold. We discuss how this hypothesis could be empirically tested. KEY POINTS: The radiologist-AI interaction has not been given sufficient attention. The utility of AI scores is limited by six key human factors limitations. We propose a hypothesis for how to mitigate these limitations by using false discovery rate and false omission rate.

Diabetic retinopathy (DR) is a leading cause of vision loss in working-age adults. While screening reduces the risk of blindness, traditional imaging is often costly and inaccessible. Artificial intelligence (AI) algorithms present a scalable diagnostic solution, but concerns regarding fairness and generalization persist. This work evaluates the fairness and performance of image-trained models in DR prediction, as well as the impact of disentanglement as a bias mitigation technique, using the diverse mBRSET fundus dataset. Three models, ConvNeXt V2, DINOv2, and Swin V2, were trained on macula images to predict DR and sensitive attributes (SAs) (e.g., age and gender/sex). Fairness was assessed between subgroups of SAs, and disentanglement was applied to reduce bias. All models achieved high DR prediction performance in diagnosing (up to 94% AUROC) and could reasonably predict age and gender/sex (91% and 77% AUROC, respectively). Fairness assessment suggests disparities, such as a 10% AUROC gap between age groups in DINOv2. Disentangling SAs from DR prediction had varying results, depending on the model selected. Disentanglement improved DINOv2 performance (2% AUROC gain), but led to performance drops in ConvNeXt V2 and Swin V2 (7% and 3%, respectively). These findings highlight the complexity of disentangling fine-grained features in fundus imaging and emphasize the importance of fairness in medical imaging AI to ensure equitable and reliable healthcare solutions.

Artificial intelligence has become an impressive force manifesting itself in the radiology field, improving workflows, and influencing clinical decision-making. With this increasing presence, a closer look at how residents can be properly exposed to this technology is needed. Within this paper, we aim to discuss the three pillars central to a trainee's experience including education on AI, AI-Education tools, and clinical implementation of AI. An already overcrowded clinical residency curricula makes little room for a thorough AI education; the challenge of which may be overcome through longitudinal distinct educational tracks during residency or external courses offered through a variety of societies. In addition to teaching the fundamentals of AI, programs which offer education tools utilizing AI will improve on antiquated clinical curricula. These education tools are a growing field in research and industry offering a variety of unique opportunities to promote active inquiry, improved comprehension and overall clinical competence. The near 700 FDA-approved AI clinical tools almost guarantees that residents will be exposed to this technology which may have mixed effects on education, although more research needs to be done to further elucidate this challenge. Ethical considerations, including algorithmic bias, liability, and post-deployment monitoring, highlight the need for structured instruction and mentorship. As AI continues to evolve, residency programs must prioritize evidence-based, adaptable curricula to prepare future radiologists to critically assess, utilize, and contribute to AI advancements, ensuring that these tools complement rather than undermine clinical expertise.