Faghani, Shahriar

Foundation models, known as the large-scale, pretrained models capable of generalizing across diverse tasks, have significantly advanced the field of medical image analysis. While most early applications focused on 2D modalities, the unique challenges and opportunities associated with volumetric medical imaging have recently attracted growing interest. This study provides a comprehensive overview of the current landscape of foundation models tailored for volumetric medical image analysis, with a focus on CT, MRI, and PET imaging. We examine key components of these models, including 3D architectures, training strategies, and supported modalities. In addition, we highlight their contribution to major clinical tasks such as classification and prediction, segmentation, image registration, quality enhancement, and visual question answering. Critical challenges of these models, including high computational cost, limited and less diverse 3D datasets, and domain adaptation, are discussed alongside the promising solutions and future research directions. By synthesizing recent advances in volumetric foundation models and outlining key technical and clinical challenges, this review provides a thorough roadmap toward the development of scalable, generalizable, and clinically applicable AI systems for volumetric medical images.

Agentic artificial intelligence marks a shift from large language models to autonomous, goal-driven clinical assistants. The framework presented in this report outlines implementation in radiology workflows and addresses cybersecurity, bias, and accountability challenges.

Abstract INTRODUCTION Glioblastoma is the most common malignant adult brain tumor with poor prognosis, largely due to its heterogeneous landscape. The AI-RANO and RANO-RGP groups organized the BraTS-Pathology 2024 challenge, providing a publicly-available dataset and a benchmarking environment for AI models to automatically identify six clinically-relevant histopathologic sub-regions in H&E-stained whole slide images (WSIs). METHODS We reclassified the TCGA-GBM and TCGA-LGG data according to the WHO 2021 criteria and identified diagnostic WSIs of 188 glioblastoma (IDH-wt, CNS WHO Gr.4) patients. A well-defined annotation protocol was communicated to eight neuropathologists, across eight institutions, who created “ground truth” annotations for Cellular Tumor (CT), Geographic Necrosis, Cortical Infiltration, Pseudopalisading Necrosis (PN), Microvascular Proliferation, and Penetration into White Matter (PWM). Comprehensive WSI curation for elimination of artifactual content and follow up partitioning resulted in approximately 300,000 data samples/patches. FINDINGS Inter-observer variability assessment highlighted difficulties in establishing a “gold standard”. The least annotated sub-regions (PWM and PN) were the most challenging to the participating methods, whereas all methods achieved their highest accuracy in the most annotated sub-region (CT). The highest average performance across sub-regions was obtained by a foundation model-based method. However, this top-ranked solution achieved an F1 score of 0.51 (Accuracy=0.65, Sensitivity=0.56, Specificity=0.93). CONCLUSIONS Our findings suggest that achieving state-of-the-art performance, in automatically identifying clinically-relevant glioblastoma sub-regions, remains an unsolved research problem. Analysis of the ground-truth annotations underscores the critical need for more precisely defined and neuropathologically standardized annotation protocol. Current AI algorithms are facing difficulties in addressing glioblastoma’s heterogeneous histologic landscape in the absence of large, comprehensively, and consistently annotated data. Building upon these findings, and towards developing more accurate AI algorithms, the 2025 iteration of the BraTS-Path challenge is currently on-going by leveraging two million data samples, aiming to enhance our disease understanding and improve diagnostic accuracy, ultimately contributing to improved patient outcomes.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well- annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied environments and providing the tools for future development of personalized volumetric reports to patients undergoing BM treatment.

Purpose Spine-related pain disorders commonly require interventions such as spinal injections, which are often image-guided and increase in demand as populations age. This study investigates the impact of lifestyle changes following retirement on the incidence of these procedures, hypothesizing that retirement significantly influences the requirement for such treatments. Methods A review of medical records from XXX County between 2000 and 2023 was conducted, focusing on patients who received spinal injections, including epidural steroid, facet, and sacroiliac joint injections. Data were stratified by age, employment status pre- and post-procedure, and type of injection. Statistical analysis, including segmental linear regression and t-tests, was used to assess changes in incidence rates over time and across demographic subsets. Results Out of 134,318 patients analyzed, a total of 359,224 spinal injections were performed. There was a significant ( p -value <.01) increase in the frequency of procedures among individuals around the age of 60, which coincides with the typical retirement age in the United States. The study also highlighted a subsequent decline in the incidence of these procedures between the ages of 70 and 74, suggesting potential adaptation to chronic conditions or the effectiveness of earlier treatments. Conclusion The findings of this study confirm the hypothesis that retirement significantly impacts the demand for spinal injections, as evidenced by the marked increase in procedures at typical retirement ages. This underscores the importance of integrating preventive care and personalized medical guidance into healthcare strategies for individuals nearing retirement to effectively manage spine-related pain disorders and reduce the necessity for invasive treatments.

Large language models pose unique cybersecurity risks in health care, including vulnerability to malicious attacks and data breaches. This article equips professionals with strategies to mitigate these threats for safe use.

Artificial intelligence (AI)-based object detection in radiology can assist in clinical diagnosis and treatment planning. This article examines the AI-based object detection models currently used in many imaging modalities, including X-ray Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasound (US). The key models from the convolutional neural network (CNN) as well as the contemporary transformer and hybrid models are analyzed based on their ability to detect pathological features, such as tumors, lesions, and tissue abnormalities. In addition, this review offers a closer look at the strengths and weaknesses of these models in terms of accuracy, robustness, and speed in real clinical settings. The common issues related to these models, including limited data, annotation quality, and interpretability of AI decisions, are discussed in detail. Moreover, the need for strong applicable models across different populations and imaging modalities are addressed. The importance of privacy and ethics in general data use as well as safety and regulations for healthcare data are emphasized. The future potential of these models lies in their accessibility in low resource settings, usability in shared learning spaces while maintaining privacy, and improvement in diagnostic accuracy through multimodal learning. This review also highlights the importance of interdisciplinary collaboration among artificial intelligence researchers, radiologists, and policymakers. Such cooperation is essential to address current challenges and to fully realize the potential of AI-based object detection in radiology.