Singh, Yashbir

Graph neural networks (GNNs) are increasingly used for medical imaging analysis and biological data modeling, where the integration of radiomics, topology, geometry, and generative artificial intelligence (AI) may improve representation learning from medical images and related biomedical data. Across the reviewed literature, GNNs show particular value for modeling spatial relationships, multimodal interactions, graph-structured biological networks, and non-Euclidean imaging features that are difficult to capture using conventional convolutional architectures alone. Topology- and geometry-aware approaches further expand this capability by encoding multi-scale structure, higher-order relationships, curvature, geodesic organization, and equivariant spatial priors. Hybrid graph–transformer models and generative graph methods represent emerging directions for modeling long-range dependencies, augmenting scarce datasets, supporting synthetic pretraining, and improving representation learning in low-label or heterogeneous biomedical settings. However, clinical translation remains limited by variability in graph construction, limited external validation, computational cost, scalability constraints, interpretability challenges, and uncertainty regarding the biological realism of synthetic data. Overall, this review highlights that GNN-based medical imaging analysis is most likely to advance when graph construction is biologically justified, model performance is evaluated across diverse clinical cohorts, and technical gains are paired with transparent validation, interpretability, and implementation strategies.

Background/Objectives: Juxtapleural and perifissural nodules are commonly encountered on thoracic imaging, are typically presumed benign, and are often considered to represent intrapulmonary lymph nodes. We evaluated the prevalence, clinical context, and management of juxtapleural nodules over a 10-year period and to assess factors associated with concern for malignancy. Methods: In this retrospective, single-center study, radiology reports from 1 January 2015 to 31 December 2024, were queried using natural language processing to identify examinations containing terminology consistent with juxtapleural nodules. After exclusion of duplicate encounters, 85,435 unique patient encounters were analyzed. Reports were further filtered to identify cases with imaging concern for malignancy and recommendations for follow-up or intervention (N = 659). Clinical characteristics, imaging modality, and prior malignancy history were compared using appropriate statistical tests. Results: Among 85,435 unique encounters, 659 patients (<1%) had juxtapleural nodules considered concerning for malignancy. These patients were older (68 vs. 64 years, p < 0.001), more frequently imaged with PET/CT (8% vs. 5%, p < 0.001), and more likely to be evaluated in inpatient settings (16% vs. 12%, p < 0.001). Of the cohort with a juxtapleural nodule considered concerning for malignancy by imaging criteria, 360 patients (55%) had a prior history of malignancy. In these patients, nodules were more commonly identified in outpatient settings (70% vs. 43%, p < 0.001) and on routine imaging (94% vs. 86%, p < 0.001). Representative cases demonstrated that malignant nodules may mimic benign perifissural morphology on initial imaging. Conclusions: Although the vast majority of reported juxtapleural nodules did not prompt malignancy-directed evaluation, a small subset of cases (approximately 0.8%), particularly in patients with prior malignancy, were interpreted as sufficiently concerning to warrant follow-up imaging or intervention. Because pathologic confirmation and standardized long-term outcome adjudication were not available for all patients, these findings should be interpreted as reflecting real-world radiologist concern and management patterns rather than a definitive malignancy rate.

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.

The field of radiology is experiencing rapid adoption of large language models (LLMs), yet their tendency to generate hallucinations (plausible but incorrect information) remains a significant barrier to trust. This comprehensive review evaluates emerging agentic artificial intelligence (AI) approaches, including multi-agent role-based systems, retrieval-augmented generation (RAG), and uncertainty quantification, to assess their potential for reducing hallucinations in radiology workflows. Evidence from 2024 to 2025 demonstrates that agentic AI can improve diagnostic accuracy and reduce error rates, though these methods remain computationally demanding and lack comprehensive clinical validation. Multi-agent frameworks enable cross-validation through role-based specialization and systematic workflow orchestration, while RAG strategies enhance accuracy by grounding responses in verified medical literature. Within multi-agent systems, uncertainty quantification enables agents to communicate confidence levels to one another, allowing them to appropriately weigh each other’s contributions during collaborative analysis. While multi-agent frameworks and RAG strategies show significant promise, practical deployment will require careful integration with human oversight, robust evaluation metrics tailored to medical imaging tasks, and regulatory adaptation to ensure safe clinical use in diverse patient populations and imaging modalities.

Radiology is undergoing a paradigm shift from traditional single-function AI systems to sophisticated multi-agent networks capable of autonomous reasoning, coordinated decision-making, and adaptive workflow management. These agentic AI systems move beyond simple pattern recognition to encompass complex radiological workflows including image analysis, report generation, clinical communication, and care coordination. While multi-agent radiological AI promises enhanced diagnostic accuracy, improved workflow efficiency, and reduced physician burden, it simultaneously amplifies the long-standing “black box” problem. Traditional explainable AI methods, which are adequate for understanding isolated diagnostic predictions, fail when applied to multi-step reasoning processes involving multiple specialized agents coordinating across imaging interpretation, clinical correlation, and treatment planning. This paper examines how agentic AI systems in radiology create “compound opacity” layers of inscrutability from agent interactions and distributed decision-making processes. We analyze the autonomy–transparency paradox specific to radiological practice, where increasing AI capability directly conflicts with interpretability requirements essential for clinical trust and regulatory oversight. Through examination of emerging multi-agent radiological workflows, we propose frameworks for responsible implementation that preserve both diagnostic innovation and the fundamental principles of medical transparency and accountability.

This review explores how multimodal foundation models (MFMs) are transforming biliary tract cancer (BTC) research. BTCs are aggressive malignancies with poor prognosis, presenting unique challenges due to difficult diagnostic methods, molecular complexity, and rarity. Importantly, intrahepatic cholangiocarcinoma (iCCA), perihilar cholangiocarcinoma (pCCA), and distal bile duct cholangiocarcinoma (dCCA) represent fundamentally distinct clinical entities, with iCCA presenting as mass-forming lesions amenable to biopsy and targeted therapies, while pCCA manifests as infiltrative bile duct lesions with challenging diagnosis and primarily palliative management approaches. MFMs offer potential to advance research by integrating radiological images, histopathology, multi-omics profiles, and clinical data into unified computational frameworks, with applications tailored to these distinct BTC subtypes. Key applications include enhanced biomarker discovery that identifies previously unrecognizable cross-modal patterns, potential for improving currently limited diagnostic accuracy—though validation in BTC-specific cohorts remains essential—accelerated drug repurposing, and advanced patient stratification for personalized treatment. Despite promising results, challenges such as data scarcity, high computational demands, and clinical workflow integration remain to be addressed. Future research should focus on standardized data protocols, architectural innovations, and prospective validation studies. The integration of artificial intelligence (AI)-based methodologies offers new solutions for these historically challenging malignancies. However, current evidence for BTC-specific applications remains largely theoretical, with most studies limited to proof-of-concept designs or related cancer types. Comprehensive clinical validation studies and prospective trials demonstrating patient benefit are essential prerequisites for clinical implementation. The timeline for evidence-based clinical adoption likely extends 7–10 years, contingent on successful completion of validation studies addressing current evidence gaps.

The integration of artificial intelligence (AI) in medical imaging has revolutionized diagnostic capabilities, yet the black-box nature of deep learning models poses significant challenges for clinical adoption. Current explainable AI (XAI) approaches, including SHAP, LIME, and Grad-CAM, predominantly focus on post hoc explanations that may inadvertently undermine clinical decision-making by providing misleading confidence in AI outputs. This paper presents a systematic review and meta-analysis of 67 studies (covering 23 radiology, 19 pathology, and 25 ophthalmology applications) evaluating XAI fidelity, stability, and performance trade-offs across medical imaging modalities. Our meta-analysis of 847 initially identified studies reveals that LIME achieves superior fidelity (0.81, 95% CI: 0.78–0.84) compared to SHAP (0.38, 95% CI: 0.35–0.41) and Grad-CAM (0.54, 95% CI: 0.51–0.57) across all modalities. Post hoc explanations demonstrated poor stability under noise perturbation, with SHAP showing 53% degradation in ophthalmology applications (ρ = 0.42 at 10% noise) compared to 11% in radiology (ρ = 0.89). We demonstrate a consistent 5–7% AUC performance penalty for interpretable models but identify modality-specific stability patterns suggesting that tailored XAI approaches are necessary. Based on these empirical findings, we propose a comprehensive three-pillar accountability framework that prioritizes transparency in model development, interpretability in architecture design, and a cautious deployment of post hoc explanations with explicit uncertainty quantification. This approach offers a pathway toward genuinely accountable AI systems that enhance rather than compromise clinical decision-making quality and patient safety.