<b>Background:</b> Insights into the nature of false-positive findings flagged by contemporary mammography artificial intelligence (AI) systems could inform the potential use of AI to reduce false-positive recall rates. <b>Objective:</b> To compare AI and radiologists in terms of characteristics of false-positive digital breast tomosynthesis (DBT) examinations in a breast cancer screening population. <b>Methods:</b> This retrospective study included 2977 women (mean age, 58 years) participating in an observational population-based screening study who underwent 3183 screening DBT examinations from January 2013 to June 2017. A commercial AI tool analyzed DBT examinations. Positive examinations were defined for AI as an elevated-risk result and for interpreting radiologists as BI-RAD category 0. False-positive examinations were defined as the absence of a breast cancer diagnosis within 1 year. Radiologists re-reviewed the imaging for AI-flagged false-positive findings. <b>Results:</b> The false-positive rate was 10% for both AI (308/3183) and radiologists (304/3183). Of 541 total false-positive examinations, 233 (43%) were false positives for AI only, 237 (44%) for radiologists only, and 71 (13%) for both. AI-only versus radiologist-only false positives were associated with greater mean patient age (60 vs 52 years, p<.001), lower frequency of dense breasts (24% vs 57%, p<.001), and greater frequencies of a personal history of breast cancer (13% vs 4%, p<.001), prior breast imaging studies (95% vs 78%, p<.001), and prior breast surgical procedures (37% vs 11%, p<.001). The false-positive examinations included 932 AI-only flagged findings, 315 radiologist-only flagged findings, and 49 flagged findings concordant between AI and radiologists. AI-only flagged findings were most commonly benign calcifications (40%), asymmetries (13%), and benign postsurgical change (12%); radiologist-only flagged findings were most commonly masses (47%), asymmetries (19%), and indeterminate calcifications (15%). Of 18 concordant flagged findings undergoing biopsy, 44% yielded high-risk lesions. <b>Conclusion:</b> Imaging and patient-level differences were observed between AI and radiologist false-positive DBT examinations. Although only a small fraction of false-positive examinations overlapped between AI and radiologists, concordant flagged findings had a high rate of representing high-risk lesions. <b>Clinical Impact:</b> The findings may help guide strategies for using AI to improve DBT recall specificity. In particular, concordant findings may represent an enriched subset of actionable abnormalities.

Artificial intelligence (AI) represents a transformative opportunity for pain medicine, offering potential solutions to longstanding challenges in pain assessment and management. This review synthesizes the current state of AI applications with a strategic framework for implementation, highlighting established adaptation pathways from adjacent medical fields. In acute pain, AI systems have achieved regulatory approval for ultrasound guidance in regional anesthesia and shown promise in automated pain scoring through facial expression analysis. For chronic pain management, machine learning algorithms have improved diagnostic accuracy for musculoskeletal conditions and enhanced treatment selection through predictive modeling. Successful integration requires interdisciplinary collaboration and physician coleadership throughout the development process, with specific adaptations needed for pain-specific challenges. This roadmap outlines a comprehensive methodological framework for AI in pain medicine, emphasizing four key phases: problem definition, algorithm development, validation, and implementation. Critical areas for future development include perioperative pain trajectory prediction, real-time procedural guidance, and personalized treatment optimization. Success ultimately depends on maintaining strong partnerships between clinicians, developers, and researchers while addressing ethical, regulatory, and educational considerations.

The global incidence of lung diseases, particularly lung cancer, is increasing at an alarming rate, underscoring the urgent need for early detection, robust monitoring, and timely intervention. This study presents design aspects of an artificial intelligence (AI)-integrated microwave-based diagnostic tool for the early detection of lung tumors. The proposed method assimilates the prowess of machine learning (ML) tools with microwave imaging (MWI). A microwave unit containing eight antennas in the form of a wearable belt is employed for data collection from the CST body models. The data, collected in the form of scattering parameters, are reconstructed as 2D images. Two different ML approaches have been investigated for tumor detection and prediction of the size of the detected tumor. The first approach employs XGBoost models on raw S-parameters and the second approach uses convolutional neural networks (CNN) on the reconstructed 2-D microwave images. It is found that the XGBoost-based classifier with S-parameters outperforms the CNN-based classifier on reconstructed microwave images for tumor detection. Whereas a CNN-based model on reconstructed microwave images performs much better than an XGBoost-based regression model designed on the raw S-parameters for tumor size prediction. The performances of both of these models are evaluated on other body models to examine their generalization capacity over unknown data. This work explores the feasibility of a low-cost portable AI-integrated microwave diagnostic device for lung tumor detection, which eliminates the risk of exposure to harmful ionizing radiations of X-ray and CT scans.

Femoroacetabular impingement (FAI) with cam-type morphology is a common hip disorder that can result in groin pain and eventually osteoarthritis. The pre-operative assessment is based on parameters obtained from x-ray or computed tomography (CT) scans, namely alpha angle (AA) and femoral head-neck offset (FHNO). The goal of our study was to develop a computer-aided detection (CAD) system to automatically select the hip region and measure diagnostic parameters from CT scans to overcome the limitations of the tedious and time-consuming process of subjectively selecting CT image slices to obtain parameters. 271 cases of ordinary abdominopelvic CT examination were collected retrospectively from two hospitals between 2018 and 2022, each equipped with a distinct CT scanner. First, a convolution neural network (CNN) was designed to select hip region slices among abdominopelvic CT scan image series. This CNN was trained using 80 CT scans divided into 50%, 20%, and 30% for training, validation and testing groups, respectively. Second, the most appropriate oblique slice passing through the femoral head-neck complex was selected, and AA and FHNO landmarks were calculated using image-processing algorithms. The best oblique slices were selected/measured manually for each hip as ground truth and its related parameters. CT hip-region selection using CNN yielded 99.34% accuracy. Pearson correlation coefficient between manual and automatic parameters measurement were 0.964 and 0.856 for AA and FHNO, respectively. The results of this study are promising for future development of a CAD software application for screening CT scans that may aid physicians to assess FAI. Question Femoroacetabular impingement is a common, underdiagnosed hip disorder requiring time-consuming image-based measurements. Can AI improve the efficiency and consistency of its radiologic assessment? Findings Automated slice selection and landmark detection using a hybrid AI method improved measurement efficiency and accuracy, with minimal bias confirmed through Bland-Altman analysis. Clinical relevance An AI-based method enables faster, more consistent evaluation of cam-type femoroacetabular impingement in routine CT images, supporting earlier identification and reducing dependency on operator experience in clinical workflows.

Bottom-of-sulcus dysplasia (BOSD) is a diagnostically challenging subtype of focal cortical dysplasia, 60% being missed on magnetic resonance imaging (MRI). Automated MRI-based detection methods have been developed for focal cortical dysplasia, but not BOSD specifically, and few methods incorporate fluorodeoxyglucose positron emission tomography (FDG-PET) alongside MRI features. We report the development and performance of an automated BOSD detector using combined MRI + PET. The training set comprised 54 patients with focal epilepsy and BOSD. The test sets comprised 17 subsequently diagnosed patients with BOSD from the same center, and 12 published patients from a different center. Across training and test sets, 81% of patients had normal initial MRIs and most BOSDs were <1.5 cm³. In the training set, 12 features from T1-MRI, fluid-attenuated inversion recovery-MRI, and FDG-PET were evaluated to determine which features best distinguished dysplastic from normal-appearing cortex. Using the Multi-centre Epilepsy Lesion Detection group's machine-learning detection method with the addition of FDG-PET, neural network classifiers were then trained and tested on MRI + PET, MRI-only, and PET-only features. The proportion of patients whose BOSD was overlapped by the top output cluster, and the top five output clusters, were determined. Cortical and subcortical hypometabolism on FDG-PET was superior in discriminating dysplastic from normal-appearing cortex compared to MRI features. When the BOSD detector was trained on MRI + PET features, 87% BOSDs were overlapped by one of the top five clusters (69% top cluster) in the training set, 94% in the prospective test set (88% top cluster), and 75% in the published test set (58% top cluster). Cluster overlap was generally lower when the detector was trained and tested on PET-only or MRI-only features. Detection of BOSD is possible using established MRI-based automated detection methods, supplemented with FDG-PET features and trained on a BOSD-specific cohort. In clinically appropriate patients with seemingly negative MRI, the detector could suggest MRI regions to scrutinize for possible BOSD.

The integration of artificial intelligence (AI) with low-dose computed tomography (LDCT) has the potential to transform lung cancer screening into a comprehensive approach to early detection of multiple diseases. Building on over 3 decades of research and global implementation by the International Early Lung Cancer Action Program (I-ELCAP), this paper reviews the development and clinical integration of AI for interpreting LDCT scans. We describe the historical milestones in AI-assisted lung nodule detection, emphysema quantification, and cardiovascular risk assessment using visual and quantitative imaging features. We also discuss challenges related to image acquisition variability, ground truth curation, and clinical integration, with a particular focus on the design and implementation of the open-source IELCAP-AIRS system and the ScreeningPLUS infrastructure, which enable AI training, validation, and deployment in real-world screening environments. AI algorithms for rule-out decisions, nodule tracking, and disease quantification have the potential to reduce radiologist workload and advance precision screening. With the ability to evaluate multiple diseases from a single LDCT scan, AI-enabled screening offers a powerful, scalable tool for improving population health. Ongoing collaboration, standardized protocols, and large annotated datasets are critical to advancing the future of integrated, AI-driven preventive care.

Video capsule endoscopy (VCE) originally revolutionized gastrointestinal imaging by providing a noninvasive method for evaluating small bowel diseases. Recent technological innovations, including enhanced imaging systems, artificial intelligence (AI), and improved localization, have significantly improved VCE's diagnostic accuracy, efficiency, and clinical utility. This review aims to summarize and evaluate recent technological advances in VCE, focusing on system comparisons, image enhancement, localization technologies, and AI-assisted lesion detection.

Despite the growing use of artificial intelligence (AI) in medicine, imaging, and dermatology, to date, there is no information on the use of AI for discriminating cosmetic fillers on ultrasound (US). An international collaborative group working in dermatologic and esthetic US was formed and worked with the staff of the Department of Computer Science and AI of the Universidad de Granada to gather and process a relevant number of anonymized images. AI techniques based on deep learning (DL) with YOLO (you only look once) architecture, together with a bounding box annotation tool, allowed experts to manually delineate regions of interest for the discrimination of common cosmetic fillers under real-world conditions. A total of 14 physicians from 6 countries participated in the AI study and compiled a final dataset comprising 1432 US images, including HA (hyaluronic acid), PMMA (polymethylmethacrylate), CaHA (calcium hydroxyapatite), and SO (silicone oil) filler cases. The model exhibits robust and consistent classification performance, with an average accuracy of 0.92 ± 0.04 across the cross-validation folds. YOLOv11 demonstrated outstanding performance in the detection of HA and SO, yielding F1 scores of 0.96 ± 0.02 and 0.94 ± 0.04, respectively. On the other hand, CaHA and PMMA show somewhat lower and less consistent performance in terms of precision and recall, with F1-scores around 0.83. AI using YOLOv11 allowed us to discriminate reliably between HA and SO using different complexity high-frequency US devices and operators. Further AI DL-specific work is needed to identify CaHA and PMMA more accurately.

Artificial intelligence (AI) has become increasingly integral to various aspects of breast cancer care, including screening, diagnosis, and treatment. This study aimed to critically examine the application of AI throughout the breast cancer care continuum to elucidate key research developments, emerging trends, and prevalent patterns. English articles and reviews published between 1988 and 2024 were retrieved from the Web of Science database, focusing on studies that applied AI in breast cancer research. Collaboration among countries was analyzed using co-authorship networks and co-occurrence mapping. Additionally, clustering analysis using Latent Dirichlet Allocation (LDA) was conducted for topic modeling, whereas linear regression was employed to assess trends in research outputs over time. A total of 8,711 publications were included in the analysis. The United States has led the research in applying AI to the breast cancer care continuum, followed by China and India. Recent publications have increasingly focused on the utilization of deep learning and machine learning (ML) algorithms for automated breast cancer detection in mammography and histopathology. Moreover, the integration of multi-omics data and molecular profiling with AI has emerged as a significant trend. However, research on the applications of robotic and ML technologies in surgical oncology and postoperative care remains limited. Overall, the volume of research addressing AI for early detection, diagnosis, and classification of breast cancer has markedly increased over the past five years. The rapid expansion of AI-related research on breast cancer underscores its potential impact. However, significant challenges remain. Ongoing rigorous investigations are essential to ensure that AI technologies yield evidence-based benefits across diverse patient populations, thereby avoiding the inadvertent exacerbation of existing healthcare disparities.

Adolescent idiopathic scoliosis (AIS) is a common spinal deformity affecting approximately 2.2% of boys and 4.8% of girls worldwide. The Cobb angle serves as the gold standard for AIS severity assessment, yet traditional manual measurements suffer from significant observer variability, compromising diagnostic accuracy. Despite prior automation attempts, existing methods use simplified spinal models and predetermined curve patterns that fail to address clinical complexity. We present a novel deep learning framework for AIS assessment that simultaneously predicts both superior and inferior endplate angles with corresponding midpoint coordinates for each vertebra, preserving the anatomical reality of vertebral wedging in progressive AIS. Our approach combines an HRNet backbone with Swin-Transformer modules and biomechanically informed constraints for enhanced feature extraction. We employ Singular Value Decomposition (SVD) to analyze angle predictions directly from vertebral morphology, enabling flexible detection of diverse scoliosis patterns without predefined curve assumptions. Using 630 full-spine anteroposterior radiographs from patients aged 10-18 years with rigorous dual-rater annotation, our method achieved 83.45% diagnostic accuracy and 2.55{\deg} mean absolute error. The framework demonstrates exceptional generalization capability on out-of-distribution cases. Additionally, we introduce the Vertebral Wedging Index (VWI), a novel metric quantifying vertebral deformation. Longitudinal analysis revealed VWI's significant prognostic correlation with curve progression while traditional Cobb angles showed no correlation, providing robust support for early AIS detection, personalized treatment planning, and progression monitoring.