The U.S. Food and Drug Administration (FDA) plays an important role in ensuring safety and effectiveness of AI/ML-enabled devices through its regulatory processes. In recent years, there has been an increase in the number of these devices cleared by FDA. This study analyzes 104 FDA-approved ML-enabled medical devices from May 2021 to April 2023, extending previous research to provide a contemporary perspective on this evolving landscape. We examined clinical task, device task, device input and output, ML method and level of autonomy. Most approvals (n = 103) were via the 510(k) premarket notification pathway, indicating substantial equivalence to existing devices. Devices predominantly supported diagnostic tasks (n = 81). The majority of devices used imaging data (n = 99), with CT and MRI being the most common modalities. Device autonomy levels were distributed as follows: 52% assistive (requiring users to confirm or approve AI provided information or decision), 27% autonomous information, and 21% autonomous decision. The prevalence of assistive devices indicates a cautious approach to integrating ML into clinical decision-making, favoring support rather than replacement of human judgment.

The diagnosis of craniosynostosis, a condition involving the premature fusion of cranial sutures in infants, is essential for ensuring timely treatment and optimal surgical outcomes. Current diagnostic approaches often require CT scans, which expose children to significant radiation risks. To address this, we present a novel deep learning-based model utilizing multi-view X-ray images for craniosynostosis detection. The proposed model integrates advanced multi-view fusion (MVF) and cross-attention mechanisms, effectively combining features from three X-ray views (AP, lateral right, lateral left) and patient metadata (age, sex). By leveraging these techniques, the model captures comprehensive semantic and structural information for high diagnostic accuracy while minimizing radiation exposure. Tested on a dataset of 882 X-ray images from 294 pediatric patients, the model achieved an AUROC of 0.975, an F1-score of 0.882, a sensitivity of 0.878, and a specificity of 0.937. Grad-CAM visualizations further validated its ability to localize disease-relevant regions using only classification annotations. The model demonstrates the potential to revolutionize pediatric care by providing a safer, cost-effective alternative to CT scans.

This study aimed to develop and evaluate an artificial intelligence model to predict 28-day mortality of pneumonia patients at the time of disposition from emergency department (ED). A multicenter retrospective study was conducted on data from pneumonia patients who visited the ED of a tertiary academic hospital for 8 months and from the Medical Information Mart for Intensive Care (MIMIC-IV) database. We combined chest X-ray information, clinical data, and CURB-65 score to develop three models with the CURB-65 score as a baseline. A total of 2,874 ED visits were analyzed. The RSF model using CXR, clinical data and CURB-65 achieved a C-index of 0.872 in test set, significantly outperforming the CURB-65 score. This study developed a prediction model in pneumonia patients' prognosis, highlighting the potential for supporting clinical decision making in ED through multi-modal clinical information.

This study presents an AI framework for real-time NAFLD detection using ultrasound imaging, addressing operator dependency, imaging variability, and class imbalance. It integrates CNNs with machine learning classifiers and applies preprocessing techniques, including normalization and GAN-based augmentation, to enhance prediction for underrepresented disease stages. Grad-CAM provides visual explanations to support clinical interpretation. Trained on 10,352 annotated images from multiple Saudi centers, the framework achieved 98.9% accuracy and an AUC of 0.99, outperforming baseline CNNs by 12.4% and improving sensitivity for advanced fibrosis and subtle features. Future work will extend multi-class classification, validate performance across settings, and integrate with clinical systems.

Early classification of brain tumors is the key to effective treatment. With advances in medical imaging technology, automated classification algorithms face challenges due to tumor diversity. Although Swin Transformer is effective in handling high-resolution images, it encounters difficulties with small datasets and high computational complexity. This study introduces SparseSwinMDT, a novel model that combines sparse token representation with multipath decision trees. Experimental results show that SparseSwinMDT achieves an accuracy of 99.47% in brain tumor classification, significantly outperforming existing methods while reducing computation time, making it particularly suitable for resource-constrained medical environments.

We investigate the use of quantum computing algorithms on real quantum hardware to tackle the computationally intensive task of feature selection for light-weight medical image datasets. Feature selection is often formulated as a k of n selection problem, where the complexity grows binomially with increasing k and n. Quantum computers, particularly quantum annealers, are well-suited for such problems, which may offer advantages under certain problem formulations. We present a method to solve larger feature selection instances than previously demonstrated on commercial quantum annealers. Our approach combines a linear Ising penalty mechanism with subsampling and thresholding techniques to enhance scalability. The method is tested in a toy problem where feature selection identifies pixel masks used to reconstruct small-scale medical images. We compare our approach against a range of feature selection strategies, including randomized baselines, classical supervised and unsupervised methods, combinatorial optimization via classical and quantum solvers, and learning-based feature representations. The results indicate that quantum annealing-based feature selection is effective for this simplified use case, demonstrating its potential in high-dimensional optimization tasks. However, its applicability to broader, real-world problems remains uncertain, given the current limitations of quantum computing hardware. While learned feature representations such as autoencoders achieve superior reconstruction performance, they do not offer the same level of interpretability or direct control over input feature selection as our approach.

Alzheimer's disease (AD) is the principal cause of dementia and requires the early diagnosis of people with mild cognitive impairment (MCI) who are at high risk of progressing. Early diagnosis is imperative for optimizing clinical management and selecting proper therapeutic interventions. Structural magnetic resonance imaging (MRI) markers have been widely investigated for predicting the conversion of MCI to AD, and recent advances in deep learning (DL) methods offer enhanced capabilities for identifying subtle neurodegenerative changes over time. We selected 228 MCI participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had at least three T1-weighted MRI scans within 18 months of baseline. MRI volumes underwent bias correction, segmentation, and radiomics feature extraction. A 3D residual network (ResNet3D) was trained using a pairwise ranking loss to capture single-timepoint risk scores. Longitudinal analyses were performed by extracting deep convolutional neural network (CNN) embeddings and gray matter radiomics for each scan, which were put into a time-aware long short-term memory (LSTM) model with an attention mechanism. A single-timepoint ResNet3D model achieved modest performance (c-index ~ 0.70). Incorporating longitudinal MRI files or downstream survival models led to a pronounced prognostic improvement (c-index:0.80-0.90), but was not further improved by longitudinal radiomics data. Time-specific classification within two- and three-year and five-year windows after the last MRI acquisition showed high accuracy (AUC > 0.85). Several radiomics, including gray matter surface to volume and elongation, emerged as the most predictive features. Each SD change in the gray matter surface to volume change within the last visit was associated with an increased risk of developing AD (HR: 1.50; 95% CI: 1.25-1.79). These findings emphasize the value of structural MRI within the advanced DL architectures for predicting MCI-to-AD conversion. The approach may enable earlier risk stratification and targeted interventions for individuals most likely to progress. limitations in sample size and computational resources warrant larger, more diverse studies to confirm these observations and explore additional improvements.

Sex differences in mental health become more evident across adolescence, with a two-fold increase of prevalence of mood disorders in females compared to males. The brain underpinnings remain understudied. Here, we investigated the role of age, puberty and mental health in determining the longitudinal development of sex differences in brain structure. We captured sex differences in limbic and non-limbic structures using machine learning models trained in cross-sectional brain imaging data of 1132 youths, yielding limbic and non-limbic estimates of brain sex. Applied to two independent longitudinal samples (total: 8184 youths), our models revealed pronounced sex differences in brain structure with increasing age. For females, brain sex was sensitive to pubertal development (menarche) over time and, for limbic structures, to mood-related mental health. Our findings highlight the limbic system as a key contributor to the development of sex differences in the brain and the potential of machine learning models for brain sex classification to investigate sex-specific processes relevant to mental health.

In recent years, Artificial Intelligence (AI) has gained prominence as a robust tool for clinical decision-making and diagnostics, owing to its capacity to process and analyze large datasets with high accuracy. More specifically, Deep Learning, and its subclasses, have shown significant potential in image processing, including medical imaging and histological analysis. In forensic pathology, AI has been employed for the interpretation of histopathological data, identifying conditions such as myocardial infarction, traumatic injuries, and heart rhythm abnormalities. This review aims to highlight key advances in AI's role, particularly machine learning (ML) and deep learning (DL) techniques, in forensic neuropathology, with a focus on its ability to interpret instrumental and histopathological data to support professional diagnostics. A systematic review of the literature regarding applications of Artificial Intelligence in forensic neuropathology was carried out according to the Preferred Reporting Item for Systematic Review (PRISMA) standards. We selected 34 articles regarding the main applications of AI in this field, dividing them into two categories: those addressing traumatic brain injury (TBI), including intracranial hemorrhage or cerebral microbleeds, and those focusing on epilepsy and SUDEP, including brain disorders and central nervous system neoplasms capable of inducing seizure activity. In both cases, the application of AI techniques demonstrated promising results in the forensic investigation of cerebral pathology, providing a valuable computer-assisted diagnostic tool to aid in post-mortem computed tomography (PMCT) assessments of cause of death and histopathological analyses. In conclusion, this paper presents a comprehensive overview of the key neuropathology areas where the application of artificial intelligence can be valuable in investigating causes of death.

Federated learning (FL) is a decentralized machine learning paradigm in which multiple clients collaboratively train a shared model without sharing their local private data. However, real-world applications of FL frequently encounter challenges arising from the non-identically and independently distributed (non-IID) local datasets across participating clients, which is particularly pronounced in the field of medical imaging, where shifts in image feature distributions significantly hinder the global model's convergence and performance. To address this challenge, we propose FedMP, a novel method designed to enhance FL under non-IID scenarios. FedMP employs stochastic feature manifold completion to enrich the training space of individual client classifiers, and leverages class-prototypes to guide the alignment of feature manifolds across clients within semantically consistent subspaces, facilitating the construction of more distinct decision boundaries. We validate the effectiveness of FedMP on multiple medical imaging datasets, including those with real-world multi-center distributions, as well as on a multi-domain natural image dataset. The experimental results demonstrate that FedMP outperforms existing FL algorithms. Additionally, we analyze the impact of manifold dimensionality, communication efficiency, and privacy implications of feature exposure in our method.