This Perspective article explores the transformative role of artificial intelligence (AI) in predicting perioperative hypoxemia through the integration of deep learning (DL) with multimodal clinical data, including lung imaging, pulmonary function tests (PFTs), and arterial blood gas (ABG) analysis. Perioperative hypoxemia, defined as arterial oxygen partial pressure (PaO₂) <60 mmHg or oxygen saturation (SpO₂) <90%, poses significant risks of delayed recovery and organ dysfunction. Traditional diagnostic methods, such as radiological imaging and ABG analysis, often lack integrated predictive accuracy. AI frameworks, particularly convolutional neural networks (CNNs) and hybrid models like TD-CNNLSTM-LungNet, demonstrate exceptional performance in detecting pulmonary inflammation and stratifying hypoxemia risk, achieving up to 96.57% accuracy in pneumonia subtype differentiation and an AUC of 0.96 for postoperative hypoxemia prediction. Multimodal AI systems, such as DeepLung-Predict, unify CT scans, PFTs, and ABG parameters to enhance predictive precision, surpassing conventional methods by 22%. However, challenges persist, including dataset heterogeneity, model interpretability, and clinical workflow integration. Future directions emphasize multicenter validation, explainable AI (XAI) frameworks, and pragmatic trials to ensure equitable and reliable deployment. This AI-driven approach not only optimizes resource allocation but also mitigates financial burdens on healthcare systems by enabling early interventions and reducing ICU admission risks.

Ultrasound (US) is the preferred modality for assessing anterior talofibular ligament (ATFL) injuries. We aimed to advance ATFL injuries classification by developing a US-based deep learning (DL) model, and explore how artificial intelligence (AI) could help radiologists improve diagnostic performance. Consecutive healthy controls and patients with acute ATFL injuries (mild strain, partial tear, complete tear, and avulsion fracture) at 10 hospitals were retrospectively included. A US-based DL model (ATFLNet) was trained (n=2566), internally validated (n=642), and externally validated (n=717 and 493). Surgical or radiological findings based on the majority consensus of three experts served as the reference standard. Prospective validation was conducted at three additional hospitals (n=472). The performance was compared to that of 12 radiologists at different levels (external validation sets 1 and 2); an ATFLNet-aided strategy was developed, comparing with the radiologists when reviewing B-mode images (external validation set 2); the strategy was then tested in a simulated scenario (reviewing images alongside dynamic clips; prospective validation set). Statistical comparisons were performed using the McNemar's test, while inter-reader agreement was evaluated with the Multireader Fleiss κ statistic. ATFLNet obtained macro-average area under the curve ≥0.970 across all five classes in each dataset, indicating robust overall performance. Additionally, it consistently outperformed senior radiologists in external validation sets (all p<.05). ATFLNet-aided strategy improved radiologists' average accuracy (0.707 vs. 0.811, p<.001) for image review. In the simulated scenario, it led to enhanced accuracy (0.794 to 0.864, p=.003), and a reduction in diagnostic variability, particularly for junior radiologists. Our US-based model outperformed human experts for ATFL injury evaluation. AI-aided strategies hold the potential to enhance diagnostic performance in real-world clinical scenarios.

Medical image segmentation is crucial for disease diagnosis and treatment planning, yet developing robust segmentation models often requires substantial computational resources and large datasets. Existing research shows that pre-trained and finetuned foundation models can boost segmentation performance. However, questions remain about how particular image preprocessing steps may influence segmentation performance across different medical imaging modalities. In particular, edges-abrupt transitions in pixel intensity-are widely acknowledged as vital cues for object boundaries but have not been systematically examined in the pre-training of foundation models. We address this gap by investigating to which extend pre-training with data processed using computationally efficient edge kernels, such as kirsch, can improve cross-modality segmentation capabilities of a foundation model. Two versions of a foundation model are first trained on either raw or edge-enhanced data across multiple medical imaging modalities, then finetuned on selected raw subsets tailored to specific medical modalities. After systematic investigation using the medical domains Dermoscopy, Fundus, Mammography, Microscopy, OCT, US, and XRay, we discover both increased and reduced segmentation performance across modalities using edge-focused pre-training, indicating the need for a selective application of this approach. To guide such selective applications, we propose a meta-learning strategy. It uses standard deviation and image entropy of the raw image to choose between a model pre-trained on edge-enhanced or on raw data for optimal performance. Our experiments show that integrating this meta-learning layer yields an overall segmentation performance improvement across diverse medical imaging tasks by 16.42% compared to models pre-trained on edge-enhanced data only and 19.30% compared to models pre-trained on raw data only.

Cervical cancer commonly associated with human papillomavirus (HPV) infection, remains the fourth most common cancer in women globally. This study aims to develop and evaluate a Natural Language Processing (NLP) system to identify and analyze cervical cancer incidence trends from 2013 to 2023 at our institution, focusing on age-specific variations and evaluating the possible impact of HPV vaccination. This retrospective cohort study, we analyzed unstructured radiology reports collected between 2013 and 2023, comprising 433,207 studies involving 250,181 women who underwent CT, MRI, or ultrasound scans of the abdominopelvic region. A rule-based NLP system was developed to extract references to cervical cancer from these reports and validated against a set of 200 manually annotated cases reviewed by an experienced radiologist. The NLP system demonstrated excellent performance, achieving an accuracy of over 99.5 %. This high reliability enabled its application in a large-scale population study. Results show that the women under 30 maintain a consistently low cervical cancer incidence, likely reflecting early HPV vaccination impact. The 30-40 cohorts declined until 2020, followed by a slight increase, while the 40-60 groups exhibited an overall downward trend with fluctuations, suggesting long-term vaccine effects. Incidence in patients over 60 also declined, though with greater variability, possibly due to other risk factors. The developed NLP system effectively identified cervical cancer cases from unstructured radiology reports, facilitating an accurate analysis of the impact of HPV vaccination on cervical cancer prevalence and imaging study requirements. This approach demonstrates the potential of AI and NLP tools in enhancing data accuracy and efficiency in medical epidemiology research. NLP-based approaches can significantly improve the collection and analysis of epidemiological data on cervical cancer, supporting the development of more targeted and personalized prevention strategies-particularly in populations with heterogeneous HPV vaccination coverage.

This systematic review and meta-analysis evaluated the performance of imaging-based artificial intelligence (AI) models in diagnosing preoperative cervical lymph node metastasis (LNM) in clinically node-negative (cN0) papillary thyroid carcinoma (PTC). We conducted a literature search in PubMed, Embase, and Web of Science until February 25, 2025. Studies were selected that focused on imaging-based AI models for predicting cervical LNM in cN0 PTC. The diagnostic performance metrics were analyzed using a bivariate random-effects model, and study quality was assessed with the QUADAS-2 tool. From 671 articles, 11 studies involving 3366 patients were included. Ultrasound (US)-based AI models showed pooled sensitivity of 0.79 and specificity of 0.82, significantly higher than radiologists (p < 0.001). CT-based AI models demonstrated sensitivity of 0.78 and specificity of 0.89. Imaging-based AI models, particularly US-based AI, show promising diagnostic performance. There is a need for further multicenter prospective studies for validation. PROSPERO: (CRD420251063416).

Radiology report generation (RRG) for diagnostic images, such as chest X-rays, plays a pivotal role in both clinical practice and AI. Traditional free-text reports suffer from redundancy and inconsistent language, complicating the extraction of critical clinical details. Structured radiology report generation (S-RRG) offers a promising solution by organizing information into standardized, concise formats. However, existing approaches often rely on classification or visual question answering (VQA) pipelines that require predefined label sets and produce only fragmented outputs. Template-based approaches, which generate reports by replacing keywords within fixed sentence patterns, further compromise expressiveness and often omit clinically important details. In this work, we present a novel approach to S-RRG that includes dataset construction, model training, and the introduction of a new evaluation framework. We first create a robust chest X-ray dataset (MIMIC-STRUC) that includes disease names, severity levels, probabilities, and anatomical locations, ensuring that the dataset is both clinically relevant and well-structured. We train an LLM-based model to generate standardized, high-quality reports. To assess the generated reports, we propose a specialized evaluation metric (S-Score) that not only measures disease prediction accuracy but also evaluates the precision of disease-specific details, thus offering a clinically meaningful metric for report quality that focuses on elements critical to clinical decision-making and demonstrates a stronger alignment with human assessments. Our approach highlights the effectiveness of structured reports and the importance of a tailored evaluation metric for S-RRG, providing a more clinically relevant measure of report quality.

This study investigates how deep learning (DL) can enhance ovarian cancer diagnosis and staging using large imaging datasets. Specifically, we compare six conventional convolutional neural network (CNN) architectures-ResNet, DenseNet, GoogLeNet, U-Net, VGG, and AlexNet-with OCDA-Net, an enhanced model designed for [¹⁸F]FDG PET image analysis. The OCDA-Net, an advancement on the ResNet architecture, was thoroughly compared using randomly split datasets of training (80%), validation (10%), and test (10%) images. Trained over 100 epochs, OCDA-Net achieved superior diagnostic classification with an accuracy of 92%, and staging results of 94%, supported by robust precision, recall, and F-measure metrics. Grad-CAM ++ heat-maps confirmed that the network attends to hyper-metabolic lesions, supporting clinical interpretability. Our findings show that OCDA-Net outperforms existing CNN models and has strong potential to transform ovarian cancer diagnosis and staging. The study suggests that implementing these DL models in clinical practice could ultimately improve patient prognoses. Future research should expand datasets, enhance model interpretability, and validate these models in clinical settings.

This work presents the results of a methodological transfer from remote sensing to healthcare, adapting AMBER -- a transformer-based model originally designed for multiband images, such as hyperspectral data -- to the task of 3D medical datacube segmentation. In this study, we use the AMBER architecture with Adaptive Fourier Neural Operators (AFNO) in place of the multi-head self-attention mechanism. While existing models rely on various forms of attention to capture global context, AMBER-AFNO achieves this through frequency-domain mixing, enabling a drastic reduction in model complexity. This design reduces the number of trainable parameters by over 80% compared to UNETR++, while maintaining a FLOPs count comparable to other state-of-the-art architectures. Model performance is evaluated on two benchmark 3D medical datasets -- ACDC and Synapse -- using standard metrics such as Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD), demonstrating that AMBER-AFNO achieves competitive or superior accuracy with significant gains in training efficiency, inference speed, and memory usage.

This study aims to evaluate the diagnostic accuracy of an open-source deep learning (DL) model for detecting clinically significant prostate cancer (csPCa) in biparametric MRI (bpMRI). It also aims to outline the necessary components of the model that facilitate effective sharing and external evaluation of PCa detection models. This retrospective diagnostic accuracy study evaluated a publicly available DL model trained to detect PCa on bpMRI. External validation was performed on bpMRI exams from 151 biologically male patients (mean age, 65 ± 8 years). The model's performance was evaluated using patient-level classification of PCa with both radiologist interpretation and histopathology serving as the ground truth. The model processed bpMRI inputs to generate lesion probability maps. Performance was assessed using the area under the receiver operating characteristic curve (AUC) for PI-RADS ≥ 3, PI-RADS ≥ 4, and csPCa (defined as Gleason ≥ 7) at an exam level. The model achieved AUCs of 0.86 (95% CI: 0.80-0.92) and 0.91 (95% CI: 0.85-0.96) for predicting PI-RADS ≥ 3 and ≥ 4 exams, respectively, and 0.78 (95% CI: 0.71-0.86) for csPCa. Sensitivity and specificity for csPCa were 0.87 and 0.53, respectively. Fleiss' kappa for inter-reader agreement was 0.51. The open-source DL model offers high sensitivity to clinically significant prostate cancer. The study underscores the importance of sharing model code and weights to enable effective external validation and further research. Question Inter-reader variability hinders the consistent and accurate detection of clinically significant prostate cancer in MRI. Findings An open-source deep learning model demonstrated reproducible diagnostic accuracy, achieving AUCs of 0.86 for PI-RADS ≥ 3 and 0.78 for CsPCa lesions. Clinical relevance The model's high sensitivity for MRI-positive lesions (PI-RADS ≥ 3) may provide support for radiologists. Its open-source deployment facilitates further development and evaluation across diverse clinical settings, maximizing its potential utility.

Alzheimer's disease (AD) progression follows a complex continuum from normal cognition (NC) through mild cognitive impairment (MCI) to dementia, yet most deep learning approaches oversimplify this into discrete classification tasks. This study introduces M$^3$AD, a novel multi-task multi-gate mixture of experts framework that jointly addresses diagnostic classification and cognitive transition modeling using structural MRI. We incorporate three key innovations: (1) an open-source T1-weighted sMRI preprocessing pipeline, (2) a unified learning framework capturing NC-MCI-AD transition patterns with demographic priors (age, gender, brain volume) for improved generalization, and (3) a customized multi-gate mixture of experts architecture enabling effective multi-task learning with structural MRI alone. The framework employs specialized expert networks for diagnosis-specific pathological patterns while shared experts model common structural features across the cognitive continuum. A two-stage training protocol combines SimMIM pretraining with multi-task fine-tuning for joint optimization. Comprehensive evaluation across six datasets comprising 12,037 T1-weighted sMRI scans demonstrates superior performance: 95.13% accuracy for three-class NC-MCI-AD classification and 99.15% for binary NC-AD classification, representing improvements of 4.69% and 0.55% over state-of-the-art approaches. The multi-task formulation simultaneously achieves 97.76% accuracy in predicting cognitive transition. Our framework outperforms existing methods using fewer modalities and offers a clinically practical solution for early intervention. Code: https://github.com/csyfjiang/M3AD.