Radiology is at the center of many medical specialties, which rely on radiologists' interpretation of images from various modalities, including CT, MRI, ultrasound, and X-ray [1]. The analysis of these images is crucial for detecting and characterizing medical conditions, quantifying disease progression, and monitoring treatment efficacy across a broad spectrum of diseases. AI has the potential to enhance radiology workflows and improve radiologists' efficiency, particularly for labor-intensive tasks such as image segmentation, or specialized and/or complex tasks which are prone to inter-reader variability [2, 3]. To date, the dominant paradigm in radiological AI development has involved training specialized models for individual tasks such as segmentation, abnormality detection (e.g., tumor detection), or pathology classification. However, this "one-task, one-model" approach is exceptionally resource-intensive, as it necessitates the curation and manual annotation of large, task-specific datasets for each modality and clinical application [4, 5]. It is potentially one of the bottlenecks in moving AI radiology models into the clinical workflow. Foundation models (FM) represent a significant paradigm shift in the field of AI.

Effective Operation and Maintenance (O&M) is critical to reducing the Levelised Cost of Energy (LCOE) from wind power, yet the unstructured, free-text nature of turbine maintenance logs presents a significant barrier to automated analysis. Our paper addresses this by presenting a novel and reproducible framework for benchmarking Large Language Models (LLMs) on the task of classifying these complex industrial records. To promote transparency and encourage further research, this framework has been made publicly available as an open-source tool. We systematically evaluate a diverse suite of state-of-the-art proprietary and open-source LLMs, providing a foundational assessment of their trade-offs in reliability, operational efficiency, and model calibration. Our results quantify a clear performance hierarchy, identifying top models that exhibit high alignment with a benchmark standard and trustworthy, well-calibrated confidence scores. We also demonstrate that classification performance is highly dependent on the task's semantic ambiguity, with all models showing higher consensus on objective component identification than on interpretive maintenance actions. Given that no model achieves perfect accuracy and that calibration varies dramatically, we conclude that the most effective and responsible near-term application is a Human-in-the-Loop system, where LLMs act as a powerful assistant to accelerate and standardise data labelling for human experts, thereby enhancing O&M data quality and downstream reliability analysis.

Autonomous robots operating in dynamic environments should identify and report anomalies. Embodying proactive mitigation improves safety and operational continuity. This paper presents a multimodal anomaly detection and mitigation system that integrates vision-language models and large language models to identify and report hazardous situations and conflicts in real-time. The proposed system enables robots to perceive, interpret, report, and if possible respond to urban and environmental anomalies through proactive detection mechanisms and automated mitigation actions. A key contribution in this paper is the integration of Hazardous and Conflict states into the robot's decision-making framework, where each anomaly type can trigger specific mitigation strategies. User studies (n = 30) demonstrated the effectiveness of the system in anomaly detection with 91.2% prediction accuracy and relatively low latency response times using edge-ai architecture.

Brain tumors are serious health problems that require early diagnosis due to their high mortality rates. Diagnosing tumors by examining Magnetic Resonance Imaging (MRI) images is a process that requires expertise and is prone to error. Therefore, the need for automated diagnosis systems is increasing day by day. In this context, a robust and explainable Deep Learning (DL) model for the classification of brain tumors is proposed. In this study, a publicly available Figshare dataset containing 3,064 T1-weighted contrast-enhanced brain MRI images of three tumor types was used. First, the classification performance of nine well-known CNN architectures was evaluated to determine the most effective backbone. Among these, EfficientNetV2 demonstrated the best performance and was selected as the backbone for further development. Subsequently, an attention-based MLP-Mixer architecture was integrated into EfficientNetV2 to enhance its classification capability. The performance of the final model was comprehensively compared with basic CNNs and the methods in the literature. Additionally, Grad-CAM visualization was used to interpret and validate the decision-making process of the proposed model. The proposed model's performance was evaluated using the five-fold cross-validation method. The proposed model demonstrated superior performance with 99.50% accuracy, 99.47% precision, 99.52% recall and 99.49% F1 score. The results obtained show that the model outperforms the studies in the literature. Moreover, Grad-CAM visualizations demonstrate that the model effectively focuses on relevant regions of MRI images, thus improving interpretability and clinical reliability. A robust deep learning model for clinical decision support systems has been obtained by combining EfficientNetV2 and attention-based MLP-Mixer, providing high accuracy and interpretability in brain tumor classification.

In subjective assessment of image and video quality, observers rate or compare selected stimuli. Before calculating the mean opinion scores (MOS) for these stimuli from the ratings, it is recommended to identify and deal with outliers that may have given unreliable ratings. Several methods are available for this purpose, some of which have been standardized. These methods are typically based on statistics and sometimes tested by introducing synthetic ratings from artificial outliers, such as random clickers. However, a reliable and comprehensive approach is lacking for comparative performance analysis of outlier detection methods. To fill this gap, this work proposes and applies an empirical worst-case analysis as a general solution. Our method involves evolutionary optimization of an adversarial black-box attack on outlier detection algorithms, where the adversary maximizes the distortion of scale values with respect to ground truth. We apply our analysis to several hard and soft outlier detection methods for absolute category ratings and show their differing performance in this stress test. In addition, we propose two new outlier detection methods with low complexity and excellent worst-case performance. Software for adversarial attacks and data analysis is available.

Objective: To develop and interpret a supervised variational autoencoder (VAE) model for classifying cardiotocography (CTG) signals based on pregnancy outcomes, addressing interpretability limits of current deep learning approaches. Methods: The OxMat CTG dataset was used to train a VAE on five-minute fetal heart rate (FHR) segments, labeled with postnatal outcomes. The model was optimised for signal reconstruction and outcome prediction, incorporating Kullback-Leibler divergence and total correlation (TC) constraints to structure the latent space. Performance was evaluated using area under the receiver operating characteristic curve (AUROC) and mean squared error (MSE). Interpretability was assessed using coefficient of determination, latent traversals and unsupervised component analyses. Results: The model achieved an AUROC of 0.752 at the segment level and 0.779 at the CTG level, where predicted scores were aggregated. Relaxing TC constraints improved both reconstruction and classification. Latent analysis showed that baseline-related features (e.g., FHR baseline, baseline shift) were well represented and aligned with model scores, while metrics like short- and long-term variability were less strongly encoded. Traversals revealed clear signal changes for baseline features, while other properties were entangled or subtle. Unsupervised decompositions corroborated these patterns. Findings: This work demonstrates that supervised VAEs can achieve competitive fetal outcome prediction while partially encoding clinically meaningful CTG features. The irregular, multi-timescale nature of FHR signals poses challenges for disentangling physiological components, distinguishing CTG from more periodic signals such as ECG. Although full interpretability was not achieved, the model supports clinically useful outcome prediction and provides a basis for future interpretable, generative models.

This study examined the use of machine learning and domain specific enrichment on patient generated health data, in the form of free text meal logs, to classify meals on alignment with different nutritional goals. We used a dataset of over 3000 meal records collected by 114 individuals from a diverse, low income community in a major US city using a mobile app. Registered dietitians provided expert judgement for meal to goal alignment, used as gold standard for evaluation. Using text embeddings, including TFIDF and BERT, and domain specific enrichment information, including ontologies, ingredient parsers, and macronutrient contents as inputs, we evaluated the performance of logistic regression and multilayer perceptron classifiers using accuracy, precision, recall, and F1 score against the gold standard and self assessment. Even without enrichment, ML outperformed self assessments of individuals who logged meals, and the best performing combination of ML classifier with enrichment achieved even higher accuracies. In general, ML classifiers with enrichment of Parsed Ingredients, Food Entities, and Macronutrients information performed well across multiple nutritional goals, but there was variability in the impact of enrichment and classification algorithm on accuracy of classification for different nutritional goals. In conclusion, ML can utilize unstructured free text meal logs and reliably classify whether meals align with specific nutritional goals, exceeding self assessments, especially when incorporating nutrition domain knowledge. Our findings highlight the potential of ML analysis of patient generated health data to support patient centered nutrition guidance in precision healthcare.

This paper presents a novel approach to recruitment automation. Large Language Models (LLMs) were fine-tuned to improve accuracy and efficiency. Building upon our previous work on the Multilayer Large Language Model-Based Robotic Process Automation Applicant Tracking (MLAR) system . This work introduces a novel methodology. Training fine-tuned LLMs specifically tuned for recruitment tasks. The proposed framework addresses the limitations of generic LLMs by creating a synthetic dataset that uses a standardized JSON format. This helps ensure consistency and scalability. In addition to the synthetic data set, the resumes were parsed using DeepSeek, a high-parameter LLM. The resumes were parsed into the same structured JSON format and placed in the training set. This will help improve data diversity and realism. Through experimentation, we demonstrate significant improvements in performance metrics, such as exact match, F1 score, BLEU score, ROUGE score, and overall similarity compared to base models and other state-of-the-art LLMs. In particular, the fine-tuned Phi-4 model achieved the highest F1 score of 90.62%, indicating exceptional precision and recall in recruitment tasks. This study highlights the potential of fine-tuned LLMs. Furthermore, it will revolutionize recruitment workflows by providing more accurate candidate-job matching.

Accurate risk assessment in lung cancer screening is critical for enabling early cancer detection and minimizing unnecessary invasive procedures. The Lung CT Screening Reporting and Data System (Lung-RADS) has been widely used as the standard framework for patient management and follow-up. Nevertheless, Lung-RADS faces trade-offs between sensitivity and specificity, as it stratifies risk solely based on lung nodule characteristics without incorporating various risk factors. Here we propose a reasoning language model (RLM) to integrate radiology findings with longitudinal medical records for individualized lung cancer risk assessment. Through a systematic study including dataset construction and distillation, supervised fine-tuning, reinforcement learning, and comprehensive evaluation, our model makes significant improvements in risk prediction performance on datasets in the national lung screening trial. Notably, RLM can decompose the risk evaluation task into sub-components, analyze the contributions of diverse risk factors, and synthesize them into a final risk score computed using our data-driven system equation. Our approach improves both predictive accuracy and monitorability through the chain of thought reasoning process, thereby facilitating clinical translation into lung cancer screening.

Aerial remote sensing using multispectral and RGB imagers has provided a critical impetus to precision agriculture. Analysis of the hyperspectral images with limited or no labels is challenging. This paper focuses on self-supervised learning to create neural network embeddings reflecting vegetation properties of trees from aerial hyperspectral images of crop fields. Experimental results demonstrate that a constructed tree representation, using a vegetation property-related embedding space, performs better in downstream machine learning tasks compared to the direct use of hyperspectral vegetation properties as tree representations.