Abstract Interventions utilizing autobiographical memory (AM) frequently depend on verbal remembrance; nevertheless, their effectiveness may be constrained in the absence of sensory stimuli and emotional reinforcement. Socially Assistive Robots (SARs) provide a multimodal option; yet, there is limited understanding of how robotic contact influences users’ emotional experiences during autobiographical memory recall. We established a pipeline wherein the humanoid robot Pepper performs life-span autobiographical interviews and produces synthetic visuals from real-time speech transcripts. Fifteen adults participated in two counterbalanced conditions during a single session: (i) grasping the robot hand while recounting two memories, and (ii) recounting two memories without tactile interaction. The results show that touch markedly improved affective valence and resulted in a more substantial post-session decline in Negative Affect, mostly due to reductions in evaluations of "nervous" and "Hostile." Arousal and dominance exhibited stability. Participants assessed the system as amiable and intelligent, despite its only mild anthropomorphic qualities. These findings suggest that robotic touch can enhance the enjoyment of robot-mediated memory and specifically alleviate anxiety without modifying the recalled content. The use of a tactile channel enhances the emotional effectiveness of SAR-based AM treatments, establishing a foundation for longitudinal studies including older persons and cognitively at-risk groups.

Abstract Most machine learning models for diabetes prediction rely on small, homogeneous datasets and fixed thresholds, producing binary outputs with limited clinical utility. These approaches lack generalizability, probabilistic awareness, and interpretability, which are essential for real-world healthcare adoption. We present Diabetes Risk via Explainable AI Modeling (D.R.E.A.M.), a framework for Type 2 diabetes mellitus (T2DM) risk prediction that delivers continuous, calibrated probabilities with transparent explanations. D.R.E.A.M. integrates two complementary datasets (PIMA and BRFSS 2015) after excluding gestational diabetes cases, applies clinically guided feature engineering and class balancing, and trains ensemble models (Random Forest, XGBoost, LightGBM). Decision thresholds are optimized using precision–recall curve analysis rather than default cutoffs, enabling clinically meaningful stratification. Model interpretability is achieved through SHapley Additive exPlanations (SHAP), providing both global and patient-level insights. All models achieved Area Under the Curves above 0.83 and F1-scores of 0.78, with Random Forest offering the best balance of sensitivity (recall = 0.89 at an optimized threshold of 0.389) and interpretability. SHAP confirmed the contribution of both physiological and behavioral factors, including glucose, BMI, blood pressure, cholesterol, and physical activity. Accessible via a lightweight web interface, D.R.E.A.M. provides real-time, explainable risk scores to support personalized preventive strategies. In summary, D.R.E.A.M. advances beyond conventional post-hoc explainability by integrating calibrated probabilistic predictions, PRC-based thresholding, and direct clinician-facing deployment. This combination transforms it from a research prototype into a transparent and clinically actionable decision support system.

Abstract Biometric identification based on the electrocardiogram (ECG) is gaining attention as a secure and reliable approach to healthcare authentication, employing the unique physiological patterns detected in the ECG signal. Traditional approaches often depend on raw waveform analysis or the extraction of fiducial points, both of which are computationally intensive and challenging to implement in real-time systems. This work presents CardioIdNet, a lightweight convolutional neural network designed to perform biometric identification directly from ECG images, eliminating the need for complex signal preprocessing steps. ECG recordings from 21 subjects in the MIT-BIH arrhythmia database were segmented and converted to grayscale waveform plots, generating a comprehensive well-suited dataset for image-based deep learning classification. The CardioIdNet architecture consists of convolutional and pooling layers for hierarchical feature extraction, followed by fully connected layers for subject classification. Training was carried out using sparse categorical cross-entropy and the Adam optimizer. The dataset was split 80/20 for training and testing, and early stopping was applied to prevent overfitting and improve generalization. The results show that CardioIdNet achieves excellent performance, with accuracy of 99%, precision, recall, and F1-score of 98.18%, an AUC of 99%, and a false negative rate of 1.85%. CardioIdNet suggests to be a promising solution for biometric authentication in healthcare real-time settings, offering a balance of simplicity, interpretability, and efficiency through image-based deep learning.

The accurate evaluation of periodontal risk during orthodontic tooth movement remains a major clinical challenge, as excessive lower incisor displacement beyond anatomical limits can result in bone damage and gingival recession. This study proposes a novel artificial intelligence framework, FusionNet-ARC, designed to predict alveolar bone availability directly from standard two-dimensional cephalometric radiographs, enabling data-driven assessment of periodontal risk without the need for high-radiation imaging modalities. A total of 996 anonymized lateral cephalometric radiographs were processed through a dedicated image analysis pipeline to extract six anatomical landmarks and compute two alveolar parameters: ARC1 (lingual bone availability) and ARC2 (vestibular bone availability). The proposed FusionNet-ARC model combines a convolutional neural network (ResNet50) for visual feature extraction with a Graph Attention Network (GAT) for landmark-based spatial reasoning. The fused embeddings were regressed to predict ARC1 and ARC2 values, and model accuracy was assessed using Mean Absolute Error (MAE), coefficient of determination (R2), and prediction match rate within a ±0.5,mm tolerance. The model achieved high predictive precision for both parameters, with 66.1 % of ARC1 and 61.4 % of ARC2 predictions within clinical tolerance, showing strong agreement with manual cephalometric measurements. When applied to periodontal risk classification, it accurately identified the corresponding Normal, Borderline, and At-Risk categories, confirming its reliability for automated estimation of alveolar bone availability and related periodontal risk from standard cephalometric radiographs. The proposed system provides an objective and quantitative method for assessing alveolar bone support within routine orthodontic workflows.

Integrating artificial intelligence into healthcare has been revolutionary, enhancing diagnostic, prognostic, and therapeutic applications. Artificial intelligence has significantly improved accuracy and efficiency in medical imaging, leading to better patient outcomes. In dentistry, particularly in orthodontics, artificial intelligence is essential for automating image-based diagnosis and treatment planning, allowing specialists to evaluate anatomical structures with increased precision. This study suggests an artificial intelligence-based approach to determine the Lower Incisor—Pg, a cephalometric parameter that evaluates the sagittal position of the lower incisor, which is an essential aesthetic and functional factor in orthodontics, affecting facial harmony. Traditional manual methods for identifying this parameter on lateral cephalometric radiographs could be subjected to variability and human error. The proposed artificial intelligence model, called PointIpg, employs deep learning algorithms to automatically identify the Lower Incisor—Pg parameter and assess lower incisor’s potential movement within the lower dental arch, offering orthodontists a tool for accurate treatment planning.PointIpg was trained using a dataset of 996 teleradiographs, from which four cephalometric landmarks (Po, Or, B1, Pg) were extracted using Delta-Dent software and a PointNet++ architecture. The model achieved a Mean Absolute Error of 0.03 mm and an overall accuracy of 97.5%, enhancing precision and consistency in orthodontic planning as a reliable alternative to manual methods.

Cardiovascular disease refers to a group of disorders affecting the heart and blood vessels, including conditions like coronary artery disease, stroke, and heart failure. Arrhythmias are irregularities in the rhythm of the heart, where the heart may beat too fast, too slow, or erratically. This study presents a comparison between ChatGPT-4o and a group of cardiologists in the analysis of electrocardiogram images for assisting in the diagnosis of cardiovascular conditions. The purpose of this comparison is to evaluate the potential of using large language models like ChatGPT-4o in clinical environments, specifically for interpreting electrocardiogram traces. To achieve this, we designed an experiment where both the model and a cohort of cardiologists analyzed the same set of ECG images, and their interpretations were compared to assess performance. The evaluation focused on key diagnostic aspects: heart rate determination, rhythm interpretation, and the overall diagnosis of potential cardiovascular abnormalities. Cardiologists were asked to provide their expert insights through a structured survey that captured their diagnostic reasoning. ChatGPT-4o, in turn, was provided with the same set of images and asked to produce diagnostic outputs. Given that large language models are not explicitly trained in medical image analysis, the responses were generated based on the model’s ability to infer from the textual and visual information presented. The model’s outputs were processed and evaluated for accuracy against the responses of the cardiologists and the ground truth labels provided by the dataset. The results revealed notable differences in diagnostic accuracy between the outputs of ChatGPT-4o and the cardiologists’ assessments. ChatGPT-4o achieved an accuracy of 29.20%, sensitivity of 29.20%, and an F1-score of 0.29 when compared to the ground truth labels. In contrast, the cardiologists collectively performed significantly better, achieving an accuracy of 58.70%, sensitivity of 58.70%, and an F1-score of 0.59.

Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels due to insufficient insulin production or insulin resistance. It primarily manifests in two forms: Type 1 diabetes, an autoimmune condition typically diagnosed in younger individuals, and Type 2 diabetes, which is more prevalent and often linked to lifestyle factors such as obesity and inactivity. This study evaluates the performance of Long Short-Term Memory networks in diagnosing the two types of diabetes from Italian medical text across four progressively refined pre-processing scenarios. Each scenario incrementally builds on the previous one to enhance text cleaning and data preparation, allowing for a more refined and effective data processing pipeline. In parallel, this study introduces DiabeTalk, a chatbot developed on the WizardLM model, designed to provide specialized advice and support for diabetes diagnosis. While the WE-long short term memory models were fine-tuned with clinical data, DiabeTalk was tested without prior training on clinical diaries, allowing us to evaluate its performance in a real-world context. The results indicate that, despite the lack of domain-specific pre-training, DiabeTalk effectively employs natural language understanding and decision-making algorithms to predict diabetes type and respond to user inquiries. However, the testing revealed limitations in accuracy (77.56% versus 97.80%), with the chatbot achieving a lower performance than the WE-long short term memory model, which was applied to minimally pre-processed raw data. The findings underscore the importance of training large language models on relevant clinical datasets to enhance their response capabilities.