Artificial Intelligence (AI) and digital devices are becoming more pervasive in the monitoring of individuals’ health as device functionalities increase and their presence in daily life continues to grow. Subsequently, these tools can provide transformative healthcare practice by enabling more personalized, proactive, and engaging interventions. This work presents the design and development of an interactive physical therapy system for adolescents that integrates a wearable EMG leg sensor array with a computer-based video game. The system captures EMG signals during therapeutic exercises and uses them to drive in-game mechanics, providing real-time feedback while also aiming to improve therapeutic engagement. A key feature of the system is its ability to adapt based on individual performance and clinician input, allowing personalized therapy progression and reporting. Using this approach, the system supports continuous personalized assessment and customized intervention for children while promoting motivation through dynamic gameplay. The developed system illustrates a promising direction for designing interactive, AI-augmented health technologies that can enhance pediatric rehabilitation outcomes through accessible, game-based experiences. Further, this work contributes to ongoing research in human-computer interaction, AI, and behavioral health by exploring multisensory interactions, social AI elements, and methods for evaluating user engagement.

One of the most promising avenues of artificial intelligence (AI) integration into medicine is its examination, evaluation, and characterization of pathological slides. The use of large language models (LLMs), the AI model subtype that is becoming increasingly popular, in pathological applications remains unexplored. This study investigates the histological image recognition capabilities of the multimodal models Gemini 1.5 Flash, ChatGPT-4o, and Claude 3.5 Sonnet and assesses their suitability for clinical or medical education use. The models were evaluated using 300 digital histology images derived from the University of South Florida Morsani College of Medicine Virtual Microscopy database, with a prompt to ascertain each model's ability to identify tissue type and plane of sectioning used. The images included the three subtypes in both longitudinal and transverse planes of sectioning. Standard machine learning metrics such as precision, recall, accuracy, and F1 score were used to classify and evaluate each model’s abilities. In the prediction of tissue type, OpenAI’s ChatGPT had the highest metrics with an F1 score of 0.772, while Claude yielded an F1 score of 0.380, and Gemini produced a 0.460 F1 score. In the prediction of sectioning, ChatGPT produced an F1 score of 0.396, while Claude produced a value of 0.472, and Gemini yielded 0.344. Overall, the results indicate that ChatGPT is most effective at identifying tissues. However, the inaccuracy demonstrated in evaluating sectioning compared to other models leaves room for improvement in its overall accuracy across varying tissue samples to reliably supplement medical education or clinical use.

Traumatic brain injury (TBI), particularly mild forms resulting from blast exposure, remains a diagnostic challenge among veterans due to delayed symptom onset and overlapping psychological conditions such as posttraumatic stress disorder. Current diagnostic methods rely heavily on subjective assessments, contributing to underdiagnosis and inconsistent care. Electroencephalography (EEG) offers a non-invasive, real-time measure of cortical activity with high temporal resolution, making it a promising tool for objective TBI assessment. This sub-study, nested within a randomized controlled trial evaluating hyperbaric oxygen therapy (HBOT) for veterans with TBI, investigates EEG as both a diagnostic modality and a therapeutic monitor. This sub-study adopts the same triple-blinded, randomized, parallel-group design as the parent trial. Participants are veterans with mild to moderate TBI, randomized to receive either HBOT or a sham treatment. EEG data are collected at three time points: baseline (pre-treatment), midpoint (18–21 dives), and post-treatment (2 weeks after completion). EEG recordings are performed during standardized cognitive and motor tasks using a 32-channel wireless headset. Primary outcomes include event-related potential amplitudes and alpha-band spectral power, analyzed for longitudinal changes and group differences. Secondary outcomes include latency measures, spectral power across additional frequency bands, and functional connectivity metrics. Data are modeled using repeated measures analysis to assess treatment effects and individual trajectories. This sub-study aims to validate EEG as a scalable and objective tool for diagnosing and monitoring TBI in clinical settings. By identifying electrophysiological signatures associated with injury severity and treatment response, EEG may enhance diagnostic precision and support personalized care strategies. The integration of EEG within a larger therapeutic trial framework allows for comprehensive evaluation of its clinical utility. Findings may inform future protocols for TBI assessment and contribute to the development of neurophysiological biomarkers that complement existing symptom-based approaches.

Anterior cruciate ligament (ACL) rupture is a critical concern in sports medicine. This study presents a detailed evaluation of machine learning (ML) techniques for the prediction of anterior cruciate ligament (ACL) injuries - a critical concern in sports medicine that often entails prolonged recovery periods and significant patient burden. Leveraging the transformative potential of artificial intelligence (AI) in medical applications, our work assesses eight distinct ML models (i.e., Support Vector Machines, Decision Tree Classifiers, Random Forest, Stochastic Gradient Descent, Logistic Regression, Gradient Boosting, Ridge Regression, and Linear Discriminant Analysis). Models were trained and tested on four datasets: 53-feature (Binary) ACL Rupture Biomechanical and Demographic (ARBD/BARBD) and 13-feature (Binary) ACL Rupture Wearable (ARW/BARW) that are readily accessible in real-life scenarios. Models were evaluated under a three-class schema ’pre-rupture,’ ’trial prior to rupture,’ ’rupture’ and then reclassified into a binary ’pre-rupture’ vs. ’elevated risk.’ Our analyses reveal that early-phase force metrics, particularly those recorded at 33 milliseconds (e.g., 33ms_Fx and 33ms_Fz) with ARBD and BARBD datasets and initial-contact forces (e.g., IC_Fx, IC_Fz) over demographic variables in ARW and BARW datasets, consistently emerge as significant predictors of injury across multiple and binary models. Notably, across the ARBD and ARW datasets, the ML models achieved accuracies ranging from approximately 79% to 87%, which improved markedly to a range of 92% to 95% when reclassified into a binary classification. These findings underscore the clinical relevance of early dynamic measurements and demonstrate the robustness and generalizability of our approach.