Anterior cruciate ligament (ACL) instability poses a considerable challenge in traumatology and orthopedic medicine, demanding precise diagnostics for optimal treatment. The pivot-shift test, a pivotal assessment tool, relies on subjective interpretation, emphasizing the need for supplementary imaging. This study addresses this limitation by introducing a machine learning classification algorithm integrated into a mobile application, leveraging smartphones’ built-in inertial sensors for dynamic rotational stability assessment during knee examinations. Orthopedic specialists conducted knee evaluations on a cohort of 52 subjects, yielding valuable insights. Quantitative analyses, employing the Intraclass Correlation Coefficient (ICC), demonstrated robust agreement in both intraobserver and interobserver assessments. Specifically, ICC values of 0.94 reflected strong concordance in the timing between maneuvers, while signal amplitude exhibited consistency, with the ICC ranging from 0.71 to 0.66. The introduced machine learning algorithms proved effective, accurately classifying 90% of cases exhibiting joint hypermobility. These quantifiable results underscore the algorithm’s reliability in assessing knee stability. This study emphasizes the practicality and effectiveness of implementing machine learning algorithms within a mobile application, showcasing its potential as a valuable tool for categorizing signals captured by smartphone inertial sensors during the pivot-shift test.

Objective: To systematically evaluate the performance of artificial intelligence (AI) and machine learning (ML) models for the detection and classification of dental caries across different imaging modalities. Review methodology: A systematic literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and Google Scholar covering publications from January 2015 through March 2026. Studies reporting quantitative performance metrics for AI/ML models applied to caries detection, classification, or segmentation were included. A three-stage screening process (title, abstract, full-text) was applied following PRISMA guidelines. Results: A total of 45 studies were included. Deep learning, particularly convolutional neural networks, constituted the predominant approach (~69%). The most employed architectures include the YOLO family (v3–v11), U-Net variants, and transfer-learning classifiers (ResNet, VGG, DenseNet, EfficientNet, MobileNet). Reported accuracy ranged from 73.3% to 98.6%, with pooled meta-analytic sensitivity of 0.85 and specificity of 0.90. Only 12% of studies used public datasets. Conclusions: AI/ML systems demonstrate substantial diagnostic potential for dental caries, frequently matching clinician performance. Critical gaps persist regarding multicenter validation, clinically interpretable models, and prospective trials evaluating patient outcomes.