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Background: Artificial intelligence (AI) has already revolutionized the analysis of image, text, and tabular data, bringing significant advances across many medical sectors. Now, by combining with wearable inertial measurement units (IMUs), AI could transform health care again by opening new opportunities in patient care and medical research., Objective: This systematic review aims to evaluate the integration of AI models with wearable IMUs in health care, identifying current applications, challenges, and future opportunities. The focus will be on the types of models used, the characteristics of the datasets, and the potential for expanding and enhancing the use of this technology to improve patient care and advance medical research., Methods: This study examines this synergy of AI models and IMU data by using a systematic methodology, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, to explore 3 core questions: (1) Which medical fields are most actively researching AI and IMU data? (2) Which models are being used in the analysis of IMU data within these medical fields? (3) What are the characteristics of the datasets used for in this fields?, Results: The median dataset size is of 50 participants, which poses significant limitations for AI models given their dependency on large datasets for effective training and generalization. Furthermore, our analysis reveals the current dominance of machine learning models in 76% on the surveyed studies, suggesting a preference for traditional models like linear regression, support vector machine, and random forest, but also indicating significant growth potential for deep learning models in this area. Impressively, 93% of the studies used supervised learning, revealing an underuse of unsupervised learning, and indicating an important area for future exploration on discovering hidden patterns and insights without predefined labels or outcomes. In addition, there was a preference for conducting studies in clinical settings (77%), rather than in real-life scenarios, a choice that, along with the underapplication of the full potential of wearable IMUs, is recognized as a limitation in terms of practical applicability. Furthermore, the focus of 65% of the studies on neurological issues suggests an opportunity to broaden research scope to other clinical areas such as musculoskeletal applications, where AI could have significant impacts., Conclusions: In conclusion, the review calls for a collaborative effort to address the highlighted challenges, including improvements in data collection, increasing dataset sizes, a move that inherently pushes the field toward the adoption of more complex deep learning models, and the expansion of the application of AI models on IMU data methodologies across various medical fields. This approach aims to enhance the reliability, generalizability, and clinical applicability of research findings, ultimately improving patient outcomes and advancing medical research., (©Ricardo Smits Serena, Florian Hinterwimmer, Rainer Burgkart, Rudiger von Eisenhart-Rothe, Daniel Rueckert. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 29.01.2025.)

Introduction: Intra-articular injections, commonly used in osteoarthritis treatment, are debated due to their potential link to septic arthritis, though its incidence remains low. Lidocaine, used as a "carrier" for therapeutic substances like hyaluronan or triamcinolone, has pain-relieving and antimicrobial properties. This study investigates the concentration-dependent antimicrobial effects of lidocaine in combination with hyaluronan and triamcinolone in both standard and synovial fluid cultures., Methods: The antimicrobial efficacy of lidocaine against Staphylococcus aureus was investigated, with variations in bacterial and lidocaine concentrations. Bacterial growth was monitored using a UV/VIS spectrometer at 600 nm. Lidocaine solutions of 1% and 2% were tested, both alone and in combination with hyaluronic acid or Triam40, in tryptic soy broth (TSB), to reflect knee joint applications. The groups included pure lidocaine (L), Triam (T), hyaluronan (H), and combinations (LT, LH, TH, LTH) with 1% or 2% lidocaine. A bacterial inoculum of 300 CFU/mL was used, and samples were incubated for 12 and 24 h. Additional tests were conducted on Staphylococcus epidermidis and methicillin-resistant Staphylococcus aureus (MRSA), as well as on S. aureus in human synovial fluid., Results: Lidocaine showed a concentration-dependent antimicrobial effect, with greater inhibition at higher concentrations and lower bacterial densities. All lidocaine-containing combinations significantly reduced the bacterial levels of S. aureus in TSB. Similar results were seen for S. epidermidis and MRSA , with notable inhibition in synovial fluid after 12 h, especially with 2% lidocaine., Conclusions: Lidocaine exhibits dose-dependent antimicrobial effects against key pathogens responsible for septic arthritis. Its combination with Triam40 and hyaluronan may reduce the risk of septic arthritis, supporting its clinical relevance.