Hss Journal最新文献

Artificial intelligence (AI) and digital health (DH) solutions are reshaping musculoskeletal (MSK) care across diagnostics, treatment planning, workflow optimization, and administrative burden reduction. AI-enabled triage systems enhance patient flow efficiency, while automated scheduling, symptom checkers, and AI-powered virtual assistants streamline pre-visit interactions. In MSK radiographic diagnostics, AI augments imaging interpretation, enabling automated fracture detection, opportunistic screening, and quantitative imaging, improving diagnostic accuracy and standardization. Preoperative planning solutions facilitate implant templating, surgical navigation, and patient-specific instrumentation, reducing variability and enhancing surgical precision. Concurrently, digital scribes and AI-driven documentation tools alleviate administrative overhead, mitigating clinician burnout and enabling refocused patient engagement. Predictive analytics optimize treatment pathways by leveraging multimodal patient data for risk stratification and personalized decision support. However, algorithmic bias, model generalizability, regulatory hurdles, and legal ambiguities present substantial implementation barriers, necessitating rigorous validation, adaptive governance, and seamless clinical integration. The U.S. and EU regulatory landscapes diverge in their approaches to AI oversight, with the former favoring expedited market access and the latter imposing stringent compliance mandates under the EU AI Act. AI's integration into MSK care demands robust validation frameworks, standardized interoperability protocols, and dynamic regulatory pathways balancing safety and innovation. Emerging generalist foundation models, open-source large language models (LLMs), and specialized AI-driven medical applications herald a paradigm shift toward precision MSK care. These innovations will require prospective clinical validation to ensure patient benefit and mitigate risk. Addressing ethical considerations, ensuring equitable access, and fostering interdisciplinary collaboration remain paramount in translating AI's potential into tangible improvements in MSK healthcare delivery.

Large language models (LLMs) offer potential applications across medical specialties; in spine surgery, opportunities exist to enhance patient care, streamline research, and improve clinical practice. This review explores the current and potential applications of LLMs in spine surgery, assessing their possibilities and limitations across patient education, research, clinical practice, and perioperative assistance.

As artificial intelligence (AI) advances in healthcare, encompassing robust applications for the diagnosis and prognostication of musculoskeletal diseases, clinicians must increasingly understand the implications of machine learning and deep learning in their practice. This review article explores computer vision algorithms and patient-specific, multimodal prediction models; provides a simple framework to guide discussion on the limitations of AI model development; and introduces the field of generative AI.

Telemedicine has become an increasingly important component of musculoskeletal care, with recent advances in virtual physical examinations, enhanced patient education, and expanded access to treatment and telerehabilitation. Emerging applications of artificial intelligence, including virtual triaging and remote patient monitoring, promise to further augment telemedicine's effectiveness and scope. Despite limitations and a continued preference for in-person visits among some patients, telemedicine can be a valuable tool for musculoskeletal health practitioners, offering new ways to deliver high-quality, timely, and cost-effective care.

Artificial intelligence (AI) presents new opportunities to advance value-based healthcare in orthopedic surgery through 3 potential mechanisms: agency, automation, and augmentation. AI may enhance patient agency through improved health literacy and remote monitoring while reducing costs through triage and reduction in specialist visits. In automation, AI optimizes operating room scheduling and streamlines administrative tasks, with documented cost savings and improved efficiency. For augmentation, AI has been shown to be accurate in diagnostic imaging interpretation and surgical planning, while enabling more precise outcome predictions and personalized treatment approaches. However, implementation faces substantial challenges, including resistance from healthcare professionals, technical barriers to data quality and privacy, and significant financial investments required for infrastructure. Success in healthcare AI integration requires careful attention to regulatory frameworks, data privacy, and clinical validation.

Background:The proliferation of artificial intelligence has led to widespread patient use of large language models (LLMs). Purpose: We sought to characterize LLM responses to questions about piriformis syndrome (PS). Methods: On August 15, 2024, we asked 3 LLMs-ChatGPT-4, Copilot, and Gemini-to respond to the 25 most frequently asked questions about PS, as tracked by Google Trends. We evaluated the accuracy and completeness of the responses according to the Likert scale. We used the Ensuring Quality Information for Patients (EQIP) tool to assess the quality of the responses and assessed readability using Flesch-Kincaid Reading Ease (FKRE) and Flesch-Kincaid Grade Level (FKGL) scores. Results: The mean completeness scores of the responses obtained from ChatGPT, Copilot, and Gemini were 2.8 ± 0.3, 2.2 ± 0.6, and 2.6 ± 0.4, respectively. There was a significant difference in the mean completeness score among LLMs. In pairwise comparisons, ChatGPT and Gemini were superior to Copilot. There was no significant difference between the LLMs in terms of mean accuracy scores. In readability analyses, no significant difference was found in terms of FKRE scores. However, a significant difference was found in FKGL scores. A significant difference between LLMs was identified in the quality analysis performed according to EQIP scores. Conclusion: Although the use of LLMs in healthcare is promising, our findings suggest that these technologies need to be improved to perform better in terms of accuracy, completeness, quality, and readability on PS for a general audience.

Artificial intelligence (AI) has emerged as a transformative force in orthopedic surgery. Potentially encompassing pre-, intra-, and postoperative processes, it can process complex medical imaging, provide real-time surgical guidance, and analyze large datasets for outcome prediction and optimization. AI has shown improvements in surgical precision, efficiency, and patient outcomes across orthopedic subspecialties, and large language models and agentic AI systems are expanding AI utility beyond surgical applications into areas such as clinical documentation, patient education, and autonomous decision support. The successful implementation of AI in orthopedic surgery requires careful attention to validation, regulatory compliance, and healthcare system integration. As these technologies continue to advance, maintaining the balance between innovation and patient safety remains crucial, with the ultimate goal of achieving more personalized, efficient, and equitable healthcare delivery while preserving the essential role of human clinical judgment. This review examines the current landscape and future trajectory of AI applications in orthopedic surgery, highlighting both technological advances and their clinical impact. Studies have suggested that AI-assisted procedures achieve higher accuracy and better functional outcomes compared to conventional methods, while reducing operative times and complications. However, these technologies are designed to augment rather than replace clinical expertise, serving as sophisticated tools to enhance surgeons' capabilities and improve patient care.

Background: Essential amino acid (EAA) supplementation, including conditionally essential amino acid (CEAA) and branched-chain amino acids (BCAA) supplementation, has been suggested as a mechanism to optimize patient outcomes by counteracting the atrophy associated with orthopedic procedures. Purpose: We sought to investigate the effect of EAA supplementation in the perioperative period on patients undergoing orthopedic and spine surgery, specifically whether it is associated with (1) reductions in postoperative muscle atrophy and (2) improved postoperative function including range of motion, strength, and mobility. Methods: We conducted a systematic review of the literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used, and the protocol was registered in the Prospective Register of Systematic Reviews (PROSPERO) database (CRD42023447774). Studies of interest were prospective, placebo-controlled, randomized clinical trials (RCTs) published between 2002 and 2023 evaluating the impact of EAA supplementation on patients undergoing orthopedic and spine surgery. Results: Ten RCTs evaluating EAA supplementation in trauma, adult reconstruction, and spine surgery were identified; half of these focused on adult reconstruction. The EAA supplementation dose (3.4-20 g), frequency (daily to 3 times per day), and duration (14-49 days) varied widely across studies. Seven studies reported parameters relating to muscle size and/or composition, with 3 studies reporting superior muscle size/composition in patients receiving perioperative EAA supplementation, when compared with controls. Three studies reported favorable mobility outcomes for patients receiving EAA. Meta-analysis was prohibited by variation in measurement and outcome variables across the studies. Conclusions: Pooled data from level I studies supports the use of EAA, BCAA, and CEAA supplementations across several orthopedic subspecialties. However, significant heterogeneity exists in the quantity, duration, and content of EAA administered. Further prospective studies are needed to determine optimal/standardized parameters for supplementation.