Hss Journal最新文献

{"title":"The Impact of AI on the Development of Multimodal Wearable Devices in Musculoskeletal Medicine.","authors":"Gage Olson, Isabel Hansmann-Canas, Zahra Karimi, Amirhossein Yazdkhasti, Ghazal Shabestanipour, Hamid Ghaednia, Joseph H Schwab","doi":"10.1177/15563316251344945","DOIUrl":"10.1177/15563316251344945","url":null,"abstract":"<p><p>As wearables are becoming an increasingly important part of wellness and everyday life for many people, their potential in healthcare is also expanding, particularly in personalized and remote healthcare. However, many wearables lack sophistication, relying on simple sensors such as accelerometers and pulse meters to measure heart rate, body composition, and daily activity. Such basic metrics are insufficient for musculoskeletal disease diagnosis, which requires more detailed, multimodal neuromusculoskeletal monitoring. A major challenge in wearables development is the need for precise electromechanical signal measurements, which are difficult to obtain with low-cost systems. Artificial intelligence (AI) holds promise in addressing these analytical challenges and enabling the creation of affordable, sophisticated wearables. While AI has been used for decades in engineering, its clinical application is still emerging, creating an opportunity for the development of AI-enhanced wearables capable of clinical diagnosis. AI can enhance data generated by various sensor types in wearable devices (such as accelerometers, electrical, optical, and acoustic sensors), enabling clinicians to monitor and diagnose complex conditions that require multiple sensing modalities. This review explores current wearable technologies, ongoing research in AI-enhanced wearables, the potential for AI to advance wearable technologies in healthcare, and the future directions in the development of multimodal wearables.</p>","PeriodicalId":35357,"journal":{"name":"Hss Journal","volume":" ","pages":"15563316251344945"},"PeriodicalIF":1.6,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12158963/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robotic-Assisted Arthroscopy Promises Enhanced Procedural Efficiency, Visualization, and Control but Must Overcome Barriers to Adoption.","authors":"Kyle N Kunze, David Ferguson, Ayoosh Pareek, Nicholas Colyvas","doi":"10.1177/15563316251340983","DOIUrl":"10.1177/15563316251340983","url":null,"abstract":"<p><p>Robotic-assisted surgery is now well-established in spine surgery and total joint arthroplasty, but its application to arthroscopy has only recently emerged in the context of advances in artificial intelligence (AI) and robotic technology. This new application addresses limitations of conventional arthroscopy, including constrained depth perception, variation in technique or anatomy leading to inaccuracies, manual fluid management adjustments, and limitations in dexterity due to the requirement that one hand is occupied by the arthroscope. Early preclinical and cadaveric studies demonstrate submillimeter precision and improved anatomic accuracy in procedures such as anterior cruciate ligament reconstruction, but widespread clinical adoption remains limited by regulatory, economic, and training hurdles. This review article synthesizes the capabilities and applications of current robotic-assisted arthroscopy platforms, surveys the landscape of available technologies, and examines barriers to adoption, thereby looking ahead to the potential use of this technology in redefining arthroscopic surgery.</p>","PeriodicalId":35357,"journal":{"name":"Hss Journal","volume":" ","pages":"15563316251340983"},"PeriodicalIF":1.6,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12126459/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144209702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bioethical Considerations of Deploying Artificial Intelligence in Clinical Orthopedic Settings: A Narrative Review.","authors":"Lulla V Kiwinda, Sophia D Kocher, Anna R Bryniarski, Christian A Pean","doi":"10.1177/15563316251340303","DOIUrl":"10.1177/15563316251340303","url":null,"abstract":"<p><p>Artificial intelligence (AI) has emerged in orthopedics with the potential to improve diagnostic accuracy, optimize surgical workflows, and support personalized care. We conducted a narrative review exploring the bioethical considerations of AI use in the orthopedic clinical setting, focusing on 4 core principles-autonomy, beneficence, nonmaleficence, and justice-to provide orthopedists with a practical framework for AI's implementation. We utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework to conduct a comprehensive PubMed search; 89 articles were evaluated and 23 met our inclusion criteria. Across these studies, bioethical considerations for the clinical implementation of AI tools consistently emerged, most commonly concerning privacy, bias, transparency, informed consent, and regulation. We offer recommendations for strengthening privacy safeguards, adopting bias mitigation strategies, improving transparency through explainable AI tools, and establishing clear regulatory frameworks with lifecycle evaluation.</p>","PeriodicalId":35357,"journal":{"name":"Hss Journal","volume":" ","pages":"15563316251340303"},"PeriodicalIF":1.6,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12126464/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144209701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing Musculoskeletal Care Using AI and Digital Health Applications: A Review of Commercial Solutions.","authors":"Johannes Pawelczyk, Moritz Kraus, Sebastian Voigtlaender, Sebastian Siebenlist, Marco-Christopher Rupp","doi":"10.1177/15563316251341321","DOIUrl":"10.1177/15563316251341321","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":35357,"journal":{"name":"Hss Journal","volume":" ","pages":"15563316251341321"},"PeriodicalIF":1.6,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12126469/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144209700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Language Models in Spine Surgery: A Promising Technology.","authors":"Romil Shah, Joseph H Schwab","doi":"10.1177/15563316251340696","DOIUrl":"10.1177/15563316251340696","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":35357,"journal":{"name":"Hss Journal","volume":" ","pages":"15563316251340696"},"PeriodicalIF":1.6,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12125007/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in the Diagnosis and Prognostication of the Musculoskeletal Patient.","authors":"Miguel M Girod, Sami Saniei, Marisa N Ulrich, Lainey G Bukowiec, Kellen L Mulford, Michael J Taunton, Cody C Wyles","doi":"10.1177/15563316251339660","DOIUrl":"10.1177/15563316251339660","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":35357,"journal":{"name":"Hss Journal","volume":" ","pages":"15563316251339660"},"PeriodicalIF":1.6,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12119539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The State of Telemedicine, Telerehabilitation, and Virtual Care in Musculoskeletal Health: A Narrative Review.","authors":"Mitchell A Johnson, Tyler Khilnani, Abigail Hyun, Troy B Amen, Nathan H Varady, Benedict U Nwachukwu, Joshua S Dines","doi":"10.1177/15563316251341229","DOIUrl":"10.1177/15563316251341229","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":35357,"journal":{"name":"Hss Journal","volume":" ","pages":"15563316251341229"},"PeriodicalIF":1.6,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12119517/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in Value-Based Health Care.","authors":"Romil Shah, Kevin J Bozic, Prakash Jayakumar","doi":"10.1177/15563316251340074","DOIUrl":"10.1177/15563316251340074","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":35357,"journal":{"name":"Hss Journal","volume":" ","pages":"15563316251340074"},"PeriodicalIF":1.6,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12119536/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Orthopedic Residency Programs: What are Our Current Goals? An International Society of Orthopedic Centers (ISOC) Delphi Consensus.","authors":"David Figueroa, Luis Moya, José Arteaga, Alex Vaisman, Mathias Bostrom, Carolina Acuña, Domenico Alesi, Fernando Radice, Francisco Figueroa, Felipe Toro, Meir Liebergall, Mark Stegeman, Magnus Tagil, Mario Lenza, Parag Sancheti, Amar Ranawat, Rafael Calvo, Rodrigo Guiloff, Laura Robbins, Sebastian Irarrazaval, Stefano Zaffagnini, Tobias Jung, Tobias Winkler","doi":"10.1177/15563316251337359","DOIUrl":"10.1177/15563316251337359","url":null,"abstract":"","PeriodicalId":35357,"journal":{"name":"Hss Journal","volume":" ","pages":"15563316251337359"},"PeriodicalIF":1.6,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12119527/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence and Digital Applications in Musculoskeletal Healthcare: Ready or Not, Here It Comes!","authors":"Kyle N Kunze","doi":"10.1177/15563316251341314","DOIUrl":"10.1177/15563316251341314","url":null,"abstract":"","PeriodicalId":35357,"journal":{"name":"Hss Journal","volume":" ","pages":"15563316251341314"},"PeriodicalIF":1.6,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12092396/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144128965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}