Exploring the potential of ChatGPT as a supplementary tool for providing orthopaedic information

Purpose: To investigate the potential use of large language models (LLMs) in orthopaedics by presenting queries pertinent to anterior cruciate ligament (ACL) surgery to generative pre-trained transformer (ChatGPT, specifically using its GPT-4 model of March 14th 2023). Additionally, this study aimed to evaluate the depth of the LLM’s knowledge and investigate its adaptability to different user groups. It was hypothesized that the ChatGPT would be able to adapt to different target groups due to its strong language understanding and processing capabilities. Methods: ChatGPT was presented with 20 questions and response was requested for two distinct target audiences: patients and non-orthopaedic medical doctors. Two board-certified orthopaedic sports medicine surgeons and two expert orthopaedic sports medicine surgeons independently evaluated the responses generated by ChatGPT. Mean correctness, completeness, and adaptability to the target audiences (patients and non-orthopaedic medical doctors) were determined. A three-point response scale facilitated nuanced assessment. Results: ChatGPT exhibited fair accuracy, with average correctness scores of 1.69 and 1.66 (on a scale from 0, incorrect, 1, partially correct, to 2, correct) for patients and medical doctors, respectively. Three of the 20 questions (15.0%) were deemed incorrect by any of the four orthopaedic sports medicine surgeon assessors. Moreover, overall completeness was calculated to be 1.51 and 1.64 for patients and medical doctors, respectively, while overall adaptiveness was determined to be 1.75 and 1.73 for patients and doctors, respectively. Conclusion: Overall, ChatGPT was successful in generating correct responses in approximately 65% of the cases related to ACL surgery. The findings of this study imply that LLMs offer potential as a supplementary tool for acquiring orthopaedic knowledge. However, although ChatGPT can provide guidance and effectively adapt to diverse target audiences, it cannot supplant the expertise of orthopaedic sports medicine surgeons in diagnostic and treatment planning endeavours due to its limited understanding of orthopaedic domains and its potential for erroneous responses. Level of evidence: V

Accelerated evidence synthesis in orthopaedics—the roles of natural language processing, expert annotation and large language models

peer reviewedIn an era of electronical medical records, rapidly expanding publication rates of medical knowledge, and large-scale registries, orthopaedics is in a dire need of innovative approaches to facilitate the adoption of the latest knowledge in clinical practice. While machine learning (ML) has been heralded as one solution to many research tasks hampered by previous technological limitations [12], there is an increasing need to direct our attention towards subdomains of ML that are convenient for the extraction of meaningful clinical information stored in medical records. We believe natural language processing (NLP) to be one such domain of ML, with an immense future potential to catalyse rate-limiting steps in orthopaedic research

A practical guide to the implementation of AI in orthopaedic research - part 1: opportunities in clinical application and overcoming existing challenges.

peer reviewedArtificial intelligence (AI) has the potential to transform medical research by improving disease diagnosis, clinical decision-making, and outcome prediction. Despite the rapid adoption of AI and machine learning (ML) in other domains and industry, deployment in medical research and clinical practice poses several challenges due to the inherent characteristics and barriers of the healthcare sector. Therefore, researchers aiming to perform AI-intensive studies require a fundamental understanding of the key concepts, biases, and clinical safety concerns associated with the use of AI. Through the analysis of large, multimodal datasets, AI has the potential to revolutionize orthopaedic research, with new insights regarding the optimal diagnosis and management of patients affected musculoskeletal injury and disease. The article is the first in a series introducing fundamental concepts and best practices to guide healthcare professionals and researcher interested in performing AI-intensive orthopaedic research studies. The vast potential of AI in orthopaedics is illustrated through examples involving disease- or injury-specific outcome prediction, medical image analysis, clinical decision support systems and digital twin technology. Furthermore, it is essential to address the role of human involvement in training unbiased, generalizable AI models, their explainability in high-risk clinical settings and the implementation of expert oversight and clinical safety measures for failure. In conclusion, the opportunities and challenges of AI in medicine are presented to ensure the safe and ethical deployment of AI models for orthopaedic research and clinical application. Level of evidence IV

ChatGPT can yield valuable responses in the context of orthopaedic trauma surgery

Purpose: To assess the possibility of using Generative Pretrained Transformer (ChatGPT) specifically in the context of orthopaedic trauma surgery by questions posed to ChatGPT and to evaluate responses (correctness, completeness and adaptiveness) by orthopaedic trauma surgeons. Methods: ChatGPT (GPT-4 of 12 May 2023) was asked to address 34 common orthopaedic trauma surgery-related questions and generate responses suited to three target groups: patient, nonorthopaedic medical doctor and expert orthopaedic surgeon. Three orthopaedic trauma surgeons independently assessed ChatGPT's responses by using a three-point response scale with a response range between 0 and 2, where a higher number indicates better performance (correctness, completeness and adaptiveness). Results: A total of 18 (52.9%) of all responses were assessed to be correct (2.0) for the patient target group, while 22 (64.7%) and 24 (70.5%) of the responses were determined to be correct for nonorthopaedic medical doctors and expert orthopaedic surgeons, respectively. Moreover, a total of 18 (52.9%), 25 (73.5%) and 28 (82.4%) of the responses were assessed to be complete (2.0) for patients, nonorthopaedic medical doctors and expert orthopaedic surgeons, respectively. The average adaptiveness was 1.93, 1.95 and 1.97 for patients, nonorthopaedic medical doctors and expert orthopaedic surgeons, respectively. Conclusion: The study results indicate that ChatGPT can yield valuable and overall correct responses in the context of orthopaedic trauma surgery across different target groups, which encompassed patients, nonorthopaedic medical surgeons and expert orthopaedic surgeons. The average correctness scores, completeness levels and adaptiveness values indicated the ability of ChatGPT to generate overall correct and complete responses adapted to the target group

Differences in postoperative knee function based on concomitant treatment of lateral meniscal injury in the setting of primary ACL reconstruction

Abstract Background Concomitant lateral meniscal (LM) injuries are common in acute anterior cruciate ligament (ACL) ruptures. However, the effect of addressing these injuries with various treatment methods during primary ACL reconstruction (ACLR) on patient-reported outcomes (PROs) is unknown. Therefore, the purpose of this study was to compare postoperative Knee injury and Osteoarthritis Outcome Score (KOOS) at 2-, 5-, and 10-years after isolated primary ACLR to primary ACLR with various treatment methods to address concomitant LM injury. Methods This study was based on data from the Swedish National Knee Ligament Registry. Patients ≥ 15 years with data on postoperative KOOS who underwent primary ACLR between the years 2005 and 2018 were included in this study. The study population was divided into five groups: 1) Isolated ACLR, 2) ACLR + LM repair, 3) ACLR + LM resection, 4) ACLR + LM injury left in situ, and 5) ACLR + LM repair + LM resection. Patients with concomitant medial meniscal or other surgically treated ligament injuries were excluded. Results Of 31,819 included patients, 24% had LM injury. After post hoc comparisons, significantly lower scores were found for the KOOS Symptoms subscale in ACLR + LM repair group compared to isolated ACLR (76.0 vs 78.3, p = 0.0097) and ACLR + LM injury left in situ groups (76.0 vs 78.3, p = 0.041) at 2-year follow-up. However, at 10-year follow-up, no differences were found between ACLR + LM repair and isolated ACLR, but ACLR + LM resection resulted in significantly lower KOOS Symptoms scores compared to isolated ACLR (80.4 vs 82.3, p = 0.041). Conclusion The results of this study suggest that LM injury during ACLR is associated with lower KOOS scores, particularly in the Symptoms subscale, at short- and long-term follow-up. However, this finding falls below minimal clinical important difference and therefore may not be clinically relevant. Level of Evidence III

Anatomic Flat Double-Bundle Medial Collateral Ligament Reconstruction

Several surgical techniques have been described to restore the anatomy of the medial collateral ligament, involving suture repair and reconstruction, with the latter having been associated with superior postoperative outcomes. Recently, a growing interest in anatomic isometric medial collateral ligament reconstruction (MCLR) has been developed, involving careful evaluation and finding the most appropriate location for the femoral placement of the allograft. Therefore, the purpose of this article is to describe anatomic MCLR aiming to restore medial knee stability by focusing on isometric positions within the native anatomy of the MCL

A practical guide to the implementation of AI in orthopaedic research – part 1: opportunities in clinical application and overcoming existing challenges

Abstract Artificial intelligence (AI) has the potential to transform medical research by improving disease diagnosis, clinical decision‐making, and outcome prediction. Despite the rapid adoption of AI and machine learning (ML) in other domains and industry, deployment in medical research and clinical practice poses several challenges due to the inherent characteristics and barriers of the healthcare sector. Therefore, researchers aiming to perform AI‐intensive studies require a fundamental understanding of the key concepts, biases, and clinical safety concerns associated with the use of AI. Through the analysis of large, multimodal datasets, AI has the potential to revolutionize orthopaedic research, with new insights regarding the optimal diagnosis and management of patients affected musculoskeletal injury and disease. The article is the first in a series introducing fundamental concepts and best practices to guide healthcare professionals and researcher interested in performing AI‐intensive orthopaedic research studies. The vast potential of AI in orthopaedics is illustrated through examples involving disease‐ or injury‐specific outcome prediction, medical image analysis, clinical decision support systems and digital twin technology. Furthermore, it is essential to address the role of human involvement in training unbiased, generalizable AI models, their explainability in high‐risk clinical settings and the implementation of expert oversight and clinical safety measures for failure. In conclusion, the opportunities and challenges of AI in medicine are presented to ensure the safe and ethical deployment of AI models for orthopaedic research and clinical application. Level of evidence I