Applications of artificial intelligence in orthopaedic surgery

Immersive teaching using virtual reality technology to improve ophthalmic surgical skills for medical postgraduate students

Medical education is primarily based on practical schooling and the accumulation of experience and skills, which is important for the growth and development of young ophthalmic surgeons. However, present learning and refresher methods are constrained by several factors. Nevertheless, virtual reality (VR) technology has considerably contributed to medical training worldwide, providing convenient and practical auxiliary value for the selection of students' sub-majors. Moreover, it offers previously inaccessible surgical step training, scenario simulations, and immersive evaluation exams. This paper outlines the current applications of VR immersive teaching methods for ophthalmic surgery interns.

Guideline for RSA and CT-RSA implant migration measurements: an update of standardizations and recommendations

Opening remarks: These guidelines are the result of discussions within a diverse group of RSA researchers. They were approved in December 2023 by the board and selected members of the International Radiostereometry Society to update the guidelines by Valstar et al. [1]. By adhering to these guidelines, RSA studies will become more transparent and consistent in execution, presentation, reporting, and interpretation. Both authors and reviewers of scientific papers using RSA may use these guidelines, summarized in the Checklist, as a reference. Deviations from these guidelines should have the underlying rationale stated.

Recommended Requirements and Essential Elements for Proper Reporting of the Use of Artificial Intelligence Machine Learning Tools in Biomedical Research and Scientific Publications

Essential elements required for proper use of artificial intelligence machine learning tools in biomedical research and scientific publications include (1) explanation justifying why a machine learning approach contributes to the purpose of the study; (2) description of the adequacy of the data (input) to produce the desired results (output); (3) details of the algorithmic (i.e., computational) approach including methods for organizing the data (preprocessing); the machine learning computational algorithm(s) assessed; on what data the models were trained; the presence of bias and efforts to mitigate these effects; and the methods for quantifying the variables (features) most influential in determining the results (e.g., Shapley values); (4) description of methods, and reporting of results, quantitating performance in terms of both model accuracy and model calibration (level of confidence in the model's predictions); (5) availability of the programming code (including a link to the code when available-ideally, the code should be available); (6) discussion of model internal validation (results applicable and sensitive to the population investigated and data on which the model was trained) and external validation (were the results investigated as to whether they are generalizable to different populations? If not, consideration of this limitation and discussion of plans for external validation, i.e., next steps). As biomedical research submissions using artificial intelligence technology increase, these requirements could facilitate purposeful use and comprehensive methodological reporting.

Use and Application of Large Language Models for Patient Questions Following Total Knee Arthroplasty

BACKGROUND

A consumer-focused health care model not only allows unprecedented access to information, but equally warrants consideration of the appropriateness of providing accurate patient health information. Nurses play a large role in influencing patient satisfaction following total knee arthroplasty (TKA), but they come at a cost. A specific natural language artificial intelligence (AI) model, ChatGPT (Chat Generative Pre-trained Transformer), has accumulated over 100 million users within months of launching. As such, we aimed to compare: (1) orthopaedic surgeons' evaluation of the appropriateness of the answers to the most frequently asked patient questions after TKA; and (2) patients' comfort level in answering their postoperative questions by using answers provided by arthroplasty-trained nurses and ChatGPT.

METHODS

We prospectively created 60 questions based on the most commonly asked patient questions following TKA. There were 3 fellowship-trained surgeons who assessed the answers provided by arthroplasty-trained nurses and ChatGPT-4 to each of the questions. The surgeons graded each set of responses based on clinical judgment as: (1) "appropriate," (2) "inappropriate" if the response contained inappropriate information, or (3) "unreliable," if the responses provided inconsistent content. Patients' comfort level and trust in AI were assessed using Research Electronic Data Capture (REDCap) hosted at our local hospital.

RESULTS

The surgeons graded 44 out of 60 (73.3%) responses for the arthroplasty-trained nurses and 44 out of 60 (73.3%) for ChatGPT to be "appropriate." There were 4 responses graded "inappropriate" and one response graded "unreliable" provided by the nurses. For the ChatGPT response, there were 5 responses graded "inappropriate" and no responses graded "unreliable." There were 136 patients (53.8%) who were more comfortable with the answers provided by ChatGPT compared to 86 patients (34.0%) who preferred the answers from arthroplasty-trained nurses. Of the 253 patients, 233 (92.1%) were uncertain if they would trust AI to answer their postoperative questions. There were 127 patients (50.2%) who answered that if they knew the previous answer was provided by ChatGPT, their comfort level in trusting the answer would change.

CONCLUSIONS

One potential use of ChatGPT can be found in providing appropriate answers to patient questions after TKA. At our institution, cost expenditures can potentially be minimized while maintaining patient satisfaction. Inevitably, successful implementation is dependent on the ability to provide information that is credible and in accordance with the objectives of both physicians and patients.

LEVEL OF EVIDENCE

III.

Biomechanical comparison of two surgical methods for Hallux Valgus deformity: Exploring the use of artificial neural networks as a decision-making tool for orthopedists

Hallux Valgus foot deformity affects gait performance. Common treatment options include distal oblique metatarsal osteotomy and chevron osteotomy. Nonetheless, the current process of selecting the appropriate osteotomy method poses potential biases and risks, due to its reliance on subjective human judgment and interpretation. The inherent variability among clinicians, the potential influence of individual clinical experiences, or inherent measurement limitations may contribute to inconsistent evaluations. To address this, incorporating objective tools like neural networks, renowned for effective classification and decision-making support, holds promise in identifying optimal surgical approaches. The objective of this cross-sectional study was twofold. Firstly, it aimed to investigate the feasibility of classifying patients based on the type of surgery. Secondly, it sought to explore the development of a decision-making tool to assist orthopedists in selecting the optimal surgical approach. To achieve this, gait parameters of twenty-three women with moderate to severe Hallux Valgus were analyzed. These patients underwent either distal oblique metatarsal osteotomy or chevron osteotomy. The parameters exhibiting differences in preoperative and postoperative values were identified through various statistical tests such as normalization, Shapiro-Wilk, non-parametric Wilcoxon, Student t, and paired difference tests. Two artificial neural networks were constructed for patient classification based on the type of surgery and to simulate an optimal surgery type considering postoperative walking speed. The results of the analysis demonstrated a strong correlation between surgery type and postoperative gait parameters, with the first neural network achieving a remarkable 100% accuracy in classification. Additionally, cases were identified where there was a mismatch with the surgeon's decision. Our findings highlight the potential of artificial neural networks as a complementary tool for surgeons in making informed decisions. Addressing the study's limitations, future research may investigate a wider range of orthopedic procedures, examine additional gait parameters and use more diverse and extensive datasets to enhance statistical robustness.

Predicting Functional Outcomes of Total Hip Arthroplasty Using Machine Learning: A Systematic Review

The aim of this review was to assess the reliability of machine learning (ML) techniques to predict the functional outcome of total hip arthroplasty. The literature search was performed up to October 2023, using MEDLINE/PubMed, Embase, Web of Science, and NIH Clinical Trials. Level I to IV evidence was included. Seven studies were identified that included 44,121 patients. The time to follow-up varied from 3 months to more than 2 years. Each study employed one to six ML techniques. The best-performing models were for health-related quality of life (HRQoL) outcomes, with an area under the curve (AUC) of more than 84%. In contrast, predicting the outcome of hip-specific measures was less reliable, with an AUC of between 71% to 87%. Random forest and neural networks were generally the best-performing models. Three studies compared the reliability of ML with traditional regression analysis: one found in favour of ML, one was not clear and stated regression closely followed the best-performing ML model, and one showed a similar AUC for HRQoL outcomes but did show a greater reliability for ML to predict a clinically significant change in the hip-specific function. ML offers acceptable-to-excellent discrimination of predicting functional outcomes and may have a marginal advantage over traditional regression analysis, especially in relation to hip-specific hip functional outcomes.

Digitalization in orthopaedics: a narrative review

Advances in technology and digital tools like the Internet of Things (IoT), artificial intelligence (AI), and sensors are shaping the field of orthopaedic surgery on all levels, from patient care to research and facilitation of logistic processes. Especially the COVID-19 pandemic, with the associated contact restrictions was an accelerator for the development and introduction of telemedical applications and digital alternatives to classical in-person patient care. Digital applications already used in orthopaedic surgery include telemedical support, online video consultations, monitoring of patients using wearables, smart devices, surgical navigation, robotic-assisted surgery, and applications of artificial intelligence in forms of medical image processing, three-dimensional (3D)-modelling, and simulations. In addition to that immersive technologies like virtual, augmented, and mixed reality are increasingly used in training but also rehabilitative and surgical settings. Digital advances can therefore increase the accessibility, efficiency and capabilities of orthopaedic services and facilitate more data-driven, personalized patient care, strengthening the self-responsibility of patients and supporting interdisciplinary healthcare providers to offer for the optimal care for their patients.

The Era of Digital Orthopedics: A Bone or Bane?

Orthopedics, the medical specialty dedicated to diagnosing, treating, and preventing disorders of the musculoskeletal system, has long been a cornerstone of healthcare. With an aging population and an increasing emphasis on maintaining an active lifestyle, the demand for orthopedic care is on the rise. However, the field of orthopedics is rapidly evolving, and one of the most significant developments in recent years is the emergence of digital orthopedics [1, 2]. This transformation is reshaping the way orthopedic care is delivered, from diagnosis and treatment to patient outcomes and beyond. In this editorial, we explore the concept of digital orthopedics, its implications, and the potential benefits it offers to both patients and health-care professionals.

Artificial Intelligence Image Recognition System for Preventing Wrong-Site Upper Limb Surgery

Our image recognition system employs a deep learning model to differentiate between the left and right upper limbs in images, allowing doctors to determine the correct surgical position. From the experimental results, it was found that the precision rate and the recall rate of the intelligent image recognition system for preventing wrong-site upper limb surgery proposed in this paper could reach 98% and 93%, respectively. The results proved that our Artificial Intelligence Image Recognition System (AIIRS) could indeed assist orthopedic surgeons in preventing the occurrence of wrong-site left and right upper limb surgery. At the same time, in future, we will apply for an IRB based on our prototype experimental results and we will conduct the second phase of human trials. The results of this research paper are of great benefit and research value to upper limb orthopedic surgery.

ChatGPT and large language models in orthopedics: from education and surgery to research

ChatGPT has quickly popularized since its release in November 2022. Currently, large language models (LLMs) and ChatGPT have been applied in various domains of medical science, including in cardiology, nephrology, orthopedics, ophthalmology, gastroenterology, and radiology. Researchers are exploring the potential of LLMs and ChatGPT for clinicians and surgeons in every domain. This study discusses how ChatGPT can help orthopedic clinicians and surgeons perform various medical tasks. LLMs and ChatGPT can help the patient community by providing suggestions and diagnostic guidelines. In this study, the use of LLMs and ChatGPT to enhance and expand the field of orthopedics, including orthopedic education, surgery, and research, is explored. Present LLMs have several shortcomings, which are discussed herein. However, next-generation and future domain-specific LLMs are expected to be more potent and transform patients' quality of life.

Revolutionizing orthopedics: a comprehensive review of robot-assisted surgery, clinical outcomes, and the future of patient care

Robotic-assisted orthopedic surgery (RAOS) is revolutionizing the field, offering the potential for increased accuracy and precision and improved patient outcomes. This comprehensive review explores the historical perspective, current robotic systems, advantages and limitations, clinical outcomes, patient satisfaction, future developments, and innovation in RAOS. Based on systematic reviews, meta-analyses, and recent studies, this article highlights the most significant findings and compares RAOS to conventional techniques. As robotic-assisted surgery continues to evolve, clinicians and researchers must stay informed and adapt their practices to provide optimal patient care. Evidence from published studies corroborates these claims, highlighting superior component positioning, decreased incidence of complications, and heightened patient satisfaction. However, challenges such as costs, learning curves, and technical issues must be resolved to fully capitalize on these advantages.

Leveraging Artificial Intelligence and Machine Learning in Regenerative Orthopedics: A Paradigm Shift in Patient Care

The integration of artificial intelligence (AI) and machine learning (ML) into regenerative orthopedics heralds a paradigm shift in clinical methodologies and patient management. This review article scrutinizes AI's role in augmenting diagnostic accuracy, refining predictive models, and customizing patient care in orthopedic medicine. Focusing on innovations such as KeyGene and CellNet, we illustrate AI's adeptness in navigating complex genomic datasets, cellular differentiation, and scaffold biodegradation, which are critical components of tissue engineering. Despite its transformative potential, AI's clinical adoption remains in its infancy, contending with challenges in validation, ethical oversight, and model training for clinical relevance. This review posits AI as a vital complement to human intelligence (HI), advocating for an interdisciplinary approach that merges AI's computational prowess with medical expertise to fulfill precision medicine's promise. By analyzing historical and contemporary developments in AI, from the foundational theories of McCullough and Pitts to sophisticated neural networks, the paper emphasizes the need for a synergistic alliance between AI and HI. This collaboration is imperative for improving surgical outcomes, streamlining therapeutic modalities, and enhancing the quality of patient care. Our article calls for robust interdisciplinary strategies to overcome current obstacles and harness AI's full potential in revolutionizing patient outcomes, thereby significantly contributing to the advancement of regenerative orthopedics and the broader field of scientific research.

Transforming Orthopedic Joint Surgeries: The Role of Artificial Intelligence (AI) and Robotics

The landscape of orthopedic joint surgeries, specifically total hip arthroplasty (THA) and total knee arthroplasty (TKA), is rapidly changing, and artificial intelligence (AI) along with robotics is at the helm of this transformation. These technologies, working synergistically, have introduced unprecedented levels of precision and personalization to surgical procedures, thereby significantly enhancing patient outcomes. In this editorial, we explore the changing perspectives of orthopedic surgeons toward AI and robotics and dissect the incorporation of these technologies in surgeries, their associated advantages, their inherent limitations, and potential future prospects. We draw from a host of recent studies to provide a comprehensive understanding of how these transformative technologies can augment surgical performance and patient care.

Artificial intelligence in orthopaedic surgery

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as 'big data', AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI's limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction.