Current understanding on artificial intelligence and machine learning in orthopaedics - A scoping review

The Performance of a Customized Generative Pre-trained Transformer on the American Society for Surgery of the Hand Self-Assessment Examination

INTRODUCTION

 Multimodal large language models (MLLMs), such as OpenAI's ChatGPT (San Francisco, CA), have the potential to improve medical care delivery and education, although important shortcomings in accuracy and image interpretation have been noted. The aim of this study was to assess the multimodal performance of a ChatGPT model customized with hand surgery-specific knowledge.

METHODS

A customized generative pre-trained transformer (GPT) was trained using peer-reviewed literature recommended by the American Society for Surgery of the Hand (ASSH). Questions were taken from the ASSH 2022 Self-Assessment Examination (SAE). GPT-4 and the customized GPT were asked text-based multiple-choice questions. The customized GPT was also asked image-containing questions, both with and without access to the image(s) associated with each question.

RESULTS

 A total of 192 questions were included. The customized GPT responded to the 119 text-only questions with greater accuracy than GPT-4 (107 (89.9%) versus 91 (76.5%), P = 0.001). Human examinees answered 87.3% (IQR: 71.6-93.7%) of the same text-based questions correctly. Of the 73 questions with images, the customized GPT answered 55 (75.3%) questions correctly, which dropped to 51 (69.9%) when the images were withheld (P = 0.317). The human examinees answered 87.2% (IQR: 79.4-95.4%) of image-based questions correctly.

CONCLUSION

 Our findings suggest significant improvements in ChatGPT's ability to answer text-based hand surgery questions with hand-specific training. ChatGPT is still limited in its ability to interpret images to answer questions related to hand conditions. These data show hand surgeons can create customized GPT models to provide tailored answers to specific questions, which may serve as the foundation for educational and clinical tools.

Machine learning/artificial intelligence in sports medicine: state of the art and future directions

Machine learning (ML) is changing the way health care is practiced and recent applications of these novel statistical techniques have started to impact orthopaedic sports medicine. Machine learning enables the analysis of large volumes of data to establish complex relationships between "input" and "output" variables. These relationships may be more complex than could be established through traditional statistical analysis and can lead to the ability to predict the "output" with high levels of accuracy. Supervised learning is the most common ML approach for healthcare data and recent studies have developed algorithms to predict patient-specific outcome after surgical procedures such as hip arthroscopy and anterior cruciate ligament reconstruction. Deep learning is a higher-level ML approach that facilitates the processing and interpretation of complex datasets through artificial neural networks that are inspired by the way the human brain processes information. In orthopaedic sports medicine, deep learning has primarily been used for automatic image (computer vision) and text (natural language processing) interpretation. While applications in orthopaedic sports medicine have been increasing exponentially, one significant barrier to widespread adoption of ML remains clinician unfamiliarity with the associated methods and concepts. The goal of this review is to introduce these concepts, review current machine learning models in orthopaedic sport medicine, and discuss future opportunities for innovation within the specialty.

A Scientometric Worldview of Artificial Intelligence in Musculoskeletal Diseases Since the 21st Century

Purpose

Over the past 24 years, significant advancements have been made in applying artificial intelligence (AI) to musculoskeletal (MSK) diseases. However, there is a lack of analytical and descriptive investigations on the trajectory, essential research directions, current research scenario, pivotal focuses, and future perspectives. This research aims to provide a thorough update on the progress in AI for MSK diseases over the last 24 years.

Methods

Data from the Web of Science database, covering January 1, 2000, to March 1, 2024, was analyzed. Using advanced analytical tools, we conducted comprehensive scientometric and visual analyses.

Results

The findings highlight the predominant influence of the USA, which accounts for 28.53% of the total publications and plays a key role in shaping research in this field. Notable productivity was seen at institutions such as the University of California, San Francisco, Harvard Medical School, and Seoul National University. Valentina Pedoia is identified as the most prolific contributor. Scientific Reports had the highest number of publications in this area. The five most significant diseases are joint diseases, bone fractures, bone tumors, cartilage diseases, and spondylitis.

Conclusion

This comprehensive scientometric assessment benefits both experienced researchers and newcomers, providing quick access to essential information and fostering the development of innovative concepts in this field.

Incorporating Technology Adoption in Medical Education: A Qualitative Study of Medical Students' Perspectives

Introduction

The integration of technology into medical education has witnessed significant growth in recent years, with tools such as virtual reality, artificial intelligence, and telemedicine gaining prominence. These tool in medical education, offering immersive, experiential learning experiences.

Methods

We approached medical students currently enrolled in medical education programs and who are familiar with and actively use AI in medical education. Initially, we invited 21 random students to participate in the study; however, only 13 agreed to interviews. Some students cited their busy exam schedules as the reason for not participating. The participants were informed of the objective of the study before the commencement of the recorded interviews. Semi-structured interviews were used to guide the record interviews. Audio recordings were transcribed and analyzed using Atlas.ti, a qualitative data analysis software.

Results

Participants exhibited a diverse range of perceptions and levels of awareness regarding VR, AI, and telemedicine technologies. Learning with virtual reality was considered to be fun, memorable, inclusive, and engaging by participants. The use of virtual reality technology is seen as complementing current teaching and learning approaches, helping to build learners' confidence, as well as providing medical students with a safe environment for problem-solving and trial-and-error learning. The students reported that AI was seen as a potential game-changer in the healthcare sector. Participants hoped that telemedicine would provide healthcare services to remote and underserved populations.

Conclusion

The study conducted focus group discussions with medical students and residents in Saudi Arabia to explore their views on integrating VR, AI, and telemedicine in medical education and practice. Their insights highlight the need for informed decision-making and strategic development to optimize the benefits and address challenges like initial investments, technical issues, ethics, and regulations. These considerations are crucial for fully realizing the potential benefits of technology in medical education globally.

Overview of Artificial Intelligence Research Within Hip and Knee Arthroplasty

Hip and knee arthroplasty are high-volume procedures undergoing rapid growth. The large volume of procedures generates a vast amount of data available for next-generation analytics. Techniques in the field of artificial intelligence (AI) can assist in large-scale pattern recognition and lead to clinical insights. AI methodologies have become more prevalent in orthopaedic research. This review will first describe an overview of AI in the medical field, followed by a description of the 3 arthroplasty research areas in which AI is commonly used (risk modeling, automated radiographic measurements, arthroplasty registry construction). Finally, we will discuss the next frontier of AI research focusing on model deployment and uncertainty quantification.

Application of Artificial Intelligence in Shoulder Pathology

Artificial intelligence (AI) refers to the science and engineering of creating intelligent machines for imitating and expanding human intelligence. Given the ongoing evolution of the multidisciplinary integration trend in modern medicine, numerous studies have investigated the power of AI to address orthopedic-specific problems. One particular area of investigation focuses on shoulder pathology, which is a range of disorders or abnormalities of the shoulder joint, causing pain, inflammation, stiffness, weakness, and reduced range of motion. There has not yet been a comprehensive review of the recent advancements in this field. Therefore, the purpose of this review is to evaluate current AI applications in shoulder pathology. This review mainly summarizes several crucial stages of the clinical practice, including predictive models and prognosis, diagnosis, treatment, and physical therapy. In addition, the challenges and future development of AI technology are also discussed.

Pelvic bone tumor segmentation fusion algorithm based on fully convolutional neural network and conditional random field

Background and objective

Pelvic bone tumors represent a harmful orthopedic condition, encompassing both benign and malignant forms. Addressing the issue of limited accuracy in current machine learning algorithms for bone tumor image segmentation, we have developed an enhanced bone tumor image segmentation algorithm. This algorithm is built upon an improved full convolutional neural network, incorporating both the fully convolutional neural network (FCNN-4s) and a conditional random field (CRF) to achieve more precise segmentation.

Methodology

The enhanced fully convolutional neural network (FCNN-4s) was employed to conduct initial segmentation on preprocessed images. Following each convolutional layer, batch normalization layers were introduced to expedite network training convergence and enhance the accuracy of the trained model. Subsequently, a fully connected conditional random field (CRF) was integrated to fine-tune the segmentation results, refining the boundaries of pelvic bone tumors and achieving high-quality segmentation.

Results

The experimental outcomes demonstrate a significant enhancement in segmentation accuracy and stability when compared to the conventional convolutional neural network bone tumor image segmentation algorithm. The algorithm achieves an average Dice coefficient of 93.31 %, indicating superior performance in real-time operations.

Conclusion

In contrast to the conventional convolutional neural network segmentation algorithm, the algorithm presented in this paper boasts a more intricate structure, proficiently addressing issues of over-segmentation and under-segmentation in pelvic bone tumor segmentation. This segmentation model exhibits superior real-time performance, robust stability, and is capable of achieving heightened segmentation accuracy.

The Performance of ChatGPT on the American Society for Surgery of the Hand Self-Assessment Examination

BACKGROUND

This study aims to compare the performance of ChatGPT-3.5 (GPT-3.5) and ChatGPT-4 (GPT-4) on the American Society for Surgery of the Hand (ASSH) Self-Assessment Examination (SAE) to determine their potential as educational tools.

METHODS

This study assessed the proportion of correct answers to text-based questions on the 2021 and 2022 ASSH SAE between untrained ChatGPT versions. Secondary analyses assessed the performance of ChatGPT based on question difficulty and question category. The outcomes of ChatGPT were compared with the performance of actual examinees on the ASSH SAE.

RESULTS

A total of 238 questions were included in the analysis. Compared with GPT-3.5, GPT-4 provided significantly more correct answers overall (58.0% versus 68.9%, respectively; P = 0.013), on the 2022 SAE (55.9% versus 72.9%; P = 0.007), and more difficult questions (48.8% versus 63.6%; P = 0.02). In a multivariable logistic regression analysis, correct answers were predicted by GPT-4 (odds ratio [OR], 1.66; P = 0.011), increased question difficulty (OR, 0.59; P = 0.009), Bone and Joint questions (OR, 0.18; P < 0.001), and Soft Tissue questions (OR, 0.30; P = 0.013). Actual examinees scored a mean of 21.6% above GPT-3.5 and 10.7% above GPT-4. The mean percentage of correct answers by actual examinees was significantly higher for correct (versus incorrect) ChatGPT answers.

CONCLUSIONS

GPT-4 demonstrated improved performance over GPT-3.5 on the ASSH SAE, especially on more difficult questions. Actual examinees scored higher than both versions of ChatGPT, but the margin was cut in half by GPT-4.

The Rise of Artificial Intelligence: Implications in Orthopedic Surgery

Artificial intelligence (AI) is slowly making its way into all domains and medicine is no exception. AI is already proving to be a promising tool in the health-care field. With respect to orthopedics, AI is already under use in diagnostics as in fracture and tumor detection, predictive algorithms to predict the mortality risk and duration of hospital stay or complications such as implant loosening and in real-time assessment of post-operative rehabilitation. AI could also be of use in surgical training, utilizing technologies such as virtual reality and augmented reality. However, clinicians should also be aware of the limitations of AI as validation is necessary to avoid errors. This article aims to provide a description of AI and its subfields, its current applications in orthopedics, the limitations, and its future prospects.

Artificial intelligence-based orthopaedic perpetual design

Background and aims

Integrating Artificial Intelligence (AI) methodologies in orthopaedic surgeries is becoming increasingly important as it optimises implant designs and treatment procedures. This research article introduces an innovative approach using an AI-driven algorithm, focusing on the humerus bone anatomy. The primary focus of this work is to determine implant dimensions tailored to individual patients.

Methodology

We have utilised Python's DICOM library, which extracts rich information from medical images obtained through CT and MRI scans. The algorithm generates precise three-dimensional reconstructions of the bone, enabling a comprehensive understanding of its morphology.

Results

Using algorithms that reconstructed 3D bone models to propose optimal implant geometries that adhere to patients' unique anatomical intricacies and cater to their functional requirements. Integrating AI techniques promotes enhanced implant designs that facilitate enhanced integration with the host bone, promoting improved patient outcomes.

Conclusion

A notable breakthrough in this research is the ability of the algorithm to predict implant physical dimensions based on CT and MRI data. The algorithm can infer implant specifications that align with patient-specific bone characteristics by training the AI model on a diverse dataset. This approach could revolutionise orthopaedic surgery, reducing patient waiting times and the duration of medical interventions.

Technological innovations in shoulder replacement: current concepts and the future of robotics in total shoulder arthroplasty

BACKGROUND

Total shoulder arthroplasty (TSA) has been rapidly evolving over the last several decades, with innovative technological strategies being investigated and developed in order to achieve optimal component precision and joint alignment and stability, preserve implant longevity, and improve patient outcomes. Future advancements such as robotic-assisted surgeries, augmented reality, artificial intelligence, patient-specific instrumentation (PSI) and other peri- and preoperative planning tools will continue to revolutionize TSA. Robotic-assisted arthroplasty is a novel and increasingly popular alternative to the conventional arthroplasty procedure in the hip and knee but has not yet been investigated in the shoulder. Therefore, the purpose of this study was to conduct a narrative review of the literature on the evolution and projected trends of technological advances and robotic assistance in total shoulder arthroplasty.

METHODS

A narrative synthesis method was employed for this review, rather than a meta-analysis or systematic review of the literature. This decision was based on 2 primary factors: (1) the lack of eligible, peer-reviewed studies with high-quality level of evidence available for review on robotic-assisted shoulder arthroplasty, and (2) a narrative review allows for a broader scope of content analysis, including a comprehensive review of all technological advances-including robotics-within the field of TSA. A general literature search was performed using PubMed, Embase, and Cochrane Library databases. These databases were queried by 2 independent reviewers from database inception through November 11, 2022, for all articles investigating the role of robotics and technology assistance in total shoulder arthroplasty. Inclusion criteria included studies describing "shoulder arthroplasty" and "robotics."

RESULTS

After exclusion criteria were applied, 4 studies on robotic-assisted TSA were described in the review. Given the novelty of this technology and limited data on robotics in TSA, these studies consisted of a literature review, nonvalidated experimental biomechanical studies in sawbones models, and preclinical proof-of-concept cadaveric studies using prototype robotic technology primarily in conjunction with PSI. The remaining studies described the technological advancements in TSA, including PSI, computer-assisted navigation, artificial intelligence, machine learning, and virtual, augmented, and mixed reality. Although not yet commercially available, robotic-assisted TSA confers the theoretical advantages of precise humeral head cuts for restoration of proximal humerus anatomy, more accurate glenoid preparation, and improved soft-tissue assessment in limited early studies.

CONCLUSION

The evidence for the use of robotics in total hip arthroplasty and total knee arthroplasty demonstrates improved component accuracy, more precise radiographic measurements, and improved early/mid-term patient-reported and functional outcomes. Although no such data currently exist for shoulder arthroplasty given that the technology has not yet been commercialized, the lessons learned from robotic hip and knee surgery in conjunction with its rapid adoption suggests robotic-assisted TSA is on the horizon of innovation. By achieving a better understanding of the past, present, and future innovations in TSA through this narrative review, orthopedic surgeons can be better prepared for future applications.

The Emerging Role of Automation, Measurement Standardization, and Artificial Intelligence in Foot and Ankle Imaging: An Update

In the past few years, advances in clinical imaging in the realm of foot and ankle have been consequential and game changing. Improvements in the hardware aspects, together with the development of computer-assisted interpretation and intervention tools, have led to a noticeable improvement in the quality of health care for foot and ankle patients. Focusing on the mainstay imaging tools, including radiographs, computed tomography scans, and ultrasound, in this review study, the authors explored the literature for reports on the new achievements in improving the quality, accuracy, accessibility, and affordability of clinical imaging in foot and ankle.

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.

Predicting 30-day readmission following total knee arthroplasty using machine learning and clinical expertise applied to clinical administrative and research registry data in an Australian cohort

BACKGROUND

Thirty-day readmission is an increasingly important problem for total knee arthroplasty (TKA) patients. The aim of this study was to develop a risk prediction model using machine learning and clinical insight for 30-day readmission in primary TKA patients.

METHOD

Data used to train and internally validate a multivariable predictive model were obtained from a single tertiary referral centre for TKA located in Victoria, Australia. Hospital administrative data and clinical registry data were utilised, and predictors were selected through systematic review and subsequent consultation with clinicians caring for TKA patients. Logistic regression and random forest models were compared to one another. Calibration was evaluated by visual inspection of calibration curves and calculation of the integrated calibration index (ICI). Discriminative performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC).

RESULTS

The models developed in this study demonstrated adequate calibration for use in the clinical setting, despite having poor discriminative performance. The best-calibrated readmission prediction model was a logistic regression model trained on administrative data using risk factors identified from systematic review and meta-analysis, which are available at the initial consultation (ICI = 0.012, AUC-ROC = 0.589). Models developed to predict complications associated with readmission also had reasonable calibration (ICI = 0.012, AUC-ROC = 0.658).

CONCLUSION

Discriminative performance of the prediction models was poor, although machine learning provided a slight improvement. The models were reasonably well calibrated, meaning they provide accurate patient-specific probabilities of these outcomes. This information can be used in shared clinical decision-making for discharge planning and post-discharge follow up.