A Systematic Review on Artificial Intelligence in Orthopedic Surgery

The healthcare industry has experienced a notable upswing in attention towards Artificial Intelligence (AI), which has been steadily transforming medical procedures. With the advancements in data storage and computer processing power, computer systems are acquiring the capacity to accomplish tasks that previously necessitated human intelligence.

Historically, AI in orthopedic surgery has evolved significantly over the decades, from early rule-based systems in the 1950s to sophisticated Machine Learning (ML) algorithms and robotic-assisted surgery today. Pioneering efforts like MYCIN in the 1970s and computer-assisted orthopedic surgery (CAOS) systems in the 1990s demonstrated AI's potential in medical decision-making and enhancing surgical precision. In the 2000s, the integration of ML enabled predictive modeling and personalized treatment planning, while the adoption of robotic systems in the 2010s revolutionized surgical techniques. Today, AI applications in orthopedics focus on leveraging patient-specific data and predictive analytics to optimize outcomes and tailor treatments, promising computer-aided detection (CAD) in musculoskeletal healthcare.

CAD systems and Diagnostic Imaging are pivotal in orthopedic disease diagnosis, leveraging AI to enhance clinical decision-making. By analyzing medical images, such as X-ray, MRI, and CT scans, clinicians can visualize bone structures and soft tissues, crucial for identifying injuries and conditions. However, interpreting these images can be intricate and time-consuming. Therefore, developing a CAD system for anomalies detection has become attractive in medical imaging. Enter AI, which has revolutionized orthopedic imaging by swiftly and accurately analyzing medical images. In fact, it has become the preferred approach for analyzing radiology images. This includes different tasks like bone tumor detection, cartilage segmentation, spinal disease prediction etc. on different image modalities Xray, MRI, CT scans, etc. The potential for improvement in patient care through these means is broad, encompassing areas such as diagnosis, management, research, and systems analysis.

Within this frame of reference, recently, Deep Learning (DL) has made significant advances. It demonstrates the capacity of diagnosing tasks for medical imaging similarly to the performance of human radiology experts, mainly through the utilization of special and powerful architectures as Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN). DL algorithms have shown remarkable proficiency in executing various radiographic tasks in the field of musculoskeletal radiology, exhibiting an expertise level that enables accurate diagnosis of orthopedic diseases. Several papers have been worked on detecting bone anomalies by exploring DL models, with good to excellent accuracy even similar to expert human performance in a minimal time with much faster speeds. Although these findings are interesting, no systematic study has been conducted to analyze the scope and efficacy of AI algorithms, especially GAN-based architecture in orthopedic anomalies diagnosis.

The scope of AI applications within orthopedic surgery covered in this review encompasses the utilization of AI techniques, particularly focusing on DL models such as GANs, AEs and CNNs, in various diagnostic tasks related to orthopedic conditions. Specifically, the review addresses the application of AI in bone diagnosis, musculoskeletal imaging, fracture detection, spine pathology diagnosis, and cartilage diagnosis. Furthermore, the review evaluates primary research studies that have developed AI algorithms for diagnosing orthopedic diseases, including bone tumors detection, fracture detection, spinal pathology diagnosis, and cartilage diseases. The emphasis is on summarizing the methods used for training AI algorithms, managing datasets, and assessing the accuracy of these algorithms in diagnosing orthopedic pathologies through medical imaging. By delineating this scope, the review aims to provide readers with a focused understanding of the current state and potential applications of AI in orthopedic surgery.

This review focuses on AI applications that have the potential to enhance or transform clinical practice. It provides a historical overview of AI in medicine to contextualize recent developments, highlights successful application areas, and identifies potential avenues for future research. AI is still a novel concept for many orthopedic surgeons, but the existing body of work provides valuable insights into potential new applications and areas for research. In this systematic review, we examine the use of CNN and GAN-based architectures in the literature. We will thoroughly discuss the current state of these models and the results of the included studies to understand the extent of AI research in orthopedics, describe how AI has been applied in the field, and provide a glimpse into its potential future applications.

The search was conducted in October 2023 and produced 987 articles. Once the duplicates had been eliminated and a preliminary assessment based on the titles and abstracts had been completed, the total number of qualifying articles was reduced to 75. A second screening phase was carried out after reading the full text. The selection process of the studies was documented using a flowchart diagram following the PRISMA protocol Figure 2. The articles were evaluated for inclusion or exclusion. It was noted that the number of published papers has been increasing annually, with the number of scientific papers published in 2023 being almost double that of 2019. This trend may be attributed to several factors, particularly the growing availability of medical images and data to researchers. In addition, the capacity of Generative models noticed in recent years in anomaly detection tasks. Moreover, medical stuff understands the advantage and the need AI systems to improve clinical performance in term of time consuming in diagnosis and precision.

In this section, we have come up with an overview of the main papers we collected during our literature review exploring AI methods notably CNN and GAN-based architectures [1].

3.1 Spine pathologies detection

In this section, we begin to report on papers in which authors have diagnosed spinal diseases using AI methods.

Among the studies that sought to assess many researchers have trained CNN on radiographic images [2-5] Particularly, in the study [6], they developed and validated DL algorithms (DLAs) to automatically detect scoliosis using unclothed back images. Their architecture contains Faster-RCNN that localized the regions of interest (from neck to hip). To identify the characteristics of each group, a resnet of 101 levels was created. After undergoing preprocessing, the Resnet was utilized to extract advanced characteristics for the purpose of binary classifications. The algorithms’ accuracy allowed for the detection of cases with a curve of 20° and severity grading for both binary and four-class categories. In the study [7], authors reported an automated approach that can handle spinal abnormalities by collecting anatomical parameters from biplanar X-rays of the spine. In order to anticipate the position of each landmark, a CNN was completely trained with an added DSNT layer that was spatial to numerical and differentiable. The predictions made by the models regarding the shape of the spine were closely linked to the actual shape. The standard errors of the computed parameters varied from 2.7° (for the pelvic tilt) to 11.5° (for then L1-L5 lordosis). The ground truth shape and the spinal form predicted by the models had a good correlation. CNN models have also been investigated to identify and segment discs and vertebrae from spinal MRIs [8-10]. Notably, He et al. [11] suggest a detection framework SpineOne to locate and categorize degenerating discs and vertebrae from MRI slices. The following three essential methods are the foundation of SpineOne: Three innovations were made: 1) a new keypoint heatmap design to enable simultaneous keypoint localization and classification; 2) Attention modules were explored to distinguish between vertebrae and discs in representations; and 3) A new method, called SpineNet, has been introduced in the paper "SpineNet: Learning Scale-Permuted Backbone for Recognition" [12]. It uses a gradient-based approach to associate multiple learning objectives during later stages of training. SpineNet is based on CNNs and has a unique backbone structure with rearranged intermediate features and connections between different scales. It was trained for object detection through a process called Neural Architecture Search.

Imaging analysis approaches are being introduced potentially for scientific research and clinical use to facilitate spinal pathologies diagnosis. Machine learning-based approaches had been investigated to detect, classify or segment anatomical spinal landmarks [13-16]. Wu et al. [13] introduce a new framework for estimating landmarks in X-ray images for AIS (Automatic Intervertebral Segmentation) assessment. The framework leverages BoostNet, a creative integration of ConvNet and statistical methods. BoostNet has strong feature extraction capabilities and is able to handle the variability in X-ray images. The BoostNet architecture shows a robust quality scoliosis detection in the clinical scenario by estimatng a mean squard error (MSE) rate of 0.00068 in 431 cross validation images and 0.0046 in 50 test images. Also, landmarks detection and alignment analysis have been recently treated in whole-spine lateral radiographs by Yeh et al. [14]. The authors [14] propose a DL approach for identifying spinal anatomical landmarks and predicting radiographic parameters. Their approach resulted in predictions that had a high correlation with ground truth values, with all p-values less than 0.001.

Otherwise, statistical methods have been investigated to treat spinal deformities by calculating the full geometry or specific geometrical of spine parameters, such as sagittal vertical axis, lumbar lordosis, and Cobb angle [15-18]. Zhang et al. [15] develop a computer-aided technique using a deep neural network (DNN) that was trained using vertebral paches taken from radiographs of a spinal model. The vertebral slopes predicted by the DNN were used to automatically determine the Cobb angle of the spinal curve. The mean absolute differences for model radiographs were less than 3°, and the intraclass correlation coefficients were higher than 0.98. This shows that the proposed approach had a good level of reproducibility when it came to measuring model radiography. Yi et al. [17] performed a method which demonstrate its merits in both Cobb angle measurement and landmark detection on low-contrast and ambiguous X-ray images. Moreover, Caesarendra et al. [18] proposes a DL architecture to identify spinal vertebrae from radiographs to automate the calculating of the Cobb angle that helps to diagnosis the presence of scoliosis and spinal deformities. From the input image, algorithm detect the landmark features, then it calculats the Cobb angle. As a result, the suggested algorithm has a classification accuracy of about 90 percent.

The use of GAN-based predictive models could be especially beneficial in spine surgery due to the complex nature of the procedures and potential high rates of com-plications in patients who often have multiple health issues. Recently, spinal anomalies detection and spinal shape diagnosis through GAN models have been gaining interest. A number of GAN-based models have been performed for spine-related tasks as well as [19, 20] from either MRIs, CT or X-ray images. Authors [20] introduce a SpineGAN model, which uses an atrous convolution autoencoder module to handle the problem of the high variety and variability of complicated spine structures while maintaining fine-grained structural data. Spine-GAN uses a discriminative network that can rectify anticipated mistakes and global-level contiguity to provide reliable performance and effective generalization. Numerous studies using the MRIs of 253 patients showed that SpineGAN achieves high pixel accuracy of 96.2 percent, demonstrating its effectiveness and potential as a therapeutic tool. CycleGAN is one of the most famous GAN models that attracted a lot of attention through image-to-image translation using unpaired images [21]. CycleGAN model uses a trainable preprocessing pipeline that normalizes the input MRI data exploring a low-capacity fully CNN, then uses FC-ResNets in flow for the vertebral bodies segmentation.

It is suggested that a pseudo-3D CycleGAN architecture be used, along with a cyclic loss function to provide coherence across MRI and CT synthesis. It produced promising results, it showed the power of the pipeline by achieving clinically durable CT scans which can be treated for surgical guidance.

In the research [19], authors proposed and verified a semi-supervised GAN approach for early scoliosis detection using chest X-rays. The proposed model employs a semi-supervised training process where the GAN is initially taught to recognize scoliosis patterns and then performs basic classification to accurately differentiate between normal and scoliotic states. The results show that the negative predictive value (NPV) and positive predictive value (PPV) are 0.856 and 0.950 respectively.

3.2 Fracture detection

Fracture is the most accurate orthopedic pathology in the most hospitals. Analyzing medical images to identify bone fractures is time-consuming and requires qualified experts. Therefore, Scientists investigate their time to help doctors to reduce diagnosis timing, and to improve decision precision [22-25].

AI/DL has the potential to assist medical professionals and decision makers in creating effective and cost-efficient treatment plans [26-28]. Numerous studies have shown that incorporating automated tools during the diagnostic phase can improve the accuracy of physician interpretation [29, 30].

Most of studies mainly investigate on accurately detecting musculoskeletal abnormalities CNN models. Authors [31-33] have proposed GnCNNr adopting the principle of normalization, including group normalization, weight normalization and cyclic learning rate planner to improve the model performance measures. When compared to other deep learning techniques like DenseNet, Inception, and Inception v2 model, the GnCNNr model had the highest accuracy of 85 percent. Regnard et al. [34] performed AI models for skeletal lesions detection and localization to compare them with the routine radiological interpretation. After a traumatic pelvic and limb injury, they gathered radiographic examinations and the related radiologists’ reports. An AI (BoneView, Gleamer) was used to analyze each exam, and the results were compared to the radiologists’ reports. An alternative to using CNN is Transfer Learning, a widely used DL approach in computer aided systems. In the study [35, 36], Abreu Dias and Kim and MacKinnon used the Inception v3 network, retrained on lateral wrist radiographs, to improve fracture detection and classification. The model was trained on 11,112 images after a data augmentation pretraining, starting with 1,389 radiographs (695 "fracture" and 694 "nofracture"). The resulting AUC was 0.954.

Now we will focus on studies that surpass the traditional Convolutional Neural Network to work with generative adversarial and AE networks [37]. The Res-unetGAN network [38], a generative adversarial network-based unsupervised anomaly detection approach, is proposed in this article. To calculate the normal distribution of normal samples, the autoencoder architecture contains ResNet50 and UNet that is employed by the generative network. The discriminator employs a Convolutional Neural Network model that is based on deep separable convolution to construct a gaming process. To achieve the goal of identifying anomalies, a reconstruction error score is calculated based on the quality of reconstruction, and the presence of defects in the sample is evaluated by this reconstruction error score. Following numerous tests on the Mura data set, the Mura defect’s detection accuracy outperforms several other models. Davletshina et al. [39] showed how unsupervised techniques trained on radiographic images devoid of anomalies can help clinicians assess. The approach focuses on improvement of diagnostic accuracy and lowers the possibility of overlooking critical areas. Therefore, the use cutting-edge techniques for unsupervised learning to find anomalies and demonstrate how the results of these techniques may be justified.

3.3 Osteoarthritis detection and prediction

Experts frequently use manual examination of patient medical images, which are typically gathered in hospitals, to make an osteoarthritis diagnosis. Osteoarthritis is a time-consuming task. Therefore, several studies coverage and concentrate on using image-based DLsystems to automatically identify osteoarthritis [40, 41].

Jakaite et al. [42] investigate ML techniques to build a comparative study to deduce ML’s capability for osteoarthritis (OA) radiography diagnosis at a primal stage, and the number of patient cases is low. With Kellgen-Lawrence scores ranging from 1 to 10, patients’ knees were detected in high-resolution X-ray scans for their investigations. Although the Group Method of Data Handling approach in DL has shown a significant enhancement in diagnostic testing, the current ML methods have only demonstrated a slight increase in diagnostic precision. The comparative trials show that the suggested framework using texture features based on Zernike has greatly increased diagnostic accuracy, increasing it by an average of 11 percent. Wang et al. [43] propose an end-to-end approach to automatic osteoarthritis diagnosis by combining into the training workflow a YOLO object detection algorithm and a visual transformer. Their strategy requires to analyse 200 annotated images from a huge dataset with more than 4500 samples, but it accurately segments 95.57 percent of the data. Additionally, compared to CNN based models developed for the same case, their classification result increased accuracy by 2.5 percent. It performed patient statistics on medical use and health behavior variables to train a deep neural network (DNN) to detect the presence of osteoarthritis [44]. From the patients’ basic background medical records, characteristics were generated using principal component analysis (PCA) with quantile transformer going over to determine the presence of osteoarthritis. Our tests demonstrated that the suggested approach, which combined a deep neural network with scaled PCA, produced an area under the curve of 76.8 percent while requiring the least amount of feature generation work. Therefore, to cut down on medical expenses and the amount of time patients spend in hospitals, this strategy could be a hopeful tool for patients and clinicians to screen in advance for potential osteoarthritis.

3.4 Bone and cartilage image diagnosis

Recently, research has explored the role of AI and has come to increasingly recognize the significance of DL in the medical domain, particularly in the area of computer-assisted knee osteoarthritis diagnosis [45, 46]. They highlighted the potential value of diagnostic approaches for early knee osteoarthritis identification namely bone segmentation [47, 48], bone classification [49] and abnormality detection [50, 51]. Most of studies mainly adapted on accurately diagnosis musculoskeletal abnormalities CNN models. For diagnosing abnormal musculoskeletal radiographs, He et al. [52] present an innovative calibrated ensemble of deep learners. Their model makes use of the advantages of most fundamental DL networks (DenseNet, ConvNet and ResNet), which are frequently used straight fully or as the framework in other DL based methods. The introduced model showed perspective results comparing to three individual models and a conventional ensemble learner, according to experimental findings based on the publicly available MURA dataset. Their model achieved an "overall efficiency of (Accuracy: 0.87, Precision: 0.93, AUC: 0.93, Recall: 0.81, Cohen’s kappa: 0.74). Using a convolutional models, Noguchi et al. [53] create and assess an algorithm for bone segmentation on whole-body CT. They assessed the effectiveness of the different data augmentation techniques to enhance the network’s performance and robustness (RICAP). The network’s mean Dice coefficient was 0.983 0.005 after training on the internal dataset. It demonstrated the effectiveness of convolutional based architectures (CNNs) for generalized abnormality detection on radiographs of the lower extremities [54]. They gathered a sizable dataset of 93,455 radiographs of the lower extremities of various body areas, classifying each exam as normal or abnormal. On this abnormality classification test, they had implemented a 161-layer densely connected and pre-trained CNN to attend an AUC-ROC of 0.880 (specificity = 0.961, sensitivity = 0.714).

Lately, researchers rely on GAN models to innovate perspective solutions for medical issues. A number of GAN-based models have been performed for bone and cartilage-related tasks as well as [55, 56] from either MRIs, Ultrasound or X-ray images. Alsilan et al. [57, 58] investigates his time for exploring DL on Bone pathologies diagnosis. suggest a computational technique based on GAN architecture that can simultaneously generate segmented bone surface masks and synthetic B-mode US images. Alsinan et al. [57] suggested GAN model produced realistic B-mode bone US images and segmented bone masks using two convolutional layers. On 1235 images taken from 27 patients using two distinct US machines, quantitative and qualitative assessment tests are carried out to demonstrate a comparison of their model’s results with the current leading GANs for bone segmentation task surfaces using a U-Net architecture. In the study [58], Alsinan suggested a real time computational technique to separate bone shadow pictures from in vivo US scans based on a novel GAN architecture. Additionally, he demonstrates the potential of using the segmented shadow images as a substitute for accurate bone surface segmentation in real-time through the application of a multi-feature supervised CNN architecture. They were able to segment bone shadows with a mean dice coefficient of 93 percent (0.02), demonstrating that the system is comparable to manual expert annotation. In the study [59], authors used GAN and UNet models to segment bone features from a wrist US scan. The ensemble models were implemented on 10,500 wrist US scans from 47 patients obtained from the pediatric emergency department of the University of Alberta Hospital (UAH). In general, GAN had the strongest recall even though UNet had the highest DICE score, accuracy, and Jacard Index.

3.5 Skeletal bone age evaluation

Bone age is a constructive indicator used to radio-logically evaluate and diagnosis various bone pathologies and indentify the most suitable medicine and the optimal timing of treatment. The purpose of bone age assessment is to measure growth and maturity and to treat pediatric disorders. Recently, several AI researchers work on developing computer aided systems for bone age assesment. In this section, we review studies that investigate DL based approaches on bone age assesment. Ren et al. [60] reported a DL-based approach for automatically training hand radiographs for a bone age assesment. The network was designed to target bone age-related regions in the images and incorporates an attention module for generating coarse and fine attention maps to be fed into the regression network. Additionally, the regression network is supervised with a dynamic attention loss, allowing it to more accurately estimate the bone age even for challenging or "outlier" images. The results of the experiment show that the proposed approach has an average discrepency of 5.2-5.3 months between clinical and automatic bone age assessments when tested on two large datasets.

A new approach [61] to improve BAA training throughout the pretraining and training process was introduced using GAN. The pre-training framework uses a unique distance metric called cosine distance, which is applied to optimal transport for data augmentation (CNN-GAN-OTD). During the training phase, a method that combined data. A DL-based computer-aided diagnosis was developed by Li et al. [62] for the purpose of performing bone age assessments. Firstly, during image-processing pipeline they reduced by exploring an unsupervised learning that identify informative regions. Accordingly, to increase the accuracy of prediction, they used a backbone image model with pre-trained parameters. The best outcomes from the experimental comparative study revealed a mean absolute error on the public RSNA dataset of 6.2 and 5.1 months on the supplementary dataset utilizing MobileNetV3 as the foundation.

3.6 Bone tumors detection

Cancer is widely recognized as one of the most perilous illnesses globally. In medical terms, it is called a malignant neoplasm. This genetic disorder is brought on by uncontrolled cell growth. Early identification of this dangerous condition can reduce mortality rates [63, 64]. The X-ray images is explored to detect, classify or segment bone cancer [65]. Park et al. [66] developed and validate an Artificial Intelligence-based primal identification and classification of bone tumors in the proximal femur on X-ray images. A single tertiary referral center provided 538 standard anteroposterior hip X-ray images, 94 (120 benign, 94 malignant, or 324 no tumor), were used to train the AI model. The image modalities were pre-trained to make them ideal for the deep learning algorithm’s training. CNN models were used to conduct the multi-classification on each femur using pre- processed pictures. As a result, the best CNN model has an AUROC of 0.953. von Schacky et al. [67] and He et al. [68] followed the same strategy by adopting DL approaches to diagnosis primary bone cancers based on clinical radiographs. All patients had their bone tumors classified as benign or malignant using the histopathologic results as the gold standard. The internal data set includes radiographs from 934 individuals, 667 of which were benign bone tumors, and 267 of which were malignant. The multitask DL model classified bone lesions as benign or malignant with an accuracy of 80.2 percent. Researchers had not limited their studies on X-ray images, they extend it to MRI. As an advantage, different types of brightness are shown for the same structures in an MRI scan [69]. The objective of this study [70] is to utilize DL techniques to deduce the malignancy of a bone tumor from magnetic resonance imaging (MRI) scans. The study’s cohort consisted of 23 individuals, including 14 females and 9 males with ages ranging from 15 to 80 years. T1 and T2 weighted MRI scans are classified using two pretrained ResNet50 image classifiers. A clinical model is used to determine a tumor’s likelihood of being malignant. The patient’s clinical data and the results of the T1 and T2 classifiers are the model’s inputs. It is a feed-forward neural network. Both classifiers achieved 95.00 percent accuracy throughout validation. The purpose of this scientific study [71] was to use the Turing test to evaluate an AI system’s capacity to detect spine tumors. This paper suggests a fast R-CNN architecture for a DL-based tumor detection approach that, in two stages, first identifies the localization and the size of the bounding box around the lesion area and then creates region proposals using a region proposal network. The respondent’s response was deemed incorrect if he failed to recognize the image that had been annotated by a person. The Turing test was regarded to have been passed if all mis-classification rates were >30 percent and the respondents could not tell the AI-detected tumor from the human-annotated one. As a result, The Turing test had an average misclassification rate of 51.2 percent.

3.7 Classification of pathological gait patterns

Human gait recognition and diagnosis become an active research area in AI [72, 73]. Ramirez et al. [74] suggest a sensor-based approaches for analyzing human gait. It introduces analytical methods in a framework for multivariate time series classification and interpretable anomaly detection for human gait study. Using a real-world clinical dataset in the field of biomechanical orthopedics to demonstrate the application. Guo et al. [75] used a foot-pressure database gathered using the GAITRite walkway system to classify pathology-related variations in gait in young children. The classifier’s accuracy is improved by combining age information with the assessment of normal and abnormal gaits. With the help of this innovative approach, it may be possible to create a measure of pediatric gait abnormalities that is precise, affordable, and real-time. This measure would be able to inform clinicians about the effectiveness interventions and optimizing the giving treatment. With the use of a smart footbed equipped with multiple sensor arrays, Lee et al. [76] suggested a DL-based strategy for classifying different gait types. Gait data was collected by us using a smart insole that integrated a pressure sensor array, an acceleration sensor array, and a gyro sensor array. A deep convolution neural network was then used to retrieve gait pattern features (DCNN). The data acquired from each sensor array was then used to build an independent DCNN, which was used to extract a feature map. The feature maps were then integrated to create a fully linked network for classifying gait types. According to experimental results, the proposed approach demonstrated a remarkably high classification accuracy of over 90 percent for seven different gait patterns, including walking, fast walking, running, ascending stairs, descending stairs, climbing hills, and descending hills.

3.8 Prosthesis control

Researchers are exploring using AI and DL to regulate prosthetics as the field of smart prosthetics has advanced significantly in recent years. In the most of development studies, it was discovered that the CNN can be used to obtain faster and more efficient limb movements. The studies [60, 77] specialize and investigate their time to develop and validate a new approach based on ML techniques to analyse after a primary total hip arthroplasty. Age, race, gender, and comorbidity scores were the features having impact on model’s performance, with an AUROC of 0.87 and 0.71 for LOS and payment respectively.

Artificial Intelligence is significantly influencing medical research and the care of patients, with numerous applications in the diagnosis of orthopedic conditions. This study systematically reviewed 75 research studies that utilized AI and DL techniques, with a specific emphasis on the use of CNN and GAN for orthopedic diagnosis.

The results indicated that the AI algorithms showed promising results, with favorable outcomes. However, there was a significant variation in study designs, which made it challenging to accurately evaluate and compare the performance of different models.

To further enhance the understanding of AI's performance, future studies should aim to adopt consistent training and testing methods and provide more comprehensive and transparent reporting of their methods and outcomes. Ultimately, ensuring clinical relevance in the development of AI models is paramount for their effective integration into real-world healthcare settings. Future studies should focus on testing models in ways that align with clinical workflows and patient care outcomes. Collaboration between AI developers, healthcare providers, and regulators is key to defining relevant performance metrics and standards. By prioritizing clinical relevance, researchers can ensure that AI models have a meaningful impact on patient care and clinical decision-making.

The implementation of AI in orthopedic diagnosis necessitates thorough cost-benefit analyses to justify the investment in this technology. While AI has the potential to enhance diagnostic accuracy, improve patient outcomes, and optimize resource allocation, its adoption requires significant financial investment, infrastructure development, and training of personnel. Conducting cost-benefit analyses can provide insights into the potential return on investment (ROI) of AI implementations, taking into account factors such as reduced healthcare costs, improved efficiency, and enhanced patient satisfaction. Additionally, assessing the long-term financial implications, including maintenance costs and scalability, is essential for ensuring the sustainability of AI implementations. By quantifying the expected benefits against the associated costs, healthcare stakeholders can make informed decisions regarding the adoption of AI in orthopedic diagnosis, ultimately maximizing value for both patients and healthcare systems.