AI applications in musculoskeletal imaging: a narrative review

Assessing Bone Age: A Paradigm for the Next Generation of Artificial Intelligence in Radiology.

Focus issue: Artificial intelligence in medical physics.

Artificial intelligence (AI) is increasingly used in clinical practice for musculoskeletal imaging tasks, such as disease diagnosis and image reconstruction. AI applications in musculoskeletal imaging have focused primarily on radiography, CT, and MRI. Although musculoskeletal ultrasound stands to benefit from AI in similar ways, such applications have been relatively underdeveloped. In comparison with other modalities, ultrasound has unique advantages and disadvantages that must be considered in AI algorithm development and clinical translation. Challenges in developing AI for musculoskeletal ultrasound involve both clinical aspects of image acquisition and practical limitations in image processing and annotation. Solutions from other radiology subspecialties (e.g., crowdsourced annotations coordinated by professional societies), along with use cases (most commonly rotator cuff tendon tears and palpable soft-tissue masses), can be applied to musculoskeletal ultrasound to help develop AI. To facilitate creation of high-quality imaging datasets for AI model development, technologists and radiologists should focus on increasing uniformity in musculoskeletal ultrasound performance and increasing annotations of images for specific anatomic regions. This Expert Panel Narrative Review summarizes available evidence regarding AI's potential utility in musculoskeletal ultrasound and challenges facing its development. Recommendations for future AI advancement and clinical translation in musculoskeletal ultrasound are discussed.

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and learn like humans. AI has the potential to revolutionise the way that healthcare professionals diagnose, treat, and manage conditions affecting the female reproductive system. Machine learning (ML) is a subset of AI which deals with the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions without being explicitly programmed to do so. Deep learning (DL) is a subfield of ML that utilises neural networks with multiple layers, known as deep neural networks (DNNs), to learn from data. DNNs are inspired by the structure and function of the human brain and are capable of automatically learning high-level features from raw data, such as images, audio and text. DL has been very successful in various applications such as image and speech recognition, natural language processing and computer vision. ML algorithms can be divided into three categories: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning algorithms are trained on a labelled dataset, where the desired output (label) is already known. Unsupervised learning algorithms are trained on an unlabelled dataset and are used to discover patterns or relationships in the data. Reinforcement learning algorithms are trained using a trial-and-error approach, where the agent receives a reward or penalty for its actions. The goal of reinforcement learning is to learn a policy that maximises the expected reward over time. AI and ML are increasingly being applied in the field of obstetrics and gynaecology, with the potential to improve diagnostic accuracy, patient outcomes, and efficiency of care. AI has been applied to the field of medicine for several decades. One of the earliest examples of AI in medicine was the development of MYCIN in the 1970s, a computer program that could diagnose bacterial infections and recommend appropriate antibiotic treatments. MYCIN was developed by a team at Stanford University led by Edward Shortliffe, and its success demonstrated the potential of AI in medical decision making. In the 1980s, AI-based expert systems such as DXplain, developed at Massachusetts General Hospital, were used to assist in the diagnosis of diseases. These early AI systems were based on rule-based systems and were limited in their capabilities. One of the earliest examples of AI was the development of computer-aided diagnostic systems for ultrasound images in the 1970s and 1980s. These systems were designed to assist radiologists in identifying fetal anomalies and other conditions. In recent years, there has been a renewed interest in the use of AI in obstetrics and gynaecology, driven by advances in ML and the availability of large amounts of data. One of the primary areas in which AI and ML are being used in obstetrics and gynaecology is in the analysis of imaging data, such as ultrasound and magnetic resonance imaging. AI algorithms can be trained to automatically identify and classify different structures in the images, such as the placenta or fetal organs, with high accuracy. Another area of focus is the use of AI to predict preterm birth. Researchers have used ML algorithms to analyse data from electronic health records and identify patterns that are associated with preterm birth. By analysing large datasets of patient information and outcomes, AI algorithms can identify patterns and risk factors that may not be apparent to human analysts. This can help to improve the prediction of obstetric outcomes and guide clinical decision making. In recent years, AI has also been applied in obstetrics and gynaecology for real-time monitoring of high-risk pregnancies and identifying fetal distress. These systems use ML algorithms to analyse data from fetal heart rate monitors and identify patterns that are associated with fetal distress. AI and ML are also being used to develop new tools for the management of gynaecological conditions, such as endometriosis and fibroids. These tools can be used to predict the progression of the disease and guide treatment decisions. One example of the use of AI in benign gynaecology is the development of computer-aided diagnostic systems for endometriosis. These systems use ML algorithms to analyse images of the pelvic region and identify the presence of endometrial tissue, which can be a sign of endometriosis. Another area where AI and ML are being applied is in the management of fibroids. ML algorithms are being used to analyse imaging data and predict the growth and behaviour of fibroids, which can aid in the development of personalised treatment plans. In the field of oncology, AI is being used to improve the accuracy and speed of cancer diagnosis. AI algorithms can analyse images of tissue samples to identify the presence of cancer cells and predict the likelihood of a positive outcome following treatment. AI algorithms can be trained to analyse images from pelvic scans and identify signs of ovarian cancer with high accuracy. In addition to these specific applications, AI and ML are also being used to improve the efficiency and organisation of care in obstetrics and gynaecology. For example, by analysing large amounts of clinical data, AI algorithms can be used to identify patients at high risk of complications, prioritise them for care and ensure that they receive the appropriate level of care in a timely manner. AI and ML have the potential to revolutionise the field of fertility and in vitro fertilisation (IVF). By using data from large patient populations, AI and ML algorithms can help identify patterns and predict outcomes that would be difficult for human experts to discern. This can lead to improvements in diagnosis, treatment planning, and overall success rates for patients undergoing IVF. One area where AI and ML are being applied is in the selection of embryos for transfer during IVF. By analysing images of embryos, AI and ML algorithms can predict which embryos are most likely to result in a successful pregnancy. Another area where AI and ML have shown potential is in the optimisation of culture conditions for embryos. This has the potential to improve the survival and development of embryos, leading to higher pregnancy rates. AI and ML are also being used to improve the timing of embryo transfer during IVF. By analysing data from patient medical histories, AI and ML algorithms can predict the optimal time for transfer to increase the chances of successful pregnancies. In addition to these applications, AI and ML are being used in other areas of fertility and IVF to improve patient outcomes. For example, AI and ML are being used to predict the likelihood of ovarian reserve, predict ovulation timing, and improve the efficiency and cost-effectiveness of fertility clinics. AI and ML are rapidly evolving fields that have the potential to revolutionise the field of surgery. These technologies can be used to assist surgeons in a variety of ways, from pre-operative planning to real-time guidance during procedures. One of the key areas where AI and ML are being applied in surgery is in image analysis. For example, algorithms can be used to automatically segment and identify structures in medical images, such as tumours or blood vessels. This can help surgeons plan procedures more accurately and reduce the risk of complications. Another area where AI and ML are being used in surgery is in the development of robotic systems. These systems can be programmed to perform specific tasks, such as suturing or cutting tissue, with a high degree of precision and accuracy. In addition, robotic systems can be equipped with sensors that provide real-time feedback to the surgeon, which can help to improve the outcome of the procedure. These systems can be programmed with advanced algorithms that allow them to make precise incisions, control bleeding, and minimise tissue damage. AI and ML can also be used to improve the efficiency and safety of surgical procedures. For example, algorithms can be trained to analyse data from vital signs monitors, such as heart rate and blood pressure, and alert surgeons to potential complications in real-time. AI and ML are also being used to assist with post-operative care. For example, algorithms can be used to analyse patient data and predict which patients are at risk of complications, such as infection or bleeding, allowing surgeons to take preventative measures. Overall, AI and ML have the potential to significantly improve the field of surgery by increasing accuracy and precision, reducing the risk of complications, and improving patient outcomes. As the technology continues to advance, it is likely that we will see an increasing number of AI-assisted surgical systems and applications in clinical practice. In gynaecology specifically, there is a scarcity of data and diversity in the data. This can lead to AI models that are not generalisable to certain populations or that make incorrect predictions for certain groups of patients. Overall, AI has the potential to improve the diagnosis and management of obstetrics and gynaecology conditions, and many studies have shown that AI systems can perform at least as well as human experts in several areas. However, it is important to note that AI and ML are still in the early stages of development in obstetrics and gynaecology and more research is needed to fully understand their potential benefits and limitations. Some of the key challenges facing the field include developing AI systems that can explain their decisions, improving the robustness of AI systems to adversarial attacks, and developing AI systems that can operate in a wide range of environments. However, it is important to note that AI is a complementary tool to the obstetrics and gynaecology specialist and it is not meant to replace human expertise. The preceding text is entirely a product of an AI system. The preceding review, Artificial Intelligence in Gynaecology: An Overview was composed and written by an evolutionary AI system, ChatGPT (Chat Generative Pre-trained Transformer). ChatGPT is an AI chatbot underpinned by the GPT architecture, an autoregressive language model that uses DL to produce human-like text. The system was trained on a dataset of over 500 GB of text data derived from books, articles, and websites prior to 2021. The system can engage in responsive dialogue, generate computer code, and produce coherent and fluent text.1 ChatGPT was conceived by OpenAI, an AI laboratory based in San Francisco, California, founded by Elon Musk and Sam Altman in 2015. Since its public release on November 30, 2022, the potential for use and misuse has exponentially grown,2 ultimately leading to the prohibition of the utilisation of AI systems by multiple organisations, including schools and universities. Prompted by this interest in AI, the aim of this study was to assess the capacity of ChatGPT to generate a scientific review. In January 2023, a multidisciplinary study group was assembled to develop the study protocol, confirm the methodology and approve the topic. This research was exempt from ethics review under National Health and Medical Research Council guidelines.3 ChatGPT was instructed to generate an narrative review based on dialogue with the lead author, AY. The input was informed by collaborative meetings of the study group over the study period. The study group nominated the topic, 'Artificial Intelligence in Gynaecology', but ChatGPT generated the title, structure and content for this paper. The study group defined the input parameters for ChatGPT and each AI output was reviewed by the authors for consistency and context, informing the next input. The dialogue thus became increasingly specific and refined in each iteration, as the initial general outline was expanded to include specific subheadings, academic language and academic references. The review was finalised from the ChatGPT output through an explicit composition protocol, limiting assembly to cut and paste, deletion to whole sentences (but not words) and conversion to Australian English. No grammatical or syntax correction was performed. The AI output was cross-referenced and verified by the study group. In this study, ChatGPT generated 7112 words in over 15 iterations, including 32 references. The output was restricted to the final review of 1809 words and nine unique references after removing duplicates4 and incorrect references (19). The final paper was submitted for blinded peer review. Thus, this study has demonstrated the capacity of an AI system, such as ChatGPT, to generate a scientific review through human academic instruction. AI is anticipated to expand the boundaries of evidence-based medicine through the potential of comprehensive analysis and summation of scientific publications. However, unlike systematic reviews or meta-analyses governed by explicit methodology, AI systems such as ChatGPT are the product of DL algorithms that are dependent upon the quality of the input to train the AI. Consequently, unlike systematic reviews, AI systems are bound by the bias, breadth, depth and quality of the training material. A dedicated medical AI would therefore be trained on an appropriate data set, such as the National Library of Medicine Medline/PubMed database. However, the volume of data is challenging: in 2022 alone, there were over 33 million citations equating to a dataset of almost 200 Gb for the minimum dataset. In contrast, ChatGPT has no external reference capabilities, such as access to the internet, search engines or any other sources of information outside of its own model. If forced outside of this framework, ChatGPT may generate plausible-sounding but incorrect or nonsensical responses.4 Most notably, pushing the AI to include references leads the system to generate bizarre fabrications.5 Our paper demonstrated that only 28% (9/32) of the references were authentic, although better than the 11% reported in a recent paper.6 In contrast to human writing, AI-generated content is more likely to be of limited depth, contain factual errors, fabricated references and repeat the instructions used to seed the output.7 The latter results in a formulaic language redundancy that all but identifies AI content. The human authors thus echo the conclusion of ChatGPT that AI is a complementary tool to the specialist and not meant to replace human expertise. For the moment. The authors report no conflicts of interest.

Bone age estimation (BAE) is based on skeletal maturity and degenerative process of the skeleton. The clinical importance of BAE is in understanding the pediatric and growth-related disorders; whereas medicolegally it is important in determining criminal responsibility and establishing identification. Artificial Intelligence (AI) has been used in the field of the field of medicine and specifically in diagnostics using medical images. AI can greatly benefit the BAE techniques by decreasing the intra observer and inter observer variability as well as by reducing the analytical time. The AI techniques rely on object identification, feature extraction and segregation. Bone age assessment is the classical example where the concepts of AI such as object recognition and segregation can be used effectively. The paper describes various AI based algorithms developed for the purpose of radiologic BAE and the performances of the models. In the current paper we have also carried out qualitative analysis using Strength, Weakness, Opportunities and Challenges (SWOC) to examine critical factors that contribute to the application of AI in BAE. To best of our knowledge, the SWOC analysis is being carried out for the first time to assess the applicability of AI in BAE. Based on the SWOC analysis we have provided strategies for successful implementation of AI in BAE in forensic and medicolegal context.

Artificial Intelligence for Computer Vision in Surgery: A Call for Developing Reporting Guidelines.

ObjectiveThis study aims to explore the clinical value of artificial intelligence (AI)-assisted bone age assessment (BAA) among children with growth hormone deficiency (GHD).MethodsA total of 290 bone age (BA) radiographs were collected from 52 children who participated in the study at Sun Yat-sen Memorial Hospital between January 2016 and August 2017. Senior pediatric endocrinologists independently evaluated BA according to the China 05 (CH05) method, and their consistent results were regarded as the gold standard (GS). Meanwhile, two junior pediatric endocrinologists were asked to assessed BA both with and without assistance from the AI-based BA evaluation system. Six months later, around 20% of the images assessed by the junior pediatric endocrinologists were randomly selected to be re-evaluated with the same procedure half a year later. Root mean square error (RMSE), mean absolute error (MAE), accuracy, and Bland-Altman plots were used to compare differences in BA. The intra-class correlation coefficient (ICC) and one-way repeated ANOVA were used to assess inter- and intra-observer variabilities in BAA. A boxplot of BA evaluated by different raters during the course of treatment and a mixed linear model were used to illustrate inter-rater effect over time.ResultsA total of 52 children with GHD were included, with mean chronological age and BA by GS of 6.64 ± 2.49 and 5.85 ± 2.30 years at baseline, respectively. After incorporating AI assistance, the performance of the junior pediatric endocrinologists improved (P < 0.001), with MAE and RMSE both decreased by more than 1.65 years (Rater 1: ΔMAE = 1.780, ΔRMSE = 1.655; Rater 2: ΔMAE = 1.794, ΔRMSE = 1.719), and accuracy increasing from approximately 10% to over 91%. The ICC also increased from 0.951 to 0.990. During GHD treatment (at baseline, 6-, 12-, 18-, and 24-months), the difference decreased sharply when AI was applied. Furthermore, a significant inter-rater effect (P = 0.002) also vanished upon AI involvement.ConclusionAI-assisted interpretation of BA can improve accuracy and decrease variability in results among junior pediatric endocrinologists in longitudinal cohort studies, which shows potential for further clinical application.

The demand for AI-driven solutions in musculoskeletal (MSK) imaging has risen alongside the surge in orthopedic imaging studies, reflecting the need for tools that enhance diagnostic accuracy, reduce healthcare costs, and alleviate physician workload. This review explores recent applications of AI-particularly computer vision and deep learning (DL)-in MSK imaging, from trauma and surgery to specialized and point-of-care technologies. The review also highlights existing challenges and limitations hindering the integration of these tools into clinical practice. AI applications are abundant in MSK imaging, with DL models showing remarkable versatility and success across multiple use cases. These include but are not limited to fracture detection, segmentation for preoperative planning, surgical navigation and tracking, tumor detection and classification, pediatric bone age estimation, and bone density measurement. Specialized use cases also target injury detection in sports medicine, and AI has been integrated into point-of-care technologies, such as motion-monitoring systems, underscoring AI's broad potential to improve diagnostic accuracy, reduce interpretation times, and increase efficiency. AI has shown promise in transforming MSK imaging, suggesting improvements in diagnostic performance, speed, and cost-efficiency. Despite research advances, challenges remain in deploying AI in real-world clinical settings, where model generalizability, data quality, and high computational demands pose obstacles. However, recent developments in AI, including the rise of adaptable foundation models and advancements in model efficiency, offer promising solutions that may accelerate the integration of AI into clinical workflows, bringing the field closer to realizing the full potential of AI in patient care.

To examine current literature on integrating emerging technologies, artificial intelligence (AI), and informatics into medical imaging education. A systematic review of peer-reviewed literature published in the past 5 years was conducted, focusing on medical imaging education, radiography curricula, AI applications, and ethical considerations. Articles were analyzed to identify recurring themes and trends in implementing AI and informatics in medical imaging education programs. Four key themes emerged from the literature: integration of emerging technologies and AI in medical imaging education; foundational informatics concepts and emerging technologies essential for medical imaging professionals; clinical applications of AI in medical imaging practice; and ethical and professional considerations regarding AI adoption. Integrating AI and informatics into medical imaging education is increasingly recognized as essential, but curriculum constraints, faculty preparedness, and the evolving nature of AI technologies are challenges to integration. Ethical concerns, including bias in AI algorithms and the potential effect on professional decision-making, highlight the need for responsible implementation. International efforts to establish AI educational frameworks are emerging that emphasize the importance of scaffolding learning to gradually build competency. To ensure the safe and effective use of AI in medical imaging, structured education and professional training must be prioritized. Future research should explore best practices for AI and informatics curriculum development, standardized assessment of AI literacy, and long-term effects of AI on clinical decision-making. By addressing these areas, medical imaging professionals can remain at the forefront of technological advancements while maintaining ethical responsibility and patient-centered care.

Background: Artificial intelligence (AI) has emerged as a disruptive force in a variety of medical disciplines, including dentistry. The article presents a comprehensive overview of the clinical applications of artificial intelligence in dental sciences. AI techniques such as machine learning, deep learning, and natural language processing are quickly being applied in dentistry to improve diagnosis accuracy, treatment planning, and patient outcomes. AI algorithms have performed admirably in diagnostics, detecting and classifying oral disorders such as dental caries, periodontal diseases, and oral malignancies using a variety of imaging modalities such as dental radiography, intraoral pictures, and 3D scans. AI algorithms help clinicians generate personalised treatment plans by analysing patient data such as medical history, genetic predispositions, and treatment outcomes. These technologies help to optimize therapy procedures, improve treatment success rates, and increase patient satisfaction. Furthermore, AI-based virtual reality and augmented reality platforms allow simulation-based training for dentistry students and practitioners, boosting their skills and decision-making abilities in complex clinical circumstances. Despite AI's tremendous promise in dental sciences, some hurdles persist, including data privacy concerns, regulatory compliance, and the need for interdisciplinary collaboration. Addressing these issues is critical for maximizing the benefits of AI in clinical practice and developing dental healthcare. To summarize, the incorporation of AI technologies has enormous potential for transforming dentistry practice by enhancing diagnosis accuracy, treatment planning, and patient outcomes. Continued study, collaboration, and innovation are required to fully realize AI's potential for improving dental care delivery and increasing oral health around the world.

Assessing perspectives on artificial intelligence applications to gastroenterology

Introduction: Applications of artificial intelligence (AI) in radiology continue to increase every year, however most radiology residencies lack a dedicated AI education curriculum. Fundamental AI education resources are even more sparse for trainees in low- to middle-income countries and under-resourced healthcare systems. The AI Literacy Course assesses the effectiveness and scalability of a free, remote AI education curriculum to increase understanding of fundamental AI terms, methods, and applications in radiology among radiology trainees in the United States and internationally. Method: A week-long AI in radiology literacy course for radiology trainees was held October 3-7, 2022. Ten 30-minute lectures utilizing a remote learning format covered basic AI terms and methods, clinical applications of AI in radiology by three different subspecialties, and special topics lectures. A proctored, hands-on clinical AI session allowed participants to directly use an FDA-cleared, AI-assisted viewer and reporting system for advanced cancer. Pre- and post-course electronic surveys were distributed to assess participants&amp;rsquo; knowledge of AI terminology and applications, as well as their interest in AI education. Results: A total of 25 residency programs throughout the US participated in the course with participants attending from 10 countries. An average of 150 participants viewed the course per day. Nearly all participants reported insufficient exposure to AI in their radiology training (95.8%). Participant knowledge of fundamental AI terms and methods increased after completion of the course, with an average pre-course evaluation of 8.3/15 and a post-course evaluation of 10.0/15 (p=0.01). Conclusion: The scalability of the AI Literacy Course demonstrates a viable model to bring accessible fundamental AI education to radiology trainees in the United States and internationally.

The potential of Artificial intelligence (AI) is increasingly recognized in musculoskeletal radiology, offering solutions to challenges posed by increasing imaging volumes and fellowship trained radiologist shortages. The integration of AI is not intended to replace radiologists but to augment their capabilities, improving workflow efficiency and diagnostic accuracy. This narrative review examines the current landscape of AI applications in musculoskeletal imaging, focusing on both general‐purpose multimodal models and specialized foundation models. AI has proven effective in musculoskeletal imaging, enhancing fracture detection, scoliosis assessment, and lower limb alignment analysis. In osteoarthritis, AI aids early detection by identifying subtle structural changes. AI‐accelerated MRI reconstruction reduces scan times by up to 90% while maintaining diagnostic quality, improving efficiency and accessibility. Emerging multimodal models further integrate imaging with clinical data, advancing precision medicine. Technical challenges persist, particularly in addressing motion artifacts and anatomical complexity. Ethical considerations, including data privacy, algorithmic bias, and model transparency, remain crucial for responsible implementation. While challenges remain in clinical validation and implementation, the combination of broad and narrow AI models shows promise in advancing precision medicine and democratizing quality care.Level of EvidenceLevel V.

Background and Objectives: Artificial intelligence (AI) has seen rapid integration into various areas of medicine, particularly with the advancement of machine learning (ML) and deep learning (DL) techniques. In pediatric orthopedics, the adoption of AI technologies is emerging but still not comprehensively reviewed. The purpose of this study is to review the latest evidence on the applications of artificial intelligence in the field of pediatric orthopedics. Materials and Methods: A literature search was conducted using PubMed and Web of Science databases to identify peer-reviewed studies published up to March 2024. Studies involving AI applications in pediatric orthopedic conditions—including spinal deformities, hip disorders, trauma, bone age assessment, and limb discrepancies—were selected. Eligible articles were screened and categorized based on application domains, AI models used, datasets, and reported outcomes. Results: AI has been successfully applied across several pediatric orthopedic subspecialties. In spinal deformities, models such as support vector machines and convolutional neural networks achieved over 90% accuracy in classification and curve prediction. For developmental dysplasia of the hip, deep learning algorithms demonstrated high diagnostic performance in radiographic interpretation. In trauma care, object detection models like YOLO and ResNet-based classifiers showed excellent sensitivity and specificity in pediatric fracture detection. Bone age estimation using DL models often matched or outperformed traditional methods. However, most studies lacked external validation, and many relied on small or single-institution datasets. Concerns were also raised about image quality, data heterogeneity, and clinical integration. Conclusions: AI holds significant potential to enhance diagnostic accuracy and decision making in pediatric orthopedics. Nevertheless, current research is limited by methodological inconsistencies and a lack of standardized validation protocols. Future efforts should focus on multicenter data collection, prospective validation, and interdisciplinary collaboration to ensure safe and effective clinical integration.