Current challenges of implementing artificial intelligence in medical imaging

This study aimed to investigate the knowledge, attitudes, and practice (KAP) of radiologists regarding artificial intelligence (AI) in medical imaging in the southeast of China. This cross-sectional study was conducted among radiologists in the Jiangsu, Zhejiang, and Fujian regions from October to December 2022. A self-administered questionnaire was used to collect demographic data and assess the KAP of participants towards AI in medical imaging. A structural equation model (SEM) was used to analyze the relationships between KAP. The study included 452 valid questionnaires. The mean knowledge score was 9.01±4.87, the attitude score was 48.96±4.90, and 75.22% of participants actively engaged in AI-related practices. Having a master's degree or above (OR=1.877, P=0.024), 5-10 years of radiology experience (OR=3.481, P=0.010), AI diagnosis-related training (OR=2.915, P<0.001), and engaging in AI diagnosis-related research (OR=3.178, P<0.001) were associated with sufficient knowledge. Participants with a junior college degree (OR=2.139, P=0.028), 5-10 years of radiology experience (OR=2.462, P=0.047), and AI diagnosis-related training (OR=2.264, P<0.001) were associated with a positive attitude. Higher knowledge scores (OR=5.240, P<0.001), an associate senior professional title (OR=4.267, P=0.026), 5-10 years of radiology experience (OR=0.344, P=0.044), utilizing AI diagnosis (OR=3.643, P=0.001), and engaging in AI diagnosis-related research (OR=6.382, P<0.001) were associated with proactive practice. The SEM showed that knowledge had a direct effect on attitude (β=0.481, P<0.001) and practice (β=0.412, P<0.001), and attitude had a direct effect on practice (β=0.135, P<0.001). Radiologists in southeastern China hold a favorable outlook on AI-assisted medical imaging, showing solid understanding and enthusiasm for its adoption, despite half lacking relevant training. There is a need for more AI diagnosis-related training, an efficient standardized AI database for medical imaging, and active promotion of AI-assisted imaging in clinical practice. Further research with larger sample sizes and more regions is necessary.

In the last few years, with the rapid development of artificial intelligence (AI) technology, its application in the medical field has become more and more in-depth and extensive, especially the research of computer vision technology in medical image analysis and recognition has achieved many results. It is recognized by experts engaged in imaging diagnosis in the medical field, which has provided a great boost to the development of precision medicine. However, the application and development of artificial intelligence in the field of medical imaging is still in its infancy, and faces many technical and ethical challenges, such as data quality issues, data security issues and clinical application issues. The application of AI technology in medical imaging needs to be further adjusted and improved in combination with social development and actual needs. In this paper, we discussed the usages of artificial intelligence in medical imaging and provided new sights for the sustainable development and improvement in this area, as well as the challenges faced by others.

Introduction: Medical imaging is essential for detecting pathologies, but it depends on the radiologist's experience. Additionally, artificial intelligence has emerged as a promising tool to improve accuracy and efficiency, although it is still debated whether it can replace the radiologist or should be a complement. Objective: To evaluate the role of artificial intelligence in medical imaging, analyzing its ability to complement or replace professionals in interpreting medical studies. Also, to identify the advantages, limitations, and impact of AI in modern radiology. Method: A systematic literature review was conducted on the use of artificial intelligence in medical imaging, focusing on studies from the last 5 years. The search included articles from high-impact scientific journals. Results: Artificial intelligence has demonstrated performance similar to that of radiologists in interpreting images of common pathologies. However, its clinical implementation faces several challenges, such as the need for large volumes of high-quality data and the difficulty of addressing complex clinical situations. Additionally, the importance of human supervision in decision-making and contextual image interpretation is emphasized, as AI cannot fully replicate this. Conclusions: AI should not be considered a replacement for the radiologist but rather a complement that helps reduce errors, optimize time, and enable more accurate medical care. The successful integration of AI in radiology depends on continuous collaboration between technology and healthcare professionals, as well as proper training and regulation.

IntroductionWhile artificial intelligence (AI) and recent developments in deep learning (DL) have sparked interest in medical imaging, there has been little commentary on the impact of AI on imaging technologists. The aim of this survey was to understand the attitudes, applications and concerns among nuclear medicine and radiography professionals in Australia with regard to the rapidly emerging applications of AI.MethodsAn anonymous online survey with invitation to participate was circulated to nuclear medicine and radiography members of the Rural Alliance in Nuclear Scintigraphy and the Australian Society of Medical Imaging and Radiation Therapy. The survey invitations were sent to members via email and as a push via social media with the survey open for 10 weeks. All information collected was anonymised and there is no disclosure of personal information as it was de‐identified from commencement.ResultsAmong the 102 respondents, there was a high level of acceptance of lower order tasks (e.g. patient registration, triaging and dispensing) and less acceptance of high order task automation (e.g. surgery and interpretation). There was a low priority perception for the role of AI in higher order tasks (e.g. diagnosis, interpretation and decision making) and high priority for those applications that automate complex tasks (e.g. quantitation, segmentation, reconstruction) or improve image quality (e.g. dose / noise reduction and pseudo CT for attenuation correction). Medico‐legal, ethical, diversity and privacy issues posed moderate or high concern while there appeared to be no concern regarding AI being clinically useful and improving efficiency. Mild concerns included redundancy, training bias, transparency and validity.ConclusionAustralian nuclear medicine technologists and radiographers recognise important applications of AI for assisting with repetitive tasks, performing less complex tasks and enhancing the quality of outputs in medical imaging. There are concerns relating to ethical aspects of algorithm development and implementation.

Continual learning in medical devices: FDA's action plan and beyond

Artificial Intelligence (AI) algorithms showcase the potential to steer a paradigm shift in clinical medicine, especially medical imaging. Concerns associated with model generalizability and biases necessitate rigorous external validation of AI algorithms prior to their adoption into clinical workflows. To address the barriers associated with patient privacy, intellectual property, and diverse model requirements, we introduce ClinValAI, a framework for establishing robust cloud-based infrastructures to clinically validate AI algorithms in medical imaging. By featuring dedicated workflows for data ingestion, algorithm scoring, and output processing, we propose an easily customizable method to assess AI models and investigate biases. Our novel orchestration mechanism facilitates utilizing the complete potential of the cloud computing environment. ClinValAI’s input auditing and standardization mechanisms ensure that inputs consistent with model prerequisites are provided to the algorithm for a streamlined validation. The scoring workflow comprises multiple steps to facilitate consistent inferencing and systematic troubleshooting. The output processing workflow helps identify and analyze samples with missing results and aggregates final outputs for downstream analysis. We demonstrate the usability of our work by evaluating a state-of-the-art breast cancer risk prediction algorithm on a large and diverse dataset of 2D screening mammograms. We perform comprehensive statistical analysis to study model calibration and evaluate performance on important factors, including breast density, age, and race, to identify latent biases. ClinValAI provides a holistic framework to validate medical imaging models and has the potential to advance the development of generalizable AI models in clinical medicine and promote health equity.

Evaluation of a classroom-based medical imaging artificial intelligence educational intervention in Ghana: A pre-test/post-test study design.

Artificial intelligence (AI) is currently developing very efficiently, and its applications are valuable in many fields of science, including medicine, mainly because of its ability to ensure accuracy, objectivity and automation, in particular, in the diagnostic process. Rapid development of diagnostic technologies provides an opportunity to introduce innovative solutions into modern medicine through the use of AI, which makes it possible to relieve medical workers by speeding up the diagnostic process and improving its quality as well as effectiveness of subsequent special treatment based on its results. This review briefly presents the current state of knowledge and a number of existing AI models applied in everyday practice in medical imaging. AI has great potential to transform X-ray diagnostics and other areas of medicine, especially in the analysis of medical images. Despite the difficulties associated with AI implementation in practice, such as the need for proper staff training and ethical issues, the advantages of its application are very significant. AI can help improve diagnostic accuracy, speed up the diagnostic process itself, and reduce medical costs. Further development of AI technologies combined with the constant cooperation between Russian AI developers and medical professionals will contribute to even greater advances in healthcare, which will undoubtedly benefit both patients and staff of medical institutions.

Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): This work was supported by an NIHR AI Award, AI_AWARD01706. This research was also funded in part, by the Wellcome Trust [Grant number 205188/Z/16/Z ]. Background There has been a rapid increase in the number of Artificial Intelligence (AI) studies of cardiac MRI (CMR) segmentation. AI has huge potential to improve image analysis assessments. However, advancement and clinical translation in this field depend on researchers presenting their work in a transparent and reproducible manner. Purpose This systematic review aimed to evaluate the quality of reporting in AI studies involving CMR segmentation. Methods MEDLINE and EMBASE databases were searched for AI CMR segmentation studies on 18/11/2021. The flow of study inclusion is shown in Figure 1. Any AI method to segment any cardiac structure on CMR was eligible for inclusion. Each study was assessed for compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Results 70 studies were included in the qualitative analysis. Studies were published between 2015 to 2021, with the majority (71%) published in 2020 and 2021. Most studies were performed in Europe (33%), China (27%) and the USA (26%). Short-axis sections were segmented in 70% of studies and most commonly included both ventricles (51%) or the left ventricle alone (30%). 20 different architecture implementations were represented. Figure 2 summarises the most relevant CLAIM domains to AI segmentation. The training sample eligibility criteria, demographics and clinical characteristics were not reported in 47% and 81% of studies, respectively. Ground truth annotations, source of the annotations and annotation tool were absent in 31%, 36% and 51% of studies respectively. Preprocessing steps and software libraries and packages used in training were not included in 27% and 24%. Details on the training approach including the number of models trained and method of selecting the final model were missing in 20% and 17% of the studies. Methods of validation or testing on external data, inter- and intra- rater variability and failure analysis were unreported in 57%, 63% and 74%, respectively. Conclusion This systematic review highlights important gaps in the AI literature of CMR studies. We identified key items missing in the dataset description, model development, validation and testing that limit the transparency, reproducibility and hence validity of published AI studies. This review may support closer adherence to established frameworks for reporting standards.

Terrance T Healey is the director of Thoracic Radiology at Rhode Island Hospital (RI, USA) and an Assistant Professor of Diagnostic Imaging at the Alpert Medical School of Brown University (RI, USA). Healey received his medical degree from the combined Dartmouth Medical School–Brown Medical School Program in 2003, completed his residency in radiology at Brown University in 2008 and a thoracic radiology fellowship at the Massachusetts General Hospital (MA, USA) in 2009. He joined the faculty staff at Rhode Island Hospital and Brown University in 2009. Robert C Ward is one of the chief residents within the Department of Diagnostic Imaging at Rhode Island Hospital (RI, USA) and the Alpert Medical School of Brown University (RI, USA). Ward received his medical degree from the George Washington University (Washington, DC, USA) in 2010. Damian E Dupuy is the director of Tumor Ablation at Rhode Island Hospital (RI, USA) and a Professor of Diagnostic Imaging at The Warren Alpert Medical School of Brown University (RI, USA). Dupuy received his medical degree from the University of Massachusetts Medical School (MA, USA) in 1988 and completed his residency in radiology at The New England Deaconess Hospital (MA, USA) and Harvard Medical School (MA, USA) in 1993. After residency, Dupuy joined the staff at Massachusetts General Hospital (MA, USA) where he worked in the Abdominal Imaging and Bone and Joint Divisions. In 1997, Dupuy joined the Department of Diagnostic Imaging at Rhode Island Hospital and Brown University. Dupuy, a pioneer in the use of image-guided ablation, helped broaden clinical applications to successfully combat cancer involving the kidney, liver, lung, head and neck, adrenal gland and skeleton. Other newer technologies, such as percutaneous microwave ablation, cryoablation and combination therapies using radiofrequency ablation with external radiation or brachytherapy, have been pioneered by Dupuy who has been the principal investigator of two National Cancer Institute-funded multicenter trials. Dupuy has received national awards for research and teaching from the American College of Radiology Imaging Network and the Radiological Society of North America where he is currently the Chair of the Interventional Oncology Symposium featured at the Annual Meeting of the Radiological Society of North America and a Fellow of the American College of Radiology. Dupuy is a member of the Radiological Society of North America, The New England Roentgen Ray Society, The American College of Radiology, Rhode Island Radiological Society and the Society of Interventional Radiology. Dupuy has published over 150 publications and given over 120 invited lectures in the field of radiology and image-guided ablation, both nationally and internationally.

BackgroundOpinions seem somewhat divided when considering the effect of artificial intelligence (AI) on medical imaging. The aim of this study was to characterise viewpoints presented online relating to the impact of AI on the field of radiology and to assess who is engaging in this discourse.MethodsTwo search methods were used to identify online information relating to AI and radiology. Firstly, 34 terms were searched using Google and the first two pages of results for each term were evaluated. Secondly, a Rich Search Site (RSS) feed evaluated incidental information over 3 weeks. Webpages were evaluated and categorized as having a positive, negative, balanced, or neutral viewpoint based on study criteria.ResultsOf the 680 webpages identified using the Google search engine, 248 were deemed relevant and accessible. 43.2% had a positive viewpoint, 38.3% a balanced viewpoint, 15.3% a neutral viewpoint, and 3.2% a negative viewpoint. Peer-reviewed journals represented the most common webpage source (48%), followed by media (29%), commercial sources (12%), and educational sources (8%). Commercial webpages had the highest proportion of positive viewpoints (66%). Radiologists were identified as the most common author group (38.9%). The RSS feed identified 177 posts of which were relevant and accessible. 86% of posts were of media origin expressing positive viewpoints (64%).ConclusionThe overall opinion of the impact of AI on radiology presented online is a positive one. Consistency across a range of sources and author groups exists. Radiologists were significant contributors to this online discussion and the results may impact future recruitment.

The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.

Open-source artificial intelligence models (OSAIM) find free applications in various industries, including information technology and medicine. Their clinical potential, especially in supporting diagnosis and therapy, is the subject of increasingly intensive research. Due to the growing interest in artificial intelligence (AI) for diagnostic purposes, we conducted a study evaluating the capabilities of AI models, including ChatGPT and Microsoft Bing, in the diagnosis of single-curve scoliosis based on posturographic radiological images. Two independent neurosurgeons assessed the degree of spinal deformation, selecting 23 cases of severe single-curve scoliosis. Each posturographic image was separately implemented onto each of the mentioned platforms using a set of formulated questions, starting from ‘What do you see in the image?’ and ending with a request to determine the Cobb angle. In the responses, we focused on how these AI models identify and interpret spinal deformations and how accurately they recognize the direction and type of scoliosis as well as vertebral rotation. The Intraclass Correlation Coefficient (ICC) with a ‘two-way’ model was used to assess the consistency of Cobb angle measurements, and its confidence intervals were determined using the F test. Differences in Cobb angle measurements between human assessments and the AI ChatGPT model were analyzed using metrics such as RMSEA, MSE, MPE, MAE, RMSLE, and MAPE, allowing for a comprehensive assessment of AI model performance from various statistical perspectives. The ChatGPT model achieved 100% effectiveness in detecting scoliosis in X-ray images, while the Bing model did not detect any scoliosis. However, ChatGPT had limited effectiveness (43.5%) in assessing Cobb angles, showing significant inaccuracy and discrepancy compared to human assessments. This model also had limited accuracy in determining the direction of spinal curvature, classifying the type of scoliosis, and detecting vertebral rotation. Overall, although ChatGPT demonstrated potential in detecting scoliosis, its abilities in assessing Cobb angles and other parameters were limited and inconsistent with expert assessments. These results underscore the need for comprehensive improvement of AI algorithms, including broader training with diverse X-ray images and advanced image processing techniques, before they can be considered as auxiliary in diagnosing scoliosis by specialists.

Artificial intelligence in medical Imaging: An analysis of innovative technique and its future promise

Social Media Growth at Annual Medical Society Meetings: A Comparative Analysis of Diagnostic and Interventional Radiology to Other Medical Specialties