Strengths,Weaknesses, Opportunities,and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology

Currently, the use of artificial intelligence (AI) in radiology, particularly machine learning (ML), has become a reality in clinical practice. Since the end of the last century, several ML algorithms have been introduced for a wide range of common imaging tasks, not only for diagnostic purposes but also for image acquisition and postprocessing. AI is now recognized to be a driving initiative in every aspect of radiology. There is growing evidence of the advantages of AI in radiology creating seamless imaging workflows for radiologists or even replacing radiologists. Most of the current AI methods have some internal and external disadvantages that are impeding their ultimate implementation in the clinical arena. As such, AI can be considered a portion of a business trying to be introduced in the health care market. For this reason, this review analyzes the current status of AI, and specifically ML, applied to radiology from the scope of strengths, weaknesses, opportunities, and threats (SWOT) analysis.     

Proceedings from the Society of Interventional Radiology Foundation Research Consensus Panel on Artificial Intelligence in Interventional Radiology: From Code to Bedside

Artificial intelligence (AI)-based technologies are the most rapidly growing field of innovation in healthcare with the promise to achieve substantial improvements in delivery of patient care across all disciplines of medicine. Recent advances in imaging technology along with marked expansion of readily available advanced health information, data offer a unique opportunity for interventional radiology (IR) to reinvent itself as a data-driven specialty. Additionally, the growth of AI-based applications in diagnostic imaging is expected to have downstream effects on all image-guidance modalities. Therefore, the Society of Interventional Radiology Foundation has called upon 13 key opinion leaders in the field of IR to develop research priorities for clinical applications of AI in IR. The objectives of the assembled research consensus panel were to assess the availability and understand the applicability of AI for IR, estimate current needs and clinical use cases, and assemble a list of research priorities for the development of AI in IR. Individual panel members proposed and all participants voted upon consensus statements to rank them according to their overall impact for IR. The results identified the top priorities for the IR research community and provide organizing principles for innovative academic-industrial research collaborations that will leverage both clinical expertise and cutting-edge technology to benefit patient care in IR.     

Artificial Intelligence and Clinical Decision Support for Radiologists and Referring Providers

Recent advances in artificial intelligence (AI) are providing an opportunity to enhance existing clinical decision support (CDS) tools to improve patient safety and drive value-based imaging. We discuss the advantages and potential applications that may be realized with the synergy between AI and CDS systems. From the perspective of both radiologist and ordering provider, CDS could be significantly empowered using AI. CDS enhanced by AI could reduce friction in radiology workflows and can aid AI developers to identify relevant imaging features their tools should be seeking to extract from images. Furthermore, these systems can generate structured data to be used as input to develop machine learning algorithms, which can drive downstream care pathways. For referring providers, an AI-enabled CDS solution could enable an evolution from existing imaging-centric CDS toward decision support that takes into account a holistic patient perspective. More intelligent CDS could suggest imaging examinations in highly complex clinical scenarios, assist on the identification of appropriate imaging opportunities at the health system level, suggest appropriate individualized screening, or aid health care providers to ensure continuity of care. AI has the potential to enable the next generation of CDS, improving patient care and enhancing providers’ and radiologists’ experience.     

基于深度学习的人工智能在肌骨影像中的研究进展

人工智能(AI)已成为当今社会信息技术领域最重要的技术革命,随着深度学习算法的进步及硬件的升级,人工智能发展迅猛.基于深度学习的人工智能在医学影像的图像分割、图像分类识别和计算机辅助诊断方面都有较大的发展,本文主要讲述人工智能在肌骨影像中的研究进展.     

Improving outcomes in radiology: bringing computer-based decision support and education to the point of care

Many computer applications have been developed in radiology and other medical disciplines to help physicians make decisions. Artificial intelligence (AI)--an approach to computer-based manipulation of symbols to simulate human reasoning--forms the basis of many of these systems. This article's goals are to: acquaint the reader with the motivations and opportunities for computer-based medical decision support systems; identify AI techniques and applications in radiology decision making; assess the impact of these technologies; and consider new directions and opportunities for AI in radiology. Among the exciting new directions is the use of AI to integrate radiology reporting, online decision support, and just-in-time learning to provide useful information and continuing education that is embedded within a radiologist's daily workflow.     

Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology

Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of medical data from patients. This data could be used for purposes such as predicting disease, diagnosis, treatment optimization, and prognostication. Radiology is positioned to lead development and implementation of AI algorithms and to manage the associated ethical and legal challenges. This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally, opportunities in AI from the perspective of a universal health care system.     

Artificial Intelligence and the Trainee Experience in Radiology

The hype around artificial intelligence (AI) in radiology continues unabated, despite the fact that the exact role AI will play in future radiology practice remains undefined. Nevertheless, education of the radiologists of the future is ongoing and needs to account for the uncertainty of this new technology. Radiology residency training has evolved even before the recent advent of imaging AI. Yet radiology residents and fellows will likely one day experience the benefits of an AI-enabled clinical training. This will offer them a customized learning experience and the ability to analyze large quantities of data about their progress in residency, with substantially less manual effort than is currently required. Additionally, they will need to learn how to interact with AI tools in clinical practice and, more importantly, understand how to evaluate AI outputs in a critical fashion as yet another piece of information contributing to the interpretation of an imaging examination. Although the exact role AI will play in the future practice of radiology remains undefined, it will surely be integrated into the education of future radiologists.     

Workflow Applications of Artificial Intelligence in Radiology and an Overview of Available Tools

In the past decade, there has been tremendous interest in applying artificial intelligence (AI) to improve the field of radiology. Currently, numerous AI applications are in development, with potential benefits spanning all steps of the imaging chain from test ordering to report communication. AI has been proposed as a means to optimize patient scheduling, improve worklist management, enhance image acquisition, and help radiologists interpret diagnostic studies. Although the potential for AI in radiology appears almost endless, the field is still in the early stages, with many uses still theoretical, in development, or limited to single institutions. Moreover, although the current use of AI in radiology has emphasized its clinical applications, some of which are in the distant future, it is increasingly clear that AI algorithms could also be used in the more immediate future for a variety of noninterpretive and quality improvement uses. Such uses include the integration of AI into electronic health record systems to reduce unwarranted variation in radiologists’ follow-up recommendations and to improve other dimensions of radiology report quality. In the end, the potential of AI in radiology must be balanced with acknowledgment of its current limitations regarding generalizability and data privacy.     

Radiology-Pathology Correlation to Facilitate Peer Learning: An Overview Including Recent Artificial Intelligence Methods

Correlation of pathology reports with radiology examinations has long been of interest to radiologists and helps to facilitate peer learning. Such correlation also helps meet regulatory requirements, ensures quality, and supports multidisciplinary conferences and patient care. Additional offshoots of such correlation include evaluating for and ensuring concordance of pathology results with radiology interpretation and procedures as well as ensuring specimen adequacy after biopsy. For much of the history of radiology, this correlation has been done manually, which is time consuming and cumbersome and provides coverage of only a fraction of radiology examinations performed. Electronic storage and indexing of radiology and pathology information laid the foundation for easier access and for the development of automated artificial intelligence methods to match pathology information with radiology reports. More recent techniques have resulted in near comprehensive coverage of radiology examinations with methods to present results and solicit feedback from end users. Newer deep learning language modeling techniques will advance these methods by providing more robust automated and comprehensive radiology-pathology correlation with the ability to rapidly, flexibly, and iteratively tune models to site and user preference.     

Bending the Artificial Intelligence Curve for Radiology: Informatics Tools From ACR and RSNA

Artificial intelligence (AI) will reshape radiology over the coming years. The radiology community has a strong history of embracing new technology for positive change, and AI is no exception. As with any new technology, rapid, successful implementation faces several challenges that will require creation and adoption of new integration technology. Use cases important to real-world application of AI are described, including clinical registries, AI research, AI product validation, and computer assistance for radiology reporting. Furthermore, the informatics technologies required for successful implementation of the use cases are described, including open Computer-Assisted Radiologist Decision Support, ACR Assist, ACR Data Science Institute use cases, common data elements (radelement.org), RadLex (radlex.org), LOINC/RSNA RadLex Playbook (loinc.org), and Radiology Report Templates (radreport.org).     

A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence–Powered Ultrasound for Improving Clinical Workflow

Ultrasound is the most commonly used imaging modality in clinical practice because it is a nonionizing, low-cost, and portable point-of-care imaging tool that provides real-time images. Artificial intelligence (AI)–powered ultrasound is becoming more mature and getting closer to routine clinical applications in recent times because of an increased need for efficient and objective acquisition and evaluation of ultrasound images. Because ultrasound images involve operator-, patient-, and scanner-dependent variations, the adaptation of classical machine learning methods to clinical applications becomes challenging. With their self-learning ability, deep-learning (DL) methods are able to harness exponentially growing graphics processing unit computing power to identify abstract and complex imaging features. This has given rise to tremendous opportunities such as providing robust and generalizable AI models for improving image acquisition, real-time assessment of image quality, objective diagnosis and detection of diseases, and optimizing ultrasound clinical workflow. In this report, the authors review current DL approaches and research directions in rapidly advancing ultrasound technology and present their outlook on future directions and trends for DL techniques to further improve diagnosis, reduce health care cost, and optimize ultrasound clinical workflow.     

Understanding artificial intelligence based radiology studies: CNN architecture

Artificial intelligence (AI) in radiology has gained wide interest due to the development of neural network architectures with high performance in computer vision related tasks. As AI based software programs become more integrated into the clinical workflow, radiologists can benefit from better understanding the principles of artificial intelligence. This series aims to explain basic concepts of AI and its applications in medical imaging. In this article, we will review the background of neural network architecture and its application in imaging analysis.     

Machine Learning in Radiology: Applications Beyond Image Interpretation

Much attention has been given to machine learning and its perceived impact in radiology, particularly in light of recent success with image classification in international competitions. However, machine learning is likely to impact radiology outside of image interpretation long before a fully functional “machine radiologist” is implemented in practice. Here, we describe an overview of machine learning, its application to radiology and other domains, and many cases of use that do not involve image interpretation. We hope that better understanding of these potential applications will help radiology practices prepare for the future and realize performance improvement and efficiency gains.     

The time is now: making the case for a UK registry of deployment of radiology artificial intelligence applications

Artificial intelligence (AI)-based healthcare applications (apps) are rapidly evolving, and radiology is a target specialty for their implementation. In this paper, we put the case for a national deployment registry to track the spread of AI apps into clinical use in radiology in the UK. By gathering data on the specific locations, purposes, and people associated with AI app deployment, such a registry would provide greater transparency on their spread in the radiology field. In combination with other regulatory and audit mechanisms, it would provide radiologists and patients with greater confidence and trust in AI apps. At the same time, coordination of this information would reduce costs for the National Health Service (NHS) by preventing duplication of piloting activities. This commentary discusses the need for a UK-wide registry for such apps, its benefits and risks, and critical success factors for its establishment. We conclude by noting that a critical window of opportunity has opened up for the development of a deployment registry, before the current pattern of localised clusters of activity turns into the widespread proliferation of AI apps across clinical practice.     

Deep Learning for Natural Language Processing in Radiology—Fundamentals and a Systematic Review

PurposeNatural language processing (NLP) enables conversion of free text into structured data. Recent innovations in deep learning technology provide improved NLP performance. We aimed to survey deep learning NLP fundamentals and review radiology-related research.MethodsThis systematic review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched for deep learning NLP radiology studies published up to September 2019. MEDLINE, Scopus, and Google Scholar were used as search databases.ResultsTen relevant studies published between 2018 and 2019 were identified. Deep learning models applied for NLP in radiology are convolutional neural networks, recurrent neural networks, long short-term memory networks, and attention networks. Deep learning NLP applications in radiology include flagging of diagnoses such as pulmonary embolisms and fractures, labeling follow-up recommendations, and automatic selection of imaging protocols. Deep learning NLP models perform as well as or better than traditional NLP models.ConclusionResearch and use of deep learning NLP in radiology is increasing. Acquaintance with this technology can help prepare radiologists for the coming changes in their field.     

Dear Medical Students - Artificial Intelligence is Not Taking Away a Radiologist's Job

Given limited exposure to radiology during the pre-clinical and clinical years, it has been challenging to recruit medical students to radiology. Now, many medical students considering radiology as a career are deterred due to misinformation surrounding how AI implementation will affect radiologists in the future. Artificial Intelligence (AI) has the potential to revolutionize the way in which medicine is practiced, especially in the field of radiology, and will ultimately support radiologists and advance the specialty. We aimed to provide a basic guide for medical students on the application of artificial intelligence in radiology, address misconceptions, highlight the role radiologists will play in AI development, and discuss the challenges faced in the future.     

基于深度学习的乳腺X线摄影的临床应用价值

乳腺X线摄影是乳腺癌筛查的有效手段,但具有一定局限性。人工智能（AI）具有提取图像特征并分析的强大能力,是推动未来智能医学影像进步的核心技术。近年来深度学习（DL）在乳腺X线摄影上的应用迅速发展,能够提高医生的工作效率、诊断准确率并降低漏诊率。对基于DL的乳腺X线摄影在乳腺癌筛查、临床诊断及风险评估中的应用价值和发展前景予以综述与展望。     

Social media's role in the perception of radiologists and artificial intelligence

Social media are impacting all industries and changing the way daily interactions take place. This has been notable in health care as it allows a mechanism to connect patients directly to physicians, advocacy groups, and health care information. Recently, the development of artificial intelligence (AI) applications in radiology has drawn media attention. This has generated a conversation on social media about the expendable role of a radiologist. Often, articles in the lay press have little medical expertise informing opinions about artificial intelligence in radiology. We propose solutions for radiologists to take the lead in the narrative on social media about the role of AI in radiology to better inform and shape public perception about the role of AI in radiology.     

Artificial Intelligence: A Private Practice Perspective

Artificial intelligence (AI) is an exciting technology that can transform the practice of radiology. However, radiology AI is still immature with limited adopters, dominated by academic institutions, and few use cases in general practice. With scale and a focus on innovation, our practice has had the opportunity to be an early adopter of AI technology. We have gained experience identifying use cases that provide value for our patients and practice; selecting AI products and vendors; piloting vendors’ AI algorithms; creating our own AI algorithms; implementing, optimizing, and maintaining these algorithms; garnering radiologist acceptance of these tools; and integrating AI into our radiologists’ daily workflow. With this experience, our practice has both managed challenges and identified unexpected benefits of AI. To ensure a successful and scalable AI implementation, multiple steps are required, including preparing the data, systems, and radiologists. This article reviews our experience with AI and describes why each step is important.