Commentary: The Future of Generative Artificial Intelligence in Radiology.

AI and ML in radiology: Making progress

Impact of the rise of artificial intelligence in radiology: What do radiologists think?

Artificial intelligence is playing an increasingly important role in radiology. However, more and more often it is no longer possible to reconstruct decisions, especially in the case of new and powerful methods from the field of deep learning. The resulting models fulfill their function without the users being able to understand the internal processes and are used as so-called black boxes. Especially in sensitive areas such as medicine, the explainability of decisions is of paramount importance in order to verify their correctness and to be able to evaluate alternatives. For this reason, there is active research going on to elucidate these black boxes. This review paper presents different approaches for explainable artificial intelligence with their advantages and disadvantages. Examples are used to illustrate the introduced methods. This study is intended to enable the reader to better assess the limitations of the corresponding explanations when meeting them in practice and strengthen the integration of such solutions in new research projects. Besides methods to analyze black-box models for explainability, interpretable models offer an interesting alternative. Here, explainability is part of the process and the learned model knowledge can be verified with expert knowledge. · The use of artificial intelligence in radiology offers many possibilities to provide safer and more efficient medical care. This includes, but is not limited to support during image acquisition and processing or for diagnosis.. · Complex models can achieve high accuracy, but make it difficult to understand data processing.. · If the explainability is already taken into account during the planning of the model, methods can be developed that are powerful and interpretable at the same time.. · Gallée L, Kniesel H, Ropinski T et al. Artificial intelligence in radiology - beyond the black box. Fortschr Röntgenstr 2023; DOI: 10.1055/a-2076-6736.

entitled "Artificial intelligence in radiology: Diagnostic sensitivity of ChatGPT for detecting hemorrhages in cranial computed tomography scans".The authors evaluated the performance of Generative Pre-trained Transformer 4 with Vision (GPT-4V) in detecting intracranial hemorrhage on non-contrast cranial computed tomography (CT) and demonstrated a marked increase in sensitivity following guided prompting.We commend the authors for addressing a timely and clinically relevant topic.Nevertheless, we believe several methodological considerations merit further discussion.

Artificial Intelligence in Radiology––The State of the Future

HomeRadiology: Artificial IntelligenceVol. 1, No. 1 Next EditorialFree AccessArtificial Intelligence, Real RadiologyCharles E. Kahn, Jr Charles E. Kahn, Jr Charles E. Kahn, Jr Published Online:Jan 30 2019https://doi.org/10.1148/ryai.2019184001MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In IntroductionGlendower:I can call spirits from the vasty deep.Hotspur:Why, so can I, or so can any man; But will they come when you do call for them?– William Shakespeare, Henry IV, Part IWelcome to this inaugural issue of Radiology: Artificial Intelligence. Our journal’s mission is to publish high-quality scientific work that advances our understanding of artificial intelligence (AI) in radiology.AI has become a topic of great interest—especially the application of machine learning techniques to medical images—but AI itself is not new. The term artificial intelligence was proposed in 1956 to describe efforts to understand, simulate, and improve upon human qualities such as reasoning, learning, solving problems, understanding verbal and written language, processing visual information, and playing games like chess and poker.What is new is a resurgence of interest in AI, particularly in the use of machine learning to recognize patterns in images. And, curiously, it is game playing that has opened this new frontier—but not the games of chess, checkers, or Go. Rather, think Xbox and PlayStation. Video games require a rapidly changing three-dimensional scene to be transformed into two-dimensional images shown in real time. The need to compute images efficiently spurred the development of highly parallelized graphics processing units. These specialized processors, in turn, have powered software for increasingly complex and sophisticated “deep” artificial neural network models. Whereas neural networks developed 10 years ago typically had three or four layers, today’s deep networks comprise hundreds of layers (1).Deep learning models have engendered both great excitement and a great deal of hyperbole. After all, if AI systems can pick out pictures of cats on the web, then surely such systems are ready to replace radiologists, right? Well, perhaps not, at least not right now. There is much work to be done to build and validate systems that can detect and characterize the thousands of imaging findings and their associated diseases that can be seen across a panoply of radiology studies.And that brings us to the quote from Shakespeare. Anyone can claim to build an AI system, but that doesn’t mean that the system will do their bidding as imagined. Our journal is here to assure that the science and applications of AI in radiology are built on thoughtful, innovative, and well-validated research.What sorts of topics will this journal publish? We will bring you the same high caliber of research that is found in RSNA’s flagship scientific journal, Radiology, but focused here on AI, machine learning, and data science in radiology. In particular, we seek to publish first-rate work that provides rigorous evaluation of AI’s applications to clinical problems in radiology.We invite manuscripts that show the impact of AI to extract information, diagnose and manage disease in patients, streamline radiology workflow, or improve health care outcomes. We’re interested in image segmentation, image reconstruction, automated detection of abnormalities, diagnostic reasoning, natural language processing, clinical workflow analysis, radiomics, and radiogenomics. We also invite manuscripts that demonstrate novel applications of AI in radiology or highlight innovative AI methodologies. Developers of publicly available sets of radiologic images, image annotations, radiology reports, or algorithms can present their work as a Data Resources report.AI and radiology do not exist in isolation: they are part of broad endeavors to advance knowledge and improve health. As such, this journal will feature articles on the ethical, legal, social, and economic implications of AI in radiology.AI is and must be a human—and humane—activity (2). We must engage in this work with an eye to how these technologies will help us care for our patients more effectively and humanely. Our goal is not to replace, but rather to extend our human abilities to provide medical care—and to improve the lives of those we are privileged to serve.All RSNA members receive access to this online bimonthly journal. We invite all readers (RSNA members or not) to sign up for our Editor’s Blog, The Vasty Deep (https://pubs.rsna.org/page/ai/blog) and to follow us on Twitter (@Radiology_AI). These social media platforms will augment the journal and offer innovative online features.Again, welcome!

Generative artificial intelligence (AI) has been applied to images for image quality enhancement, domain transfer, and augmentation of training data for AI modeling in various medical fields. Image-generative AI can produce large amounts of unannotated imaging data, which facilitates multiple downstream deep-learning tasks. However, their evaluation methods and clinical utility have not been thoroughly reviewed. This article summarizes commonly used generative adversarial networks and diffusion models. In addition, it summarizes their utility in clinical tasks in the field of radiology, such as direct image utilization, lesion detection, segmentation, and diagnosis. This article aims to guide readers regarding radiology practice and research using image-generative AI by 1) reviewing basic theories of image-generative AI, 2) discussing the methods used to evaluate the generated images, 3) outlining the clinical and research utility of generated images, and 4) discussing the issue of hallucinations.

Artificial intelligence (AI) has transformed the world and influences every aspect of our lives. AI has revolutionized industries, healthcare, and daily life tasks. Machine learning (ML) algorithms are used in AI to mimic how people think, reason, and learn. ML techniques can be classified into four categories based on the learning method and the desired outcome: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. A subfield of ML called deep learning (DL). A DL algorithm uses layers of neural networks to learn complex patterns from large amounts of data through advances in computing power and improved training techniques. In the healthcare sector, AI is an excellent tool for supporting the treatment of the growing population by decreasing the pressure on healthcare providers. In the medical field, diagnostic radiology is an area most in need of AI. Diagnostic imaging applications utilizing AI are promising, but they are constrained by a number of limitations. Data availability, for example, is one of the biggest challenges in programming AI algorithms. In the United Arab Emirates (UAE), AI in radiology has revolutionized medical imaging by enhancing diagnostic accuracy and streamlining workflows. It enables early disease detection and empowers radiologists to offer personalized treatment plans. However, it is worth noting that while AI adoption is on the rise, its full potential has not yet been fully realized. In this chapter, we will discuss types of machine learning algorithms, applications in healthcare focusing on diagnostic radiology, and their limitations.

Patient Perspectives on Artificial Intelligence in Radiology

Artificial intelligence (AI) is a broad, umbrella term that encompasses the theory and development of computer systems able to perform tasks normally requiring human intelligence. The aim of this study is to assess the radiology community’s attitude in Saudi Arabia toward the applications of AI. Methods: Data for this study were collected using electronic questionnaires in 2019 and 2020. The study included a total of 714 participants. Data analysis was performed using SPSS Statistics (version 25). Results: The majority of the participants (61.2%) had read or heard about the role of AI in radiology. We also found that radiologists had statistically different responses and tended to read more about AI compared to all other specialists. In addition, 82% of the participants thought that AI must be included in the curriculum of medical and allied health colleges, and 86% of the participants agreed that AI would be essential in the future. Even though human–machine interaction was considered to be one of the most important skills in the future, 89% of the participants thought that it would never replace radiologists. Conclusion: Because AI plays a vital role in radiology, it is important to ensure that radiologists and radiographers have at least a minimum understanding of the technology. Our finding shows an acceptable level of knowledge regarding AI technology and that AI applications should be included in the curriculum of the medical and health sciences colleges.

The rapid growth of medical imaging has placed increasing demands on radiologists. In this scenario, artificial intelligence (AI) has become an attractive partner, one that may complement case interpretation and may aid in various non-interpretive aspects of the work in the radiological clinic. In this review, we discuss interpretative and non-interpretative uses of AI in the clinical practice, as well as report on the barriers to AI’s adoption in the clinic. We show that AI currently has a modest to moderate penetration in the clinical practice, with many radiologists still being unconvinced of its value and the return on its investment. Moreover, we discuss the radiologists’ liabilities regarding the AI decisions, and explain how we currently do not have regulation to guide the implementation of explainable AI or of self-learning algorithms.

BackgroundThe integration of artificial intelligence (AI) in radiology has the potential to improve diagnostic accuracy and efficiency. Medical students and junior doctors will likely use AI more frequently in the future, making their perceptions essential for identifying educational gaps.PurposeTo explore the perceptions of UK medical students and junior doctors regarding AI in radiology.Material and MethodsA cross-sectional survey was distributed across UK medical schools and foundation programs. A total of 250 responses were analyzed using descriptive statistics and non-parametric tests, focusing on career impact, clinical effectiveness, educational development, and ethical concerns.ResultsMost respondents (55.2%) were undeterred by career uncertainties related to AI, with 64% confident that AI would not replace radiologists. Up to 80.6% supported AI's clinical benefits, and 63.2% endorsed its educational integration. However, there were concerns about job displacement and insufficient AI training. Medical students were more worried about job security than junior doctors, while those committed to radiology were less apprehensive and viewed AI as complementary.ConclusionEducational programs and regulatory frameworks are essential to facilitate AI integration in radiology. Addressing concerns about job displacement and improving AI education will be key to preparing future radiologists for technological advancements.

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

Artificial intelligence (AI) in radiology is recently a rapidly growing subject. Much literature about AI in radiology has been launched within 5 years, as well as commercial AI companies. This phenomenon makes some old radiologists feel worried about losing their jobs, and junior doctors hesitate to choose radiology as a specialty. Currently, implementations of proprietary AIs in clinical practice are limited, with a default setting for a convenient human overwrite. The AIs in clinical imaging largely remain either investigational as part of clinical/pre-clinical trials or being developed for commercialized purposes. Radiologists have an important role in all AI processes from the beginning to the end and vital in training the machine, as well as to validate its added benefit for outcome prediction/prognostication. This article will discuss the importance for radiologists to develop, implement, and monitor AI in clinical imaging, together with some ethical considerations. We would like to encourage radiologists to use AI as an adjunct tool, to save time and have better performance.

Artificial Intelligence in Radiology: Overview of Application Types, Design, and Challenges