Artificial Intelligence In Radiology Research Articles

Forging Connections in Latin America to Advance AI in Radiology.

The 1° Encontro Latino-Americano de IA em Saúde (1st Latin American Meeting on AI in Health) was held during the 2022 Jornada Paulista de Radiologia, the annual radiology meeting in the state of São Paulo. The event was created to foster discussion among Latin American countries about the complexity, challenges, and opportunities in developing and using artificial intelligence (AI) in those countries. Technological improvements in AI have created high expectations in health care. AI is recognized increasingly as a game changer in clinical radiology. To counter the fear that AI would "take over" radiology, the program included activities to educate radiologists. The development of AI in Latin America is in its early days, and although there are some pioneer cases, many regions still lack world-class technological infrastructure and resources. Legislation, regulation, and public policies in data privacy and protection, digital health, and AI are recent advances in many countries. The meeting program was developed with a broad scope, with expertise from different countries, backgrounds, and specialties, with the objective of encompassing all levels of complexity (from basic concepts to advanced techniques), perspectives (clinical, technical, ethical, and business), and specialties (both informatics and data science experts and the usual radiology clinical groups). It was an opportunity to connect with peers from other countries and share lessons learned about AI in health care in different countries and contexts. Keywords: Informatics, Use of AI in Education, Impact of AI on Education, Social Implications © RSNA, 2022.

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.

Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?

As the role of artificial intelligence (AI) in clinical practice evolves, governance structures oversee the implementation, maintenance, and monitoring of clinical AI algorithms to enhance quality, manage resources, and ensure patient safety. In this article, a framework is established for the infrastructure required for clinical AI implementation and presents a road map for governance. The road map answers four key questions: Who decides which tools to implement? What factors should be considered when assessing an application for implementation? How should applications be implemented in clinical practice? Finally, how should tools be monitored and maintained after clinical implementation? Among the many challenges for the implementation of AI in clinical practice, devising flexible governance structures that can quickly adapt to a changing environment will be essential to ensure quality patient care and practice improvement objectives.

On the Opportunities and Risks of Foundation Models for Natural Language Processing in Radiology.

HomeRadiology: Artificial IntelligenceVol. 4, No. 4 PreviousNext CommentaryOn the Opportunities and Risks of Foundation Models for Natural Language Processing in RadiologyWalter F. Wiggins , Ali S. TejaniWalter F. Wiggins , Ali S. TejaniAuthor AffiliationsFrom the Department of Radiology, Duke University Health System, 2301 Erwin Rd, Durham, NC 27710 (W.F.W.); Duke Center for Artificial Intelligence in Radiology, Duke University School of Medicine, Durham, NC (W.F.W.); and Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (A.S.T.).Address correspondence to W.F.W. (email: [email protected]).Walter F. Wiggins Ali S. TejaniPublished Online:Jul 20 2022https://doi.org/10.1148/ryai.220119MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In

An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology.

Imaging in the emergent setting carries high stakes. With increased demand for dedicated on-site service, emergency radiologists face increasingly large image volumes that require rapid turnaround times. However, novel artificial intelligence (AI) algorithms may assist trauma and emergency radiologists with efficient and accurate medical image analysis, providing an opportunity to augment human decision making, including outcome prediction and treatment planning. While traditional radiology practice involves visual assessment of medical images for detection and characterization of pathologies, AI algorithms can automatically identify subtle disease states and provide quantitative characterization of disease severity based on morphologic image details, such as geometry and fluid flow. Taken together, the benefits provided by implementing AI in radiology have the potential to improve workflow efficiency, engender faster turnaround results for complex cases, and reduce heavy workloads. Although analysis of AI applications within abdominopelvic imaging has primarily focused on oncologic detection, localization, and treatment response, several promising algorithms have been developed for use in the emergency setting. This article aims to establish a general understanding of the AI algorithms used in emergent image-based tasks and to discuss the challenges associated with the implementation of AI into the clinical workflow.

The Radiology: Artificial Intelligence Trainee Editorial Board: Initial Experience and Future Directions

In 2019, the journal Radiology: Artificial Intelligence introduced its Trainee Editorial Board (TEB) to offer formal training in medical journalism to medical students, radiology residents and fellows, and research-career trainees. The TEB aims to build a community of radiologists, radiation oncologists, medical physicists, and researchers in fields related to artificial intelligence (AI) in radiology. The program presented opportunities to learn about the editorial process, improve skills in writing and reviewing, advance the field of AI in radiology, and help translate and disseminate AI research. To meet these goals, TEB members contribute actively to the editorial process from peer review to publication, participate in educational webinars, and create and curate content in a variety of forms. Almost all of the contact has been mediated through the web. In this article, we share initial experiences and identify future directions and opportunities.

Artificial Intelligence in Radiology: an introduction to the most important concepts

The interpretation of medical imaging tests is one of the main tasks that radiologists do. For years, it has been a challenge to teach computers to do this kind of cognitive task; the main objective of the field of computer vision is to overcome this challenge. Thanks to technological advances, we are now closer than ever to achieving this goal, and radiologists need to become involved in this effort to guarantee that the patient remains at the center of medical practice.This article clearly explains the most important theoretical concepts in this area and the main problems or challenges at the present time; moreover, it provides practical information about the development of an artificial intelligence project in a radiology department.

Is Artificial Intelligence (AI) a Pipe Dream? Why Legal Issues Present Significant Hurdles to AI Autonomy.

Proponents of artificial intelligence (AI) technology have suggested that in the near future, AI software may replace human radiologists. Although assimilation of AI into the specialty has occurred more slowly than predicted, developments in machine learning, deep learning, and neural networks suggest that technologic hurdles and costs will eventually be overcome. However, beyond these technologic hurdles, formidable legal hurdles threaten the impact of AI on the specialty. Legal liability for errors committed by AI will influence the ultimate role of AI within radiology and also influence whether AI remains a simple decision support tool or develops into an autonomous member of the health care team. Additional areas of uncertainty include the potential application of products liability law to AI and the approach taken by the U.S. FDA in potentially classifying autonomous AI as a medical device. The current ambiguity of the legal treatment of AI will profoundly influence development of autonomous AI given that vendors, radiologists, and hospitals will be unable to reliably assess their liability associated with implementing such tools. Advocates of AI in radiology and health care in general need to lobby for legislative action to better clarify the liability risks of AI in a way that does not deter technologic development.

Can the computed tomography texture analysis of colorectal liver metastases predict the response to first-line cytotoxic chemotherapy?

BACKGROUNDArtificial intelligence in radiology has the potential to assist with the diagnosis, prognostication and therapeutic response prediction of various cancers. A few studies have reported that texture analysis can be helpful in predicting the response to chemotherapy for colorectal liver metastases, however, the results have varied. Necrotic metastases were not clearly excluded in these studies and in most studies the full range of texture analysis features were not evaluated. This study was designed to determine if the computed tomography (CT) texture analysis results of non-necrotic colorectal liver metastases differ from previous reports. A larger range of texture features were also evaluated to identify potential new biomarkers.AIMTo identify potential new imaging biomarkers with CT texture analysis which can predict the response to first-line cytotoxic chemotherapy in non-necrotic colorectal liver metastases (CRLMs).METHODSPatients who presented with CRLMs from 2012 to 2020 were retrospectively selected on the institutional radiology information system of our private radiology practice. The inclusion criteria were non-necrotic CRLMs with a minimum size of 10 mm (diagnosed on archived 1.25 mm portal venous phase CT scans) which were treated with standard first-line cytotoxic chemotherapy (FOLFOX, FOLFIRI, FOLFOXIRI, CAPE-OX, CAPE-IRI or capecitabine). The final study cohort consisted of 29 patients. The treatment response of the CRLMs was classified according to the RECIST 1.1 criteria. By means of CT texture analysis, various first and second order texture features were extracted from a single non-necrotic target CRLM in each responding and non-responding patient. Associations between features and response to chemotherapy were assessed by logistic regression models. The prognostic accuracy of selected features was evaluated by using the area under the curve.RESULTSThere were 15 responders (partial response) and 14 non-responders (7 stable and 7 with progressive disease). The responders presented with a higher number of CRLMs (P = 0.05). In univariable analysis, eight texture features of the responding CRLMs were associated with treatment response, but due to strong correlations among some of the features, only two features, namely minimum histogram gradient intensity and long run low grey level emphasis, were included in the multiple analysis. The area under the receiver operating characteristic curve of the multiple model was 0.80 (95%CI: 0.64 to 0.96), with a sensitivity of 0.73 (95%CI: 0.48 to 0.89) and a specificity of 0.79 (95%CI: 0.52 to 0.92). CONCLUSIONEight first and second order texture features, but particularly minimum histogram gradient intensity and long run low grey level emphasis are significantly correlated with treatment response in non-necrotic CRLMs.

The concept of the invisible radiologist in the era of artificial intelligence

The radiologists were traditionally working in the background. What upgraded them as physicians during the second half of the past century was their clinical training and function precipitated by the evolution of Interventional Radiology and Medical Imaging, especially with ultrasonography. These allowed them to participate in patient’s diagnosis and treatment by direct contact as well asvia multidisciplinary medical consultations. The wide application of teleradiology and PACS pushed radiologists back again which is no longer acceptable, especially in view of the amazing applications of artificial intelligence (AI) in Radiology. It is our belief that clinical radiologists have to be able to control the penetration of AI in Radiology, securing their work for the benefit of both clinicians and patients.

A Basic Primer of Artificial Intelligence for Radiologists

Artificial intelligence (AI) comprises computer systems that behave in ways previously thought to require human intelligence.1 AI and related technologies are increasingly prevalent in business and society and are beginning to be applied to health care.2 Within health care, AI has increasingly influenced the field of radiology, and its role is likely only to grow in the future. Within radiology, AI has demonstrated benefits in the areas of image analysis and interpretation, various noninterpretive domains, and resident training. And yet, AI remains vaguely and incompletely understood by a great many practicing radiologists, radiology residents, and students considering a career in radiology. The purpose of this article is to describe the primary current and potential future applications of AI to the field of radiology and to define some of the key terms used in discussions of AI. This article is meant to provide readers with a clear, foundational understanding of AI in radiology and to equip radiologists with literacy and fluency in the AI lexicon.

Artificial intelligence in radiology: Are Saudi residents ready, prepared, and knowledgeable?

Objectives:To assess the knowledge and perception of artificial intelligence (AI) among radiology residents across Saudi Arabia and assess their interest in learning about AI.Methods:An observational cross-sectional study carried out among radiology residents enrolled in the Saudi Board of Radiology, Saudi Arabia. An anonymized, self-administered questionnaire was distributed in April 2020 and responses were collected until July 2020.Results:A total of 154 residents filled the questionnaire. The top 3 aspects of AI participants wanted to learn were: clinical use of AI applications, advantages and limitations of AI applications, and technical methods. Approximately 43.5% of participants did not expect AI to affect job positions, while 42% anticipated that job positions will decrease. Approximately 53% expected a reduction in reporting workload, while 28% expected an increase in workload.Conclusion:Currently, the exposure of radiologists to the use of AI is inadequate. It is imperative that AI is introduced to radiology trainees and that radiologists stay updated with advances in AI to be more knowledgeable on how to benefit from it.

For Watson, Solving Cancer Wasn't So Elementary: Prospects for Artificial Intelligence in Radiology

2021 heralds major 5- and 10-year anniversaries for artificial intelligence that should not pass without acknowledgement. In 2011, IBM's question-answering computer Watson roundly defeated television gameshow Jeopardy! champions Ken Jennings and Brad Rutter with a three-day total of $77,000 – as compared to Jennings’ $24,000 and Rutter's $22,000 – winning a first-place prize of $1 million ( 1 Maerz M IBM's Watson computer beats “Jeopardy!” champions. Los Angeles Times. 2011; (Accessed at: November 20, 2021Accessed from:)https://www.latimes.com/archives/la-xpm-2011-feb-17-la-na-jeopardy-20110217-story.html Google Scholar ). In 2016, the Canadian psychologist and computer scientist Geoffrey Hinton, FRS, who has worked for both Google and the University of Toronto, announced that it was time to stop training radiologists, because they would soon be replaced by image perception algorithms – in fact, Hinton compared radiologists to Warner's Wyle E. Coyote, the cartoon character who has already passed the edge of a cliff but has not yet looked down ( 2 Botz B A coyote that ran over the edge? — differing opinions about the impact of AI on radiology. Medium. 2016; (PublishedAccessed at: November 20, 2021Accessed from:)https://chatbotslife.com/a-coyote-that-ran-over-the-edge-differing-opinions-about-the-impact-of-ai-on-radiology-4b5e3860722e Google Scholar ).

Evaluation and Real-World Performance Monitoring of Artificial Intelligence Models in Clinical Practice: Try It, Buy It, Check It.

The pace of regulatory clearance of artificial intelligence (AI) algorithms for radiology continues to accelerate, and numerous algorithms are becoming available for use in clinical practice. End users of AI in radiology should be aware that AI algorithms may not work as expected when used beyond the institutions in which they were trained, and model performance may degrade over time. In this article, we discuss why regulatory clearance alone may not be enough to ensure AI will be safe and effective in all radiological practices and review strategies available resources for evaluating before clinical use and monitoring performance of AI models to ensure efficacy and patient safety.

Toward Generalizability in the Deployment of Artificial Intelligence in Radiology: Role of Computation Stress Testing to Overcome Underspecification.

The clinical deployment of artificial intelligence (AI) applications in medical imaging is perhaps the greatest challenge facing radiology in the next decade. One of the main obstacles to the incorporation of automated AI-based decision-making tools in medicine is the failure of models to generalize when deployed across institutions with heterogeneous populations and imaging protocols. The most well-understood pitfall in developing these AI models is overfitting, which has, in part, been overcome by optimizing training protocols. However, overfitting is not the only obstacle to the success and generalizability of AI. Underspecification is also a serious impediment that requires conceptual understanding and correction. It is well known that a single AI pipeline, with prescribed training and testing sets, can produce several models with various levels of generalizability. Underspecification defines the inability of the pipeline to identify whether these models have embedded the structure of the underlying system by using a test set independent of, but distributed identically, to the training set. An underspecified pipeline is unable to assess the degree to which the models will be generalizable. Stress testing is a known tool in AI that can limit underspecification and, importantly, assure broad generalizability of AI models. However, the application of stress tests is new in radiologic applications. This report describes the concept of underspecification from a radiologist perspective, discusses stress testing as a specific strategy to overcome underspecification, and explains how stress tests could be designed in radiology-by modifying medical images or stratifying testing datasets. In the upcoming years, stress tests should become in radiology the standard that crash tests have become in the automotive industry. Keywords: Computer Applications-General, Informatics, Computer-aided Diagnosis © RSNA, 2021.

Predicting cancer outcomes with radiomics and artificial intelligence in radiology.

The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challenges in clinical decision-making that AI tools can solve using radiology images, such as prognostication of outcome across multiple cancers, prediction of response to various treatment modalities, discrimination of benign treatment confounders from true progression, identification of unusual response patterns and prediction of the mutational and molecular profile of tumours. We describe the evolution of and opportunities for AI in oncology imaging, focusing on hand-crafted radiomic approaches and deep learning-derived representations, with examples of their application for decision support. We also address the challenges faced on the path to clinical adoption, including data curation and annotation, interpretability, and regulatory and reimbursement issues. We hope to demystify AI in radiology for clinicians by helping them to understand its limitations and challenges, as well as the opportunities it provides as a decision-support tool in cancer management.

Do People Favor Artificial Intelligence Over Physicians? A Survey Among the General Population and Their View on Artificial Intelligence in Medicine

ObjectivesTo investigate the general population’s view on artificial intelligence (AI) in medicine with specific emphasis on 3 areas that have experienced major progress in AI research in the past few years, namely radiology, robotic surgery, and dermatology. MethodsFor this prospective study, the April 2020 Online Longitudinal Internet Studies for the Social Sciences Panel Wave was used. Of the 3117 Longitudinal Internet Studies For The Social Sciences panel members contacted, 2411 completed the full questionnaire (77.4% response rate), after combining data from earlier waves, the final sample size was 1909. A total of 3 scales focusing on trust in the implementation of AI in radiology, robotic surgery, and dermatology were used. Repeated-measures analysis of variance and multivariate analysis of variance was used for comparison. ResultsThe overall means show that respondents have slightly more trust in AI in dermatology than in radiology and surgery. The means show that higher educated males, employed or student, of Western background, and those not admitted to a hospital in the past 12 months have more trust in AI. The trust in AI in radiology, robotic surgery, and dermatology is positively associated with belief in the efficiency of AI and these specific domains were negatively associated with distrust and accountability in AI in general. ConclusionsThe general population is more distrustful of AI in medicine unlike the overall optimistic views posed in the media. The level of trust is dependent on what medical area is subject to scrutiny. Certain demographic characteristics and individuals with a generally positive view on AI and its efficiency are significantly associated with higher levels of trust in AI.

Stakeholders' perspectives on the future of artificial intelligence in radiology: a scoping review.

Artificial intelligence (AI) has the potential to impact clinical practice and healthcare delivery. AI is of particular significance in radiology due to its use in automatic analysis of image characteristics. This scoping review examines stakeholder perspectives on AI use in radiology, the benefits, risks, and challenges to its integration. A search was conducted from 1960 to November 2019 in EMBASE, PubMed/MEDLINE, Web of Science, Cochrane Library, CINAHL, and grey literature. Publications reflecting stakeholder attitudes toward AI were included with no restrictions. Commentaries (n = 32), surveys (n = 13), presentation abstracts (n = 8), narrative reviews (n = 8), and a social media study (n = 1) were included from 62 eligible publications. These represent the views of radiologists, surgeons, medical students, patients, computer scientists, and the general public. Seven themes were identified (predicted impact, potential replacement, trust in AI, knowledge of AI, education, economic considerations, and medicolegal implications). Stakeholders anticipate a significant impact on radiology, though replacement of radiologists is unlikely in the near future. Knowledge of AI is limited for non-computer scientists and further education is desired. Many expressed the need for collaboration between radiologists and AI specialists to successfully improve patient care. Stakeholder views generally suggest that AI can improve the practice of radiology and consider the replacement of radiologists unlikely. Most stakeholders identified the need for education and training on AI, as well as collaborative efforts to improve AI implementation. Further research is needed to gain perspectives from non-Western countries, non-radiologist stakeholders, on economic considerations, and medicolegal implications. Stakeholders generally expressed that AI alone cannot be used to replace radiologists. The scope of practice is expected to shift with AI use affecting areas from image interpretation to patient care. Patients and the general public do not know how to address potential errors made by AI systems while radiologists believe that they should be "in-the-loop" in terms of responsibility. Ethical accountability strategies must be developed across governance levels. Students, residents, and radiologists believe that there is a lack in AI education during medical school and residency. The radiology community should work with IT specialists to ensure that AI technology benefits their work and centres patients.

Artificial Intelligence in Radiology in Pakistan; Pros and Cons

Artificial Intelligence in Radiology in Pakistan; Pros and Cons

Ethical Concerns About Reidentification of Individuals from MRI Neuroimages

Ethical Concerns About Reidentification of Individuals from MRI Neuroimages