Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association.

Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.

Integrating and Adopting AI in the Radiology Workflow: A Primer for Standards and Integrating the Healthcare Enterprise (IHE) Profiles.

The deployment of artificial intelligence (AI) solutions in radiology practice creates new demands on existing imaging workflow. Accommodating custom integrations creates a substantial operational and maintenance burden. These custom integrations also increase the likelihood of unanticipated problems. Standards-based interoperability facilitates AI integration with systems from different vendors into a single environment by enabling seamless exchange between information systems in the radiology workflow. Integrating the Healthcare Enterprise (IHE) is an initiative to improve how computer systems share information across health care domains, including radiology. IHE integrates existing standards-such as Digital Imaging and Communications in Medicine, Health Level Seven, and health care lexicons and ontologies (ie, LOINC, RadLex, SNOMED Clinical Terms)-by mapping data elements from one standard to another. IHE Radiology manages profiles (standards-based implementation guides) for departmental workflow and information sharing across care sites, including profiles for scaling AI processing traffic and integrating AI results. This review focuses on the need for standards-based interoperability to scale AI integration in radiology, including a brief review of recent IHE profiles that provide a framework for AI integration. This review also discusses challenges and additional considerations for AI integration, including technical, clinical, and policy perspectives.

Environmental Sustainability and AI in Radiology: A Double-Edged Sword.

According to the World Health Organization, climate change is the single biggest health threat facing humanity. The global health care system, including medical imaging, must manage the health effects of climate change while at the same time addressing the large amount of greenhouse gas (GHG) emissions generated in the delivery of care. Data centers and computational efforts are increasingly large contributors to GHG emissions in radiology. This is due to the explosive increase in big data and artificial intelligence (AI) applications that have resulted in large energy requirements for developing and deploying AI models. However, AI also has the potential to improve environmental sustainability in medical imaging. For example, use of AI can shorten MRI scan times with accelerated acquisition times, improve the scheduling efficiency of scanners, and optimize the use of decision-support tools to reduce low-value imaging. The purpose of this Radiology in Focus article is to discuss this duality at the intersection of environmental sustainability and AI in radiology. Further discussed are strategies and opportunities to decrease AI-related emissions and to leverage AI to improve sustainability in radiology, with a focus on health equity. Co-benefits of these strategies are explored, including lower cost and improved patient outcomes. Finally, knowledge gaps and areas for future research are highlighted.

From Bench to Bedside With Large Language Models: AJR Expert Panel Narrative Review.

Large language models (LLMs) hold immense potential to revolutionize radiology. However, their integration into practice requires careful consideration. Artificial intelligence (AI) chatbots and general-purpose LLMs have potential pitfalls related to privacy, transparency, and accuracy, limiting their current clinical readiness. Thus, LLM-based tools must be optimized for radiology practice to overcome these limitations. Although research and validation for radiology applications remain in their infancy, commercial products incorporating LLMs are becoming available alongside promises of transforming practice. To help radiologists navigate this landscape, this AJR Expert Panel Narrative Review provides a multidimensional perspective on LLMs, encompassing considerations from bench (development and optimization) to bedside (use in practice). At present, LLMs are not autonomous entities that can replace expert decision-making, and radiologists remain responsible for the content of their reports. Patient-facing tools, particularly medical AI chatbots, require additional guardrails to ensure safety and prevent misuse. Still, if responsibly implemented, LLMs are well-positioned to transform efficiency and quality in radiology. Radiologists must be well-informed and proactively involved in guiding the implementation of LLMs in practice to mitigate risks and maximize benefits to patient care.

Management of spontaneous coronary artery dissection: Trends over time.

BACKGROUND: Spontaneous coronary artery dissection (SCAD) is an increasingly recognized cause of acute coronary syndrome. Guidance regarding the optimal management of patients with SCAD has been published over the past 10 years, but the impact on clinical practice has not been evaluated. The present study aims to examine if approaches to invasive management, medical therapy, and vascular imaging have changed over time. METHODS: This is a retrospective cohort study of 157 patients treated for SCAD between 2005 and 2019 at an academic health system in Philadelphia, Pennsylvania. We aimed to examine change in management over time, including rates of coronary revascularization, discharge medications, and vascular imaging. RESULTS: Conservative management of SCAD increased over time from 35% before 2013 to 89% in 2019, p < 0.001. Revascularization was associated with younger age, pregnancy-associated SCAD, and lesions of the left main artery, left anterior descending artery, and multiple vessels, p < 0.05 for all. Partial imaging for extracoronary vascular abnormalities ranged from 33% before 2013 to 71% in 2018, p = 0.146. The rate of comprehensive vascular imaging (cross-sectional head to pelvis imaging) remained low in all time categories (10-18%) and did not change over time. Patients who underwent comprehensive imaging were more likely to be diagnosed with fibromuscular dysplasia (FMD) compared to those with partial imaging (63% vs 15%, p < 0.001). CONCLUSION: Management of spontaneous coronary artery dissection has changed over time. More patients are being managed conservatively and undergo screening for extracoronary vascular abnormalities such as FMD. Future efforts should focus on improving rates of comprehensive vascular screening.

Quality Improvement Report: Design and Implementation of a Radiology E-Consult Service.

Electronic consultations (e-consults) mediated through an electronic health record system or web-based platform allow synchronous or asynchronous physician-to-physician communication. E-consults have been explored in various clinical specialties, but relatively few instances in the literature describe e-consults to connect health care providers directly with radiologists.The authors outline how a radiology department can implement an e-consult service and review the development of such a service in a large academic health system. They describe the logistics, workflow, turnaround time expectations, stakeholder management, and pilot implementation and highlight challenges and lessons learned.

Improved Fine-Tuning of In-Domain Transformer Model for Inferring COVID-19 Presence in Multi-Institutional Radiology Reports.

Building a document-level classifier for COVID-19 on radiology reports could help assist providers in their daily clinical routine, as well as create large numbers of labels for computer vision models. We have developed such a classifier by fine-tuning a BERT-like model initialized from RadBERT, its continuous pre-training on radiology reports that can be used on all radiology-related tasks. RadBERT outperforms all biomedical pre-trainings on this COVID-19 task (P<0.01) and helps our fine-tuned model achieve an 88.9 macro-averaged F1-score, when evaluated on both X-ray and CT reports. To build this model, we rely on a multi-institutional dataset re-sampled and enriched with concurrent lung diseases, helping the model to resist to distribution shifts. In addition, we explore a variety of fine-tuning and hyperparameter optimization techniques that accelerate fine-tuning convergence, stabilize performance, and improve accuracy, especially when data or computational resources are limited. Finally, we provide a set of visualization tools and explainability methods to better understand the performance of the model, and support its practical use in the clinical setting. Our approach offers a ready-to-use COVID-19 classifier and can be applied similarly to other radiology report classification tasks.

Imaging Informatics Fellowship Curriculum: Building Consensus on the Most Critical Topics and the Future of the Informatics Fellowship.

The existing fellowship imaging informatics curriculum, established in 2004, has not undergone formal revision since its inception and inaccurately reflects present-day radiology infrastructure. It insufficiently equips trainees for today's informatics challenges as current practices require an understanding of advanced informatics processes and more complex system integration. We sought to address this issue by surveying imaging informatics fellowship program directors across the country to determine the components and cutline for essential topics in a standardized imaging informatics curriculum, the consensus on essential versus supplementary knowledge, and the factors individual programs may use to determine if a newly developed topic is an essential topic. We further identified typical program structural elements and sought fellowship director consensus on offering official graduate trainee certification to imaging informatics fellows. Here, we aim to provide an imaging informatics fellowship director consensus on topics considered essential while still providing a framework for informatics fellowship programs to customize their individual curricula.

Ensemble Approaches to Recognize Protected Health Information in Radiology Reports.

Natural language processing (NLP) techniques for electronic health records have shown great potential to improve the quality of medical care. The text of radiology reports frequently constitutes a large fraction of EHR data, and can provide valuable information about patients' diagnoses, medical history, and imaging findings. The lack of a major public repository for radiological reports severely limits the development, testing, and application of new NLP tools. De-identification of protected health information (PHI) presents a major challenge to building such repositories, as many automated tools for de-identification were trained or designed for clinical notes and do not perform sufficiently well to build a public database of radiology reports. We developed and evaluated six ensemble models based on three publically available de-identification tools: MIT de-id, NeuroNER, and Philter. A set of 1023 reports was set aside as the testing partition. Two individuals with medical training annotated the test set for PHI; differences were resolved by consensus. Ensemble methods included simple voting schemes (1-Vote, 2-Votes, and 3-Votes), a decision tree, a naïve Bayesian classifier, and Adaboost boosting. The 1-Vote ensemble achieved recall of 998 / 1043 (95.7%); the 3-Votes ensemble had precision of 1035 / 1043 (99.2%). F1 scores were: 93.4% for the decision tree, 71.2% for the naïve Bayesian classifier, and 87.5% for the boosting method. Basic voting algorithms and machine learning classifiers incorporating the predictions of multiple tools can outperform each tool acting alone in de-identifying radiology reports. Ensemble methods hold substantial potential to improve automated de-identification tools for radiology reports to make such reports more available for research use to improve patient care and outcomes.

Musculoskeletal Outside Interpretation (MOI-RADS): an automated quality assurance tool to prospectively track discrepancies in second-opinion interpretations in musculoskeletal imaging.

OBJECTIVE: To implement an automated quality assurance tool to prospectively track discrepancies in musculoskeletal (MSK) exams submitted for second-opinion radiology interpretation at a tertiary center. METHODS: From 2013 to 2020, a standardized template was included in re-interpretation MSK reports, and a concordance assessment compared with primary interpretation was assigned. Analysis of standardized template implementation and discordance rates was performed. Of the re-interpretations that demonstrated likely clinically relevant discordance, a sample was randomly selected and the EMR was reviewed to evaluate the impact on patient care and change in medical management. RESULTS: A total of 1052 re-interpretations were identified using the standardized template. Services with higher requests for second-opinion interpretation were oncology (n = 351, 33%) and orthopedic surgery (n = 255, 24%). Overall utilization rate of the template was 65% with marked decreased during the last year (22% rate). In comparison to the primary report, there was a 30% discordance rate (n = 309) with 18% (n = 184) classified as likely clinically relevant. From the subset of discrepancies that could be clinically relevant, there was a change in management in 63% of the cases (19/30) with the re-interpretation ultimately proving correct in 80% of the cases (24/30). CONCLUSION: Implementation of a quality assurance tool embedded in the radiology workflow of second-opinion interpretations can facilitate the analysis of patient care impact; however, stricter implementation is necessary. Oncologic studies were the most common indication for re-interpretations. Although the primary and second interpretations in the majority of cases were in agreement, subspecialty MSK radiology interpretation was shown to be more accurate than primary interpretations and impacted clinical management in cases of discrepancy.

The Role of Imaging Informatics in Disaster Preparedness During the COVID-19 Pandemic.

Disaster preparedness is a major but necessary undertaking for every health care facility. The 2019 coronavirus (SARS-CoV-2) provided an unforeseen opportunity to compare the response of two radiology departments, University Health System A (UHSA) and University Health System B (UHSAB). Preparing for this disaster was unique since though unexpected, was thought to be detected early enough to allow for sufficient preparation. Unlike many other disasters which are short-term, single events, this has been an on-going event. Changes at both health systems included workflow alterations for exposure reduction to faculty, trainees, and staff. UHSA was able to quickly divert workflow to previously deployed home workstations, while University of Utah Health Sciences Center required 2 to 3 weeks to procure and initialize enough remote workstations to significantly affect departmental operations. Other measures such as universal masking, temperature screening at facility entrances, virtual appointments, and physical barriers were used by both systems to limit patient-to-patient, patient-to-staff, staff-to-patient, and staff-staff physical interaction to help decrease exposure risk. The goal of these preparations is to allow each department to fulfill imaging needs in support of the organizational clinical mission with the flexibility to adapt to the unique and dynamic nature of this disaster.

Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance.

Automated quantitative and probabilistic medical image analysis has the potential to improve the accuracy and efficiency of the radiology workflow. We sought to determine whether AI systems for brain MRI diagnosis could be used as a clinical decision support tool to augment radiologist performance. We utilized previously developed AI systems that combine convolutional neural networks and expert-derived Bayesian networks to distinguish among 50 diagnostic entities on multimodal brain MRIs. We tested whether these systems could augment radiologist performance through an interactive clinical decision support tool known as Adaptive Radiology Interpretation and Education System (ARIES) in 194 test cases. Four radiology residents and three academic neuroradiologists viewed half of the cases unassisted and half with the results of the AI system displayed on ARIES. Diagnostic accuracy of radiologists for top diagnosis (TDx) and top three differential diagnosis (T3DDx) was compared with and without ARIES. Radiology resident performance was significantly better with ARIES for both TDx (55% vs 30%; P < .001) and T3DDx (79% vs 52%; P = 0.002), with the largest improvement for rare diseases (39% increase for T3DDx; P < 0.001). There was no significant difference between attending performance with and without ARIES for TDx (72% vs 69%; P = 0.48) or T3DDx (86% vs 89%; P = 0.39). These findings suggest that a hybrid deep learning and Bayesian inference clinical decision support system has the potential to augment diagnostic accuracy of non-specialists to approach the level of subspecialists for a large array of diseases on brain MRI.

Use of an Online Crowdsourcing Platform to Assess Patient Comprehension of Radiology Reports and Colloquialisms.

OBJECTIVE. The purpose of this study was to use an online crowdsourcing platform to assess patient comprehension of five radiology reporting templates and radiology colloquialisms. MATERIALS AND METHODS. In this cross-sectional study, participants were surveyed as patient surrogates using a crowdsourcing platform. Two tasks were completed within two 48-hour time periods. For the first crowdsourcing task, each participant was randomly assigned a set of radiology reports in a constructed reporting template and subsequently tested for comprehension. For the second crowdsourcing task, each participant was randomly assigned a radiology colloquialism and asked to indicate whether the phrase indicated a normal, abnormal, or ambivalent finding. RESULTS. A total of 203 participants enrolled for the first task and 1166 for the second within 48 hours of task publication. The payment totaled $31.96. Of 812 radiology reports read, 384 (47%) were correctly interpreted by the patient surrogates. Patient surrogates had higher rates of comprehension of reports written in the patient summary (57%, p < 0.001) and traditional unstructured in combination with patient summary (51%, p = 0.004) formats than in the traditional unstructured format (40%). Most of the patient surrogates (114/203 [56%]) expressed a preference for receiving a full radiology report via an electronic patient portal. Several radiology colloquialisms with modifiers such as "low," "underdistended," and "decompressed" had low rates of comprehension. CONCLUSION. Use of the crowdsourcing platform is an expeditious, cost-effective, and customizable tool for surveying laypeople in sentiment- or task-based research. Patient summaries can help increase patient comprehension of radiology reports. Radiology colloquialisms are likely to be misunderstood by patients.

Automated Detection of Radiology Reports that Require Follow-up Imaging Using Natural Language Processing Feature Engineering and Machine Learning Classification.

While radiologists regularly issue follow-up recommendations, our preliminary research has shown that anywhere from 35 to 50% of patients who receive follow-up recommendations for findings of possible cancer on abdominopelvic imaging do not return for follow-up. As such, they remain at risk for adverse outcomes related to missed or delayed cancer diagnosis. In this study, we develop an algorithm to automatically detect free text radiology reports that have a follow-up recommendation using natural language processing (NLP) techniques and machine learning models. The data set used in this study consists of 6000 free text reports from the author's institution. NLP techniques are used to engineer 1500 features, which include the most informative unigrams, bigrams, and trigrams in the training corpus after performing tokenization and Porter stemming. On this data set, we train naive Bayes, decision tree, and maximum entropy models. The decision tree model, with an F1 score of 0.458 and accuracy of 0.862, outperforms both the naive Bayes (F1 score of 0.381) and maximum entropy (F1 score of 0.387) models. The models were analyzed to determine predictive features, with term frequency of n-grams such as "renal neoplasm" and "evalu with enhanc" being most predictive of a follow-up recommendation. Key to maximizing performance was feature engineering that extracts predictive information and appropriate selection of machine learning algorithms based on the feature set.

Automating Import and Reconciliation of Outside Examinations Submitted to an Academic Radiology Department.

Although advances in electronic image sharing have made continuity of patient care easier, currently, the majority of outside studies are received on CD. At our institution, there were 9 full-time employees (FTE) at three locations using three workflows to manually upload, schedule, and process studies to PACS. As the demand to view and store outside studies has grown, so has the processing turnaround time. To reduce turnaround time and the need for human intervention, we developed an automated workflow to import outside studies from a CD to our PACS and reconcile them with an internal accession number and exam code.

A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop.

Imaging research laboratories are rapidly creating machine learning systems that achieve expert human performance using open-source methods and tools. These artificial intelligence systems are being developed to improve medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection, computer-aided classification, and radiogenomics. In August 2018, a meeting was held in Bethesda, Maryland, at the National Institutes of Health to discuss the current state of the art and knowledge gaps and to develop a roadmap for future research initiatives. Key research priorities include: 1, new image reconstruction methods that efficiently produce images suitable for human interpretation from source data; 2, automated image labeling and annotation methods, including information extraction from the imaging report, electronic phenotyping, and prospective structured image reporting; 3, new machine learning methods for clinical imaging data, such as tailored, pretrained model architectures, and federated machine learning methods; 4, machine learning methods that can explain the advice they provide to human users (so-called explainable artificial intelligence); and 5, validated methods for image de-identification and data sharing to facilitate wide availability of clinical imaging data sets. This research roadmap is intended to identify and prioritize these needs for academic research laboratories, funding agencies, professional societies, and industry.

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes. This article is a simultaneous joint publication in Radiology, Journal of the American College of Radiology, Canadian Association of Radiologists Journal, and Insights into Imaging. Published under a CC BY-NC-ND 4.0 license. Online supplemental material is available for this article.

Patient Factor Disparities in Imaging Follow-Up Rates After Incidental Abdominal Findings.

OBJECTIVE: The effect of demographics and societal determinants on imaging follow-up rates is not clear. The purpose of this study was to compare characteristics of patients with imaging findings representing possible cancer who undergo follow-up imaging versus those who do not to better understand factors that contribute to follow-up completion. MATERIALS AND METHODS: The records of 1588 patients with indeterminate abdominal imaging findings consecutively registered between July 1, 2013, and March 20, 2014, were reviewed. Several patient characteristics, including distance between patients' home zip codes and the flagship hospital of the health system were compared between the groups who did and did not undergo follow-up imaging. Subgroup analyses based on the location of the index examination were also performed. RESULTS: Among the 1513 (36.62%) included patients, 554 did not undergo follow-up abdominal imaging within 1 year of the index examination. The same was true of 270 of 938 (28.78%) outpatients and 168 of 279 (60.21%) emergency department patients. Eighty-nine of 959 (9.28%) patients who underwent follow-up imaging were younger than 40 years, compared with 76 of 554 (13.72%) patients who did not undergo follow-up imaging (p = 0.005). Fifty-four of 959 (5.63%) patients who underwent follow-up imaging were older than 80 years, compared with 70 of 554 (12.64%) patients who did not undergo follow-up imaging (p < 0.001). More white patients (587 of 959 vs 301 of 554, p = 0.007) and fewer black patients (204 of 554 versus 270 of 959, p < 0.001) were found in the follow-up imaging group. Greater distance from the flagship hospital correlated with less follow-up in the outpatient subgroup only (p = 0.03). CONCLUSION: Emergency department patients and patients at the extremes of age are less likely to complete follow-up imaging. Insurance status and race and ethnicity may affect follow-up completion rates. The relationship between distance to hospital and follow-up completion requires further investigation.

Toward Complete Structured Information Extraction from Radiology Reports Using Machine Learning.

Unstructured and semi-structured radiology reports represent an underutilized trove of information for machine learning (ML)-based clinical informatics applications, including abnormality tracking systems, research cohort identification, point-of-care summarization, semi-automated report writing, and as a source of weak data labels for training image processing systems. Clinical ML systems must be interpretable to ensure user trust. To create interpretable models applicable to all of these tasks, we can build general-purpose systems which extract all relevant human-level assertions or "facts" documented in reports; identifying these facts is an information extraction (IE) task. Previous IE work in radiology has focused on a limited set of information, and extracts isolated entities (i.e., single words such as "lesion" or "cyst") rather than complete facts, which require the linking of multiple entities and modifiers. Here, we develop a prototype system to extract all useful information in abdominopelvic radiology reports (findings, recommendations, clinical history, procedures, imaging indications and limitations, etc.), in the form of complete, contextualized facts. We construct an information schema to capture the bulk of information in reports, develop real-time ML models to extract this information, and demonstrate the feasibility and performance of the system.