Jul 2026· Seminars in roentgenology· Vol 64, pp.
151009
· 0 citations· 90 references
Medicine
TL;DR
This narrative review examines the role of AI across the emergency radiology workflow through three lenses: current capabilities, limitations of the supporting evidence, and practical considerations for clinical implementation, integrating published evidence with practical insights.
Abstract
Emergency radiology operates in a high-acuity, time-sensitive environment where imaging is tightly integrated into real-time clinical decision-making. Growing imaging demand, increasing case complexity, and workforce constraints have intensified pressure on emergency radiologists. Artificial intelligence (AI) has emerged as a potential tool to support imaging prioritization, interpretation, and operational efficiency. However, to meaningfully advance care delivery, the role of AI must be considered beyond algorithm performance, including its implementation, reliability, and real-world clinical impact. In this narrative review, we examine the role of AI across the emergency radiology workflow through three lenses: current capabilities, limitations of the supporting evidence, and practical considerations for clinical implementation. We review applications spanning pre-image acquisition, image acquisition and reconstruction, computer-aided triage and detection, reporting, and follow-up, integrating published evidence with practical insights. Discrepancies between reported and real-world performance, the influence of human-AI interaction on clinical decision-making, and the potential for subtle errors and bias are also discussed. As national regulatory and local governance frameworks continue to evolve, including emerging challenges posed by large language models, gaps remain between reported and real-world AI performance. In emergency radiology, the true impact of AI will depend on how seamlessly and effectively these tools are integrated into existing clinical workflows. Local validation, ongoing performance monitoring, and multidisciplinary institutional oversight are essential to identify performance variability, mitigate biases, and support reliable use in a high-stakes clinical environment.
Background: Artificial intelligence (AI) is increasingly being incorporated into radiology, not only for image interpretation but also for scheduling, examination protocoling, image acquisition, reconstruction, worklist prioritisation, quantitative analysis, reporting, communication, and follow-up. The clinical value of these systems depends on more than algorithmic accuracy. It also depends on interoperability, usability, external validation, human oversight, institutional readiness, and the ability to demonstrate measurable improvement in patient care. Objective: This review examines how AI is reshaping radiology workflow, summarises clinically relevant applications across imaging modalities, evaluates evidence regarding diagnostic performance and operational efficiency, and discusses implementation, ethics, regulation, workforce, economic, and equity-related considerations. Methods: A structured narrative review framework was developed using PubMed/MEDLINE, Embase, Scopus, Web of Science, Cochrane Library, PubMed Central, major radiology journals, and publicly available regulatory and professional sources. Original clinical research articles published between January 2016 and August 2026 were prioritised. Studies were considered when they evaluated an AI application in clinical imaging, measured diagnostic or workflow outcomes, or described prospective implementation. Because the studies differed substantially in task, population, modality, endpoint, and reference standard, findings were synthesised narratively rather than pooled statistically. Results: AI has demonstrated value in selected tasks involving mammographic screening, chest-radiograph interpretation, CT triage, MRI reconstruction, segmentation, quantitative imaging, and worklist prioritisation. Prospective and randomised studies suggest that AI can preserve or improve diagnostic performance while reducing selected forms of reader workload. Nevertheless, reported workflow gains are variable. Benefits may be attenuated by false-positive alerts, additional review requirements, poor system integration, case-mix differences, and local staffing patterns. Evidence connecting AI deployment with improved patient outcomes, cost-effectiveness, and long-term equity remains less mature. Conclusions: AI should be treated as a sociotechnical intervention rather than as a stand-alone software product. The most defensible implementation strategy is to begin with narrowly defined clinical problems, conduct local validation, integrate outputs into existing systems, train users, monitor performance after deployment, and retain accountable human oversight. Sustainable transformation will require prospective multicentre research, transparent reporting, interoperable architectures, lifecycle regulation, and deliberate protection against bias and unequal access.
Neelam Rao Bharti, Deeksha Jaiswal, Nidhi Goswami et al.· Genetics and Molecular Resea...· 0 citations
AI should be viewed not as a replacement for ultrasound professionals but as a decision-support technology that may improve consistency, efficiency, and diagnostic confidence when carefully validated in real-world clinical settings.
Bayan Alghamdi, Eman M Alrewily, Sharefa S Alghamdi· Ultrasound Quarterly· 0 citations
Artificial intelligence (AI) has become one of the most actively discussed tools in modern day radiology, promising to help interpret X-rays, CT scans, and MRIs alongside human radiologists. It has matched or exceeded human accuracy outright, particularly in a few narrow tasks. Furthermore, AI has come at a time when imaging volume has grown far faster than the radiology workforce, leading to heavy workloads for radiologists. However, this progress is not without its issues, including AI models that can be deliberately fooled and an inability to explain how a given prediction was reached in the first place. What remains unresolved is whether this radiology-based AI can be reliably explained, trusted, and applied in a workplace setting when limitations such as the risk of human over-reliance and the availability of training data are considered. To address this, this paper examines peer-reviewed studies covering AI's technical validation, real-world clinical deployment, and behavioral evaluation of how radiologists work alongside this tool. The findings show that 1) a convolutional neural network can exceed average radiologist accuracy on a narrow pneumonia- detection task, 2) a structured radiologist-feedback system can significantly reduce a deployed model's false-positive rate, and 3) a study involving radiologists found AI's accuracy, not a radiologist's intelligence or experience, to be the strongest predictor of whether AI will help or hinder the user. Finally, this paper applies the knowledge from the results to a real-world AI versus radiologist comparison of a chest X-ray case, which results in a single page web interface development (https://yashcoder99.github.io/xray) via JavaScript. Together, this study demonstrates the complexity of AI's value in radiology. As AI usage and deployment rates accelerate and new peer-reviewed evidence becomes available every year, we offer an up-to-date, evidence-backed analysis and application for weighing AI's strengths against its somewhat hidden weaknesses.
The findings highlight the need for evidence from large, multicenter, prospective trials and evaluation frameworks that reflect the consequences of clinical decision-making, as well as further exploration of safeguards to monitor and address mismatches between training data and incoming scans during deployment.
Lisa Koopmans, Fernando Vega Lara, Christian Roest et al.· Abdominal Radiology· 0 citations
Abstract The integration of artificial intelligence (AI) into radiology practice has the potential to transform the health care sector in the setting of ongoing radiologist shortages. AI extends powerful tools to radiologists that enhance both diagnostic precision and reporting efficiency. A key requirement is that an AI model must display reliable performance across a diversity of radiologic data differing broadly across institutions, with varying imaging equipment and patient demographics. Thus, clinical validation by the radiologist is critical. Another important consideration is the smooth and seamless integration of AI into the radiology workflow without impeding clinical flow. It is also important, when deploying AI, to pay attention to its regulatory and ethical aspects. AI algorithms utilized for image analysis fall within the regulatory umbrella of medical devices and need to address corresponding safety and quality standards. Data privacy protections are paramount to ensure patient confidentiality. Decisions for the deployment of AI should be governed by ethical principles. Overall, for all entities, a governance framework is necessary, providing protocols underlining roles and responsibilities in AI deployment and utilization. Governance committees and regular performance audits are crucial. Initial training followed by periodic follow-up training of radiologists and administrators on functions and pitfalls of AI algorithms should be the norm. By emphasizing regulatory compliance, combined with initial and periodic clinical validation, radiologists and health care institutions as well as teleradiology centers can efficiently utilize AI to enhance diagnostic quality and proficiency, while maintaining the critical element of trust with clinical departments and patients.
Arjun Kalyanpur, Neetika Mathur· Indian Journal of Radiology...· 0 citations
Abstract Artificial intelligence is now embedded in radiology workflows across detection, triage, quantification, and reporting. Yet, most clinicians deploy these tools without a working understanding of how their outputs are generated or where they reliably fail. Unlike conventional rule-based clinical workflows, modern imaging AI systems generate probabilistic outputs whose reliability depends on training data, task definition, and deployment context. Radiologists must understand what these systems actually produce, know their predictable failure points, and never allow a confidence score to replace the clinical reasoning that only a trained human can apply. This review provides a clinician-oriented framework for understanding imaging AI by covering core model architectures, task-based applications, workflow integration, and the practical interpretation of algorithmic outputs. This is important because as imaging AI scales across institutions and populations, the radiologist's capacity to interrogate, contextualize, and, where necessary, override algorithmic outputs becomes not just a clinical skill but a professional responsibility.
Sharad Maheshwari, Sachin Kumar· Indian Journal of Radiology...· 0 citations
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