Jul 2026· Indian Journal of Radiology and Imaging· 0 citations· 60 references
TL;DR
The challenges in going from AI model performance to better outcomes are discussed, including the impact of false positives on workflow, ethical and regulatory issues, and implementation challenges.
Abstract
Abstract This is a narrative review of recent peer-reviewed literature on artificial intelligence (AI) in emergency imaging spanning patient scheduling, image optimization, detection of critical findings, communication, and regulatory oversight. We review the current AI applications in emergency radiology and summarize clinically meaningful performance metrics. There are multiple instances of high performance in identifying imaging findings and prioritizing worklists, but limited prospective data demonstrating patient outcome improvements. We discuss the challenges in going from AI model performance to better outcomes, including the impact of false positives on workflow, ethical and regulatory issues, and implementation challenges. Practical tips for the evaluation, adoption, and monitoring of AI tools are outlined. Regulatory frameworks are in evolution and lag this rapidly changing landscape. Radiologist oversight remains critical at all steps to ensure safe and effective use of AI tools.
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.
Zachary D. Miller, William E. King, N. Cross et al.· Seminars in roentgenology· 0 citations
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
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 Artificial intelligence (AI) in pediatric imaging is evolving rapidly but lags behind and remains fundamentally distinct from adult radiology AI, due to various factors, such as developmental variability, differing disease epidemiology, unique imaging constraints, and limited data ecosystems. This narrative review examines the biological, technical, and structural factors that shape pediatric imaging AI, summarizes current and emerging applications, and highlights key barriers to safe clinical translation. Evidence indicates that the most immediate and impactful applications of pediatric AI lie in image acquisition, dose reduction, and workflow optimization, while interpretive and prognostic tools remain variably mature, with bone age assessment and chest radiograph interpretation representing the most established use cases. However, broader deployment is constrained by small, fragmented datasets, lack of standardized reference labels, limited generalizability, insufficient prospective validation, and gaps in regulatory and clinical integration frameworks. Furthermore, adult-trained models frequently underperform in children, reinforcing the need for pediatric-specific design and evaluation. The review emphasizes the importance of ethical governance, stakeholder trust, and multidisciplinary collaboration in guiding implementation. Future progress will depend on shared data infrastructures, federated learning approaches, rigorous age-stratified validation, and outcome-focused evaluation aligned with pediatric care priorities. Overall, pediatric imaging AI should be regarded as a distinct clinical, technical, and ethical domain requiring evidence-driven, child-centered development to ensure safe, effective, and equitable adoption in practice.
Amit Gupta, Anjali Agrawal, Manisha Jana· Indian Journal of Radiology...· 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.
Artificial intelligence (AI) applications have transformed radiology, yet pediatric medical imaging remains substantially underrepresented in AI development, validation, regulation, and implementation. Unlike adults, children go through continuous physiologic and anatomic changes that require age-specific models trained on representative developmental data. However, pediatric AI is limited by scarce publicly available datasets, fragmented institutional data, rare diseases, heterogeneous reporting practices, and insufficient external validation. Ethical and regulatory challenges are also a concern in children, including consent for secondary data use, off-label use of adult-trained AI models, and the need for postdeployment surveillance. Additionally, reimbursement is misaligned and must be optimized to allow innovation. This AJR Expert Panel Narrative Review examines the current landscape of pediatric AI in radiology and proposes practical priorities to support its safe and equitable adoption. The panel gives key recommendations, emphasizing the importance of an implementation roadmap to establish a dedicated pediatric AI infrastructure and standards that are essential to ensure diagnostic accuracy, workflow efficiency, and optimal clinical outcomes for children while minimizing bias and protecting patient safety.
Mario Sinti-Ycochea, Marla Sammer, Susan Sotardi et al.· AJR. American journal of roe...· 0 citations
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