AI Detection of Imaging Acquisition Errors: A Comprehensive Review for Clinicians

Abstract

Imaging acquisition errors represent a critical challenge in radiology, impacting diagnostic accuracy, workflow efficiency, and patient safety. Recent advances in artificial intelligence (AI) have introduced sophisticated tools capable of detecting and correcting these errors in real-time. This review synthesizes current evidence regarding the application of AI for identifying imaging acquisition errors, discusses the mechanisms underlying these technologies, and evaluates their clinical relevance, risk factors, and integration into practice. Emphasis is placed on recent advances, guideline recommendations, and the practical implications for healthcare professionals in diverse clinical settings.

Introduction

Medical imaging is foundational to modern diagnostic pathways, yet the integrity of imaging data is frequently compromised by acquisition errors such as motion artifacts, incorrect positioning, and suboptimal parameter selection. These errors can undermine diagnostic confidence and result in repeat scans, delayed diagnoses, increased healthcare costs, and unnecessary radiation exposure. AI-based detection systems promise a paradigm shift by enabling real-time error recognition and mitigation, potentially transforming clinical workflows and patient care. This article provides a comprehensive review of the scientific landscape, emphasizing the translation of AI technologies from research to clinical application.

Epidemiology / Disease Burden

Imaging acquisition errors are pervasive across radiology modalities, with studies indicating error rates ranging from 3% to 15% depending on modality and clinical environment. In high-volume settings such as emergency departments and outpatient imaging centers, the prevalence of suboptimal scans is even higher, contributing significantly to repeat imaging rates and increased patient throughput pressures. Commonly affected modalities include MRI (notably subject to motion artifacts) and CT (with frequent issues related to contrast timing and patient positioning). The burden is compounded by the rising volume of imaging studies worldwide, highlighting the need for scalable solutions such as AI-driven error detection.

Pathophysiology

The pathophysiological basis of imaging acquisition errors is multifactorial. Patient-related factors (such as involuntary movement, inability to hold breath, or poor compliance), operator-dependent variables (including incorrect protocol selection or equipment misuse), and technical issues (malfunctioning hardware, software glitches) all contribute. AI systems leverage deep learning algorithms to analyze large datasets, identifying patterns indicative of errors by learning from annotated examples. These models can differentiate between normal and aberrant acquisition patterns, allowing for automated flagging of suspect images before clinical interpretation.

Risk Factors

Several risk factors predispose imaging studies to acquisition errors. These include patient age (pediatric and geriatric populations), cognitive impairment, acute clinical status, and complex comorbidities. Technologist experience, time pressures, and high patient volumes further increase risk. Certain anatomical regions (e.g., abdomen, thorax) and advanced imaging protocols (dynamic contrast-enhanced studies, functional MRI) are particularly susceptible. AI systems are being designed to consider these risk factors, tailoring detection algorithms to specific patient populations and imaging contexts for improved sensitivity and specificity.

Clinical Features

Clinically, imaging acquisition errors manifest as artifacts, incomplete anatomical coverage, misregistration, and suboptimal contrast enhancement. These may result in nondiagnostic images, missed or incorrect diagnoses, and subsequent clinical mismanagement. Early recognition of these features is crucial to prevent downstream consequences. AI tools can provide immediate feedback at the point of image acquisition, alerting technologists to potential errors and enabling corrective action without patient repositioning or scan repetition.

Diagnosis

Traditional diagnosis of imaging acquisition errors relies on manual review by technologists and radiologists, a process susceptible to human oversight, especially in high-volume environments. AI-based solutions utilize convolutional neural networks (CNNs) and other deep learning architectures trained on annotated imaging datasets to automatically detect errors. Recent studies report that AI algorithms can achieve sensitivities and specificities exceeding 90% for certain error types, with performance often rivaling or surpassing human experts. Integration with PACS and scanner consoles facilitates real-time error detection, reducing the reliance on retrospective quality assurance.

Treatment & Management

The management of imaging acquisition errors traditionally involves rescanning, which increases costs, radiation exposure, and resource utilization. AI-assisted detection allows for immediate identification and potential correction of errors during the initial scan, minimizing the need for repeat imaging. Some systems also provide prescriptive feedback, guiding technologists on optimal patient positioning, protocol adjustments, and acquisition parameter modifications. This proactive approach enhances image quality, streamlines workflow, and supports adherence to ALARA (As Low As Reasonably Achievable) principles in radiation safety.

Recent Advances / Emerging Therapies

Recent advances in AI for imaging error detection have been fueled by the development of large, diverse training datasets and improvements in model architecture. Novel approaches include unsupervised learning for anomaly detection, reinforcement learning for technologist guidance, and federated learning to preserve patient privacy while enabling multi-institutional data sharing. Emerging evidence supports the integration of AI-based quality control tools into routine clinical practice, with pilot studies demonstrating reductions in error rates, improved diagnostic confidence, and enhanced workflow efficiency. Regulatory bodies are beginning to issue guidance on the validation and deployment of such tools, reflecting their growing clinical relevance.

Guideline Recommendations

Several professional societies, including the Radiological Society of North America (RSNA) and the European Society of Radiology (ESR), now recommend the adoption of AI-driven quality assurance mechanisms as part of comprehensive imaging safety protocols. Guidelines emphasize the need for rigorous validation, transparency of algorithm performance, and integration with existing clinical governance frameworks. Continuous education for technologists and radiologists on AI system capabilities and limitations is also highlighted as essential for safe and effective implementation.

Conclusion

AI detection of imaging acquisition errors represents a transformative advance in radiological practice, offering the potential to enhance diagnostic accuracy, optimize resource utilization, and improve patient outcomes. As evidence accumulates and regulatory frameworks mature, the integration of AI-based tools into clinical workflows will likely become standard practice. Ongoing research, interdisciplinary collaboration, and adherence to evolving guidelines will be essential to maximize benefits while mitigating risks, ensuring that AI serves as a robust adjunct in the pursuit of imaging excellence.