Applications of Artificial Intelligence in the Radiology Roundtrip: Process Streamlining, Workflow Optimization, and Beyond

Pierre, Kevin; Haneberg, Adam G.; Kwak, Sean; Peters, Keith; Hochhegger, Bruno; Sananmuang, Thiparom; Tunlayadechanont, Padcha; Tighe, Patrick J.; Mancuso, Anthony A.; Forghani, Reza

doi:10.1053/j.ro.2023.02.003

Cited by 15 publications

(5 citation statements)

References 109 publications

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“…AI-based radiology workflow optimization is currently a major topic in the scientific community, with lots of research being conducted. Most of the published work has focused on optimization of components of the workflow other than report generation (e.g., AI-aided improvements in scan protocols, work lists, or hanging protocols) [ 1 , 29 , 30 ]. The studies that do focus on the reporting process include NLP-based integration of speech recognition into SR, large language model (LLM)-based creation of structured reports from free-text reports, or the automated prediction of the impressions section of free-text reports [ 22 , 31 , 32 ].…”

Section: Discussionmentioning

confidence: 99%

A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports

Jorg,

Halfmann,

Stoehr

et al. 2024

Insights Imaging

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Objectives Artificial intelligence (AI) has tremendous potential to help radiologists in daily clinical routine. However, a seamless, standardized, and time-efficient way of integrating AI into the radiology workflow is often lacking. This constrains the full potential of this technology. To address this, we developed a new reporting pipeline that enables automated pre-population of structured reports with results provided by AI tools. Methods Findings from a commercially available AI tool for chest X-ray pathology detection were sent to an IHE-MRRT-compliant structured reporting (SR) platform as DICOM SR elements and used to automatically pre-populate a chest X-ray SR template. Pre-populated AI results could be validated, altered, or deleted by radiologists accessing the SR template. We assessed the performance of this newly developed AI to SR pipeline by comparing reporting times and subjective report quality to reports created as free-text and conventional structured reports. Results Chest X-ray reports with the new pipeline could be created in significantly less time than free-text reports and conventional structured reports (mean reporting times: 66.8 s vs. 85.6 s and 85.8 s, respectively; both p < 0.001). Reports created with the pipeline were rated significantly higher quality on a 5-point Likert scale than free-text reports (p < 0.001). Conclusion The AI to SR pipeline offers a standardized, time-efficient way to integrate AI-generated findings into the reporting workflow as parts of structured reports and has the potential to improve clinical AI integration and further increase synergy between AI and SR in the future. Critical relevance statement With the AI-to-structured reporting pipeline, chest X-ray reports can be created in a standardized, time-efficient, and high-quality manner. The pipeline has the potential to improve AI integration into daily clinical routine, which may facilitate utilization of the benefits of AI to the fullest. Key points • A pipeline was developed for automated transfer of AI results into structured reports. • Pipeline chest X-ray reporting is faster than free-text or conventional structured reports. • Report quality was also rated higher for reports created with the pipeline. • The pipeline offers efficient, standardized AI integration into the clinical workflow. Graphical Abstract

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Section: Discussionmentioning

confidence: 99%

A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports

Jorg,

Halfmann,

Stoehr

et al. 2024

Insights Imaging

View full text Add to dashboard Cite

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“…The application of ML research to clinical practice requires not only algorithm development but also validation in varied clinical environments [100]. For example, the CNN model developed by Pease et al ( 2022) outperformed the IMPACT model in sixmonth-outcome prediction when tested on internal data but showed no improvement during external validation [85].…”

Section: Research Frontiers: Expanding the Role Of ML In Tbi Diagnosi...mentioning

confidence: 99%

“…The principal advantage of ML resides in its improved efficiency in both diagnostic assistance and workflow improvement [100]. The potential future improvement in diagnostic times, workflow optimization, and treatment decision support will likely enhance patient throughput, reduce waiting times, and improve departmental efficiency.…”

Section: Cost Considerationsmentioning

confidence: 99%

Machine Learning in Neuroimaging of Traumatic Brain Injury: Current Landscape, Research Gaps, and Future Directions

Pierre,

Turetsky,

Raviprasad

et al. 2024

Trauma Care

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In this narrative review, we explore the evolving role of machine learning (ML) in the diagnosis, prognosis, and clinical management of traumatic brain injury (TBI). The increasing prevalence of TBI necessitates advanced techniques for timely and accurate diagnosis, and ML offers promising tools to meet this challenge. Current research predominantly focuses on integrating clinical data, patient demographics, lab results, and imaging findings, but there remains a gap in fully harnessing the potential of image features. While advancements have been made in areas such as subdural hematoma segmentation and prognosis prediction, the translation of these techniques into clinical practice is still in its infancy. This is further compounded by challenges related to data privacy, clinician trust, and the interoperability of various health systems. Despite these hurdles, FDA-approved ML applications for TBI and their subsequent promising results underscore the potential of ML in revolutionizing TBI care. This review concludes by emphasizing the importance of bridging the gap between theoretical research and real-world clinical application and the necessity of addressing the ethical and privacy implications of integrating ML into healthcare.

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“…The integration of AI into echocardiography offers numerous advantages, including workflow optimization through the automation of tasks such as image acquisition, segmentation, and measurements [17,[46][47][48][49][50][51]. This automation enhances efficiency and productivity by reducing the need for manual procedures, ensuring consistent measurement accuracy, and streamlining report generation [17,[47][48][49][50][51][52].…”

Section: Benefits and Implicationsmentioning

confidence: 99%

Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve

Vasile,

Iriart

2023

Diagnostics

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Advancements in artificial intelligence (AI) have rapidly transformed various sectors, and the field of echocardiography is no exception. AI-driven technologies hold immense potential to revolutionize echo labs’ diagnostic capabilities and improve patient care. This paper explores the importance for echo labs to embrace AI and stay ahead of the curve in harnessing its power. Our manuscript provides an overview of the growing impact of AI on medical imaging, specifically echocardiography. It highlights how AI-driven algorithms can enhance image quality, automate measurements, and accurately diagnose cardiovascular diseases. Additionally, we emphasize the importance of training echo lab professionals in AI implementation to optimize its integration into routine clinical practice. By embracing AI, echo labs can overcome challenges such as workload burden and diagnostic accuracy variability, improving efficiency and patient outcomes. This paper highlights the need for collaboration between echocardiography laboratory experts, AI researchers, and industry stakeholders to drive innovation and establish standardized protocols for implementing AI in echocardiography. In conclusion, this article emphasizes the importance of AI adoption in echocardiography labs, urging practitioners to proactively integrate AI technologies into their workflow and take advantage of their present opportunities. Embracing AI is not just a choice but an imperative for echo labs to maintain their leadership and excel in delivering state-of-the-art cardiac care in the era of advanced medical technologies.

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Applications of Artificial Intelligence in the Radiology Roundtrip: Process Streamlining, Workflow Optimization, and Beyond

Cited by 15 publications

References 109 publications

A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports

A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports

Machine Learning in Neuroimaging of Traumatic Brain Injury: Current Landscape, Research Gaps, and Future Directions

Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve

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