Use of artificial intelligence in emergency radiology: An overview of current applications, challenges, and opportunities

Al‐Dasuqi, Khalid; Johnson, Michele H.; Cavallo, Joseph J.

doi:10.1016/j.clinimag.2022.05.010

Cited by 17 publications

(4 citation statements)

References 35 publications

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“…Various software based artificial intelligence has been developed and will be the future. 12 One of the most significant advancements is the widespread adoption of computed tomography (CT) scans, which provide detailed images of the body and enable rapid diagnoses of a wide range of medical conditions. Another important development is the use of portable ultrasound machines, which allow radiologists to perform real-time imaging and diagnosis at the bedside in the emergency department.…”

Section: Recent Advancesmentioning

confidence: 99%

Saving lives in real time: The role of radiology in emergency medicine

Akshay¹,

Trivedi²,

Lambe³

et al. 2023

JETHS

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Emergency medicine is a fast-paced and ever-evolving field that requires quick and accurate diagnoses to ensure the best possible outcomes for patients. Radiologists play a critical role in this process, as they use imaging techniques such as X-rays, CT scans, and MRI to provide valuable insights into a patient's condition. With a keen eye for detail and a deep understanding of anatomy and pathology, radiologists are often the first to identify signs of serious illness or injury, and their expertise is instrumental in guiding the course of treatment. In this review article, we will explore the innovative ways in which radiologists are transforming emergency medicine, and highlight the important contributions they make to patient care every day. From improving diagnostic accuracy to reducing wait times and streamlining treatment pathways, radiologists are making a real difference in the lives of patients and their families.

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Section: Recent Advancesmentioning

confidence: 99%

Saving lives in real time: The role of radiology in emergency medicine

Akshay¹,

Trivedi²,

Lambe³

et al. 2023

JETHS

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“…Therefore, CAST is being studied to alleviate congestion and perform rapid diagnoses. A previous study on the implementation of AI/ML-based CAD approved by the FDA in the ER indicated that numerous products are available for immediate use in emergency medicine [60]. The main targets of AI/ML-based CAD in ERs are stroke, followed by intracranial haemorrhage, spine injury, and other circumstances in which a delay of a few minutes could worsen the prognosis.…”

Section: Applicability Of Computer-aided Simple Triagementioning

confidence: 99%

Performance evaluation methods for improvements at post-market of artificial intelligence/machine learning-based computer-aided detection/diagnosis/triage in the United States

Yuba

Iwasaki²

2023

PLOS Digit Health

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Computer-aided detection (CADe), computer-aided diagnosis (CADx), and computer-aided simple triage (CAST), which incorporate artificial intelligence (AI) and machine learning (ML), are continually undergoing post-market improvement. Therefore, understanding the evaluation and approval process of improved products is important. This study intended to conduct a comprehensive survey of AI/ML-based CAD products approved by the U.S. Food and Drug Administration (FDA) that had been improved post-market to gain insights into the efficacy and safety required for market approval. A survey of the product code database published by the FDA identified eight products that were improved post-market. The methods used to evaluate the performance of improvements were analysed, and post-market improvements were approved with retrospective data. Reader study testing (RT) or software standalone testing (SA) procedures were conducted retrospectively. Six RT procedures were conducted because of modifications to the intended use. An average of 17.3 readers (minimum 14, maximum 24) participated, and the area under the curve (AUC) was considered the primary endpoint. The addition of study learning data that did not change the intended use and changes in the analysis algorithm were evaluated by SA. The average sensitivity, specificity, and AUC were 93% (minimum 91.1, maximum 97), 89.6% (minimum 85.9, maximum 96), and 0.96 (minimum 0.96, maximum 0.97), respectively. The average interval between applications was 348 days (minimum –18, maximum 975), which showed that the improvements were implemented within approximately one year. This is the first comprehensive study on AI/ML-based CAD products that have been improved post-market to elucidate evaluation points for post-market improvements. The findings will be informative for the industry and academia in developing and improving AI/ML-based CAD.

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“…QA1 QA2 QA3 QA4 Total P1 Abdellatif et al [16] 1 0 0 1 2 P2 Khan et al [17] 1 1 1 0.5 3.5 P3 Shaikh et al [18] 0 1.5 1 0 2.5 P4 Zarembo et al [19] 0.5 0.5 0.5 0.5 2 P5 Cao et al [20] 1 0.5 0 0 1.5 P6 Yang et al [21] 1 0.5 1 0.5 3 P7 Cote et al [22] 1 1 1 1 4 P8 Xu et al [23] 0.5 1 1 0.5 3 P9 Dey et al [24] 1 1 0.5 0 2.5 P10 Zanca et al [25] 1 0 1 1 3 P11 Al-Dasuqi et al [26] 0.5 1 0.5 0.5 2.5 P12 O'Hare et al [27] 1 1 1 0.5 3.5 P13 Druffel et al [28] 0 0 1 1 2 P14 Perkusich et al [29] 0.5 1 1 1 3.5 P15 Vinsard et al [30] 0.5 0 0.5 1 2 P16 Beegle et al [31] 1 0 1 0.5 2.5 P17 Salahirad et al [32] 1 1 0.5 0.5 3 P18 Dlamini et al [33] 0.5 0.5 0.5 0.5 2 P19 Cross et al [34] 0.5 1 1 1 3.5 P20 Malamateniou et al [35] 0 0 1 1 2 P21 Lenarduzzi et al [7] 1 0.5 0.5 0.5 2.5 P22 Sikorska et al [36] 0 1 0 1 2 P23 Field et al [37] 1 1 0 1 3 P24 Gao et al [38] 0.5 0 0 0 0.5 P25 Khanagar et al [39] 1 0 0 1 2 P26 Shafiq et al [40] 1 0.5 1 1 3.5 P27 Waade et al [41] 1 1 1 0.5 3.5 P28 Felderer et al [42] 1 0 0 0 1 P29 Ji et al [43] 0 0 1 1 2 P30 Harman et al [44] 1 0.5 1 1 3.5 P31 Shehab et al [45] 0 0.5 0.5 0.5 1.5 P32 Rana et al [46] 1 1 1 0 3 P33 Huang et al [47] 1 0.5 0.5 0.5 2.5 P34 Shakeel et al [48] 1 0.5 1 1 3.5 P35 Zhou et al [49] 0.5 0.5 0 0 1 P36 Sayago-Heredia et al…”

Section: Appendix Scores -Quality Assessmentmentioning

confidence: 99%

Taxonomy of Quality Assessment for Intelligent Software Systems: A Systematic Literature Review

Jabborov,

Kharlamova,

Kholmatova

et al. 2023

IEEE Access

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The increasing integration of AI software into various aspects of our daily lives has amplified the importance of evaluating the quality of these intelligent systems. The rapid proliferation of AI-based software projects and the growing reliance on these systems underscore the urgency of examining their quality for practical applications in both industry and academia. This systematic literature review delves into the study of quality assessment metrics and methods for AI-based systems, pinpointing key attributes and properties of intelligent software projects that are crucial for determining their quality. Furthermore, a comprehensive analysis of this domain will enable researchers to devise novel methods and metrics for effectively and efficiently evaluating the quality of such systems. Despite its importance, this area of development is still relatively nascent and evolving. This paper presents a systematic review of the current state of the taxonomy of quality assessment for AIbased software. We analyzed 271 articles from six different sources that focused on the quality assessment of intelligent software systems. The primary objective of this work is to provide an overview of the field and consolidate knowledge, which will aid researchers in identifying additional areas for future research. Moreover, our findings reveal the necessity to establish remedial strategies and develop tools to automate the process of identifying appropriate actions in response to abnormal metric values.

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Use of artificial intelligence in emergency radiology: An overview of current applications, challenges, and opportunities

Cited by 17 publications

References 35 publications

Saving lives in real time: The role of radiology in emergency medicine

Saving lives in real time: The role of radiology in emergency medicine

Performance evaluation methods for improvements at post-market of artificial intelligence/machine learning-based computer-aided detection/diagnosis/triage in the United States

Taxonomy of Quality Assessment for Intelligent Software Systems: A Systematic Literature Review

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