Machine Learning and Improved Quality Metrics in Acute Intracranial Hemorrhage by Noncontrast Computed Tomography

Davis, Melissa; Rao, Balaji; Cedeno, Paul A.; Saha, Atin; Zohrabian, Vahe M.

doi:10.1067/j.cpradiol.2020.10.007

Cited by 37 publications

(23 citation statements)

References 21 publications

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“…The reviewed body of literature was fairly recent, with the majority of the studies (32/45, 71%) having been published between 2020 and 2022 [ 45 - 76 ]. Most (33/45, 73%) of the articles were from North America and Europe [ 46 , 47 , 49 - 55 , 57 , 58 , 61 - 63 , 67 - 70 , 73 - 87 ], of which most (18/33, 55%) were from the United States [ 46 , 47 , 49 - 52 , 54 , 68 , 73 - 77 , 79 - 81 , 84 , 87 ]. The greatest number of AI systems were implemented either in hospital-wide settings (6/45, 13%) [ 50 , 55 , 56 , 65 , 74 , 80 ] or in radiology (6/45, 13%) [ 53 , 56 , 66 , 68 , 73 , 76 ].…”

Section: Resultsmentioning

confidence: 99%

“…Most (33/45, 73%) of the articles were from North America and Europe [ 46 , 47 , 49 - 55 , 57 , 58 , 61 - 63 , 67 - 70 , 73 - 87 ], of which most (18/33, 55%) were from the United States [ 46 , 47 , 49 - 52 , 54 , 68 , 73 - 77 , 79 - 81 , 84 , 87 ]. The greatest number of AI systems were implemented either in hospital-wide settings (6/45, 13%) [ 50 , 55 , 56 , 65 , 74 , 80 ] or in radiology (6/45, 13%) [ 53 , 56 , 66 , 68 , 73 , 76 ]. Most (27/45, 60%) of the studies were authored by a multidisciplinary team [ 46 , 47 , 50 - 55 , 58 , 59 , 61 , 62 , 64 , 67 , 69 , 70 , 72 , 74 , 75 , 78 - 80 , 82 , 86 - 89 ], with clinical and IT or informatics backgrounds being the most common combination (9/27, 33%) [ 47 , 50 , 55 , 61 , 70 , 74 , 79 , …”

Section: Resultsmentioning

confidence: 99%

“…Most (33/45, 73%) of the articles were from North America and Europe[46,47,[49][50][51][52][53][54][55]57,58,[61][62][63][67][68][69][70][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87], of which most (18/33, 55%) were from the United States[46,47,49-52,54,68,73-77, 79-81,84,87]. The greatest number of AI systems were implemented either in hospital-wide settings (6/45, 13%)[50,55,56,65,74,80] or in radiology (6/45, 13%)[53,56,66,68,73,76]. Most (27/45, 60%) of the studies were authored by a multidisciplinary team[46,47,50-55,58,…”

mentioning

confidence: 99%

“…59,61,62,64,67,69,70,72,74,75,[78][79][80]82,[86][87][88][89], with clinical and IT or informatics backgrounds being the most common combination (9/27, 33%)[47,50,55,61,70,74,79,87,89]. Among studies with authors from only 1 domain, the most common background was clinical (8/45, 18%)[63,65,66,68,71,73,76,84]. There was a wide range of study designs.…”

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confidence: 99%

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Artificial Intelligence Applications in Health Care Practice: Scoping Review

Sharma¹,

Savage²,

Nair³

et al. 2022

J Med Internet Res

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Background Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood. Objective The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible? Methods A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized. Results Of the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare. Conclusions Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Artificial Intelligence Applications in Health Care Practice: Scoping Review

Sharma¹,

Savage²,

Nair³

et al. 2022

J Med Internet Res

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“…Similarly, deep learning architecture software reportedly reduced the time to diagnosis of new intracranial hemorrhage on outpatient CT scans by 96% [11]. Furthermore, the use of artificial intelligence led to a reduction in report turnaround time in a level 1 trauma center [12].…”

Section: Discussionmentioning

confidence: 99%

Implementation of Machine Learning Software on the Radiology Worklist Decreases Scan View Delay for the Detection of Intracranial Hemorrhage on CT

Ginat

2021

Brain Sciences

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Background and Purpose: Prompt identification of acute intracranial hemorrhage on CT is important. The goal of this study was to assess the impact of artificial intelligence software for prioritizing positive cases. Materials and Methods: Cases analyzed by Aidoc (Tel Aviv, Israel) software for triaging acute intracranial hemorrhage cases on non-contrast head CT were retrospectively reviewed. The scan view delay time was calculated as the difference between the time the study was completed on PACS and the time the study was first opened by a radiologist. The scan view delay was stratified by scan location, including emergency, inpatient, and outpatient. The scan view delay times for cases flagged as positive by the software were compared to those that were not flagged. Results: A total of 8723 scans were assessed by the software, including 6894 cases that were not flagged and 1829 cases that were flagged as positive. Although there was no statistically significant difference in the scan view time for emergency cases, there was a significantly lower scan view time for positive outpatient and inpatient cases flagged by the software versus negative cases, with a reduction of 604 min on average, 90% in the scan view delay (p-value < 0.0001) for outpatients, and a reduction of 38 min on average, and 10% in the scan view delay (p-value <= 0.01) for inpatients. Conclusion: The use of artificial intelligence triage software for acute intracranial hemorrhage on head CT scans is associated with a significantly shorter scan view delay for cases flagged as positive than cases not flagged among outpatients and inpatients at an academic medical center.

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