Artificial intelligence in emergency medicine: A scoping review

Kirubarajan, Abirami; Taher, Ahmed; Khan, Shawn; Masood, Sameer

doi:10.1002/emp2.12277

Cited by 65 publications

(37 citation statements)

References 73 publications

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“…One of the major challenges to bringing this work from the computer science laboratory to the bedside will be creating the interfaces necessary to implement it on the messy data of the real-time EHR. A review of the 20 most recently published studies cited by Kirubaranjan’s scoping review of AI in EM found that only one was implemented in the EHR, the rest performed on retrospective datasets exported for research [ 5 ]. As the work of EHR integration and data cleaning is as great or greater than writing ML algorithms, there is still a large gap between many published academic projects and their readiness for use in clinical practice.…”

Section: Reviewmentioning

confidence: 99%

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Machine Learning and Precision Medicine in Emergency Medicine: The Basics

Lee

Lam

Rocha

et al. 2021

Cureus

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As machine learning (ML) and precision medicine become more readily available and used in practice, emergency physicians must understand the potential advantages and limitations of the technology. This narrative review focuses on the key components of machine learning, artificial intelligence, and precision medicine in emergency medicine (EM). Based on the content expertise, we identified articles from EM literature. The authors provided a narrative summary of each piece of literature. Next, the authors provided an introduction of the concepts of ML, artificial intelligence as an extension of ML, and precision medicine. This was followed by concrete examples of their applications in practice and research. Subsequently, we shared our thoughts on how to consume the existing research in these subjects and conduct high-quality research for academic emergency medicine. We foresee that the EM community will continue to adapt machine learning, artificial intelligence, and precision medicine in research and practice. We described several key components using our expertise.

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Section: Reviewmentioning

confidence: 99%

“…Although there are multiple prior papers using ML and AI in EM, including some recent reviews of the topic, few papers describe the basic concepts in a digestible format for emergency physicians without prior statistical or research backgrounds [ 5 ]. In this paper, we provide an introduction to the concepts of ML and precision medicine.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Precision Medicine in Emergency Medicine: The Basics

Lee

Lam

Rocha

et al. 2021

Cureus

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“…By addressing this research question, we complement related studies on AI in emergency care [ 12 , 13 ] with a distinct focus on why and how AI is used in emergency care and its effects on the work design of clinicians.…”

Section: Introductionmentioning

confidence: 99%

Influence of artificial intelligence on the work design of emergency department clinicians a systematic literature review

Boonstra

Laven

2022

BMC Health Serv Res

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Objective This systematic literature review aims to demonstrate how Artificial Intelligence (AI) is currently used in emergency departments (ED) and how it alters the work design of ED clinicians. AI is still new and unknown to many healthcare professionals in emergency care, leading to unfamiliarity with its capabilities. Method Various criteria were used to establish the suitability of the articles to answer the research question. This study was based on 34 selected peer-reviewed papers on the use of Artificial Intelligence (AI) in the Emergency Department (ED), published in the last five years. Drawing on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, all articles were scanned, read full-text, and analyzed afterward. Results The majority of the AI applications consisted of AI-based tools to aid with clinical decisions and to relieve overcrowded EDs of their burden. AI support was mostly offered during triage, the moment that sets the patient trajectory. There is ample evidence that AI-based applications could improve the clinical decision-making process. Conclusion The use of AI in EDs is still in its nascent stages. Many studies focus on the question of whether AI has clinical utility, such as decision support, improving resource allocation, reducing diagnostic errors, and promoting proactivity. Some studies suggest that AI-based tools essentially have the ability to outperform human skills. However, it is evident from the literature that current technology does not have the aims or power to do so. Nevertheless, AI-based tools can impact clinician work design in the ED by providing support with clinical decisions, which could ultimately help alleviate a portion of the increasing clinical burden.

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“…More recently there have been scoping reviews of AI that have examined their use in aiding in medical diagnoses 27 and for clinical decision support. 28 Both reviews examined triage and unstructured narratives data, but they included these with disparate types of data.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, neither study referenced the previously mentioned systematic reviews, and both missed a significant number of studies identified in the systematic reviews that examined AI. Some of these omissions may have been intentional given that one review excluded studies that were linked to specific presentations (such as influenza, chest pain or trauma)28 or because the other review included only those that explicitly mentioned the use of synonyms for ‘artificial intelligence’ in their title, were not directly relevant to medicine (eg, public health) or ‘did not report outcomes or evaluations’ (eg, non-intervention epidemiological studies) 27…”

Section: Introductionmentioning

confidence: 99%

Emergency nurses’ triage narrative data, their uses and structure: a scoping review protocol

et al. 2022

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IntroductionThe first clinical interaction most patients have in the emergency department occurs during triage. An unstructured narrative is generated during triage and is the first source of in-hospital documentation. These narratives capture the patient’s reported reason for the visit and the initial assessment and offer significantly more nuanced descriptions of the patient’s complaints than fixed field data. Previous research demonstrated these data are useful for predicting important clinical outcomes. Previous reviews examined these narratives in combination or isolation with other free-text sources, but used restricted searches and are becoming outdated. Furthermore, there are no reviews focused solely on nurses’ (the primary collectors of these data) narratives.Methods and analysisUsing the Arksey and O’Malley scoping review framework and PRISMA-ScR reporting guidelines, we will perform structured searches of CINAHL, Ovid MEDLINE, ProQuest Central, Ovid Embase and Cochrane Library (via Wiley). Additionally, we will forward citation searches of all included studies. No geographical or study design exclusion criteria will be used. Studies examining disaster triage, published before 1990, and non-English language literature will be excluded. Data will be managed using online management tools; extracted data will be independently confirmed by a separate reviewer using prepiloted extraction forms. Cohen’s kappa will be used to examine inter-rater agreement on pilot and final screening. Quantitative data will be expressed using measures of range and central tendency, counts, proportions and percentages, as appropriate. Qualitative data will be narrative summaries of the authors’ primary findings.Patient and public involvementNo patients involved.Ethics and disseminationNo ethics approval is required. Findings will be submitted to peer-reviewed conferences and journals. Results will be disseminated using individual and institutional social media platforms.

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Artificial intelligence in emergency medicine: A scoping review

Cited by 65 publications

References 73 publications

Machine Learning and Precision Medicine in Emergency Medicine: The Basics

Machine Learning and Precision Medicine in Emergency Medicine: The Basics

Influence of artificial intelligence on the work design of emergency department clinicians a systematic literature review

Emergency nurses’ triage narrative data, their uses and structure: a scoping review protocol

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