Artificial Intelligence in Optimizing the Functioning of Emergency Departments; a Systematic Review of Current Solutions
Introduction: The burgeoning burden on emergency departments is a global challenge that we have been confronting for many years. Emerging artificial intelligence (AI)-based solutions may constitute a critical component in the optimization of these units. This systematic review was conducted to thor...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Shahid Beheshti University of Medical Sciences
2024-01-01
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| Series: | Archives of Academic Emergency Medicine |
| Subjects: | |
| Online Access: | https://journals.sbmu.ac.ir/aaem/index.php/AAEM/article/view/2110 |
| _version_ | 1797336315827585024 |
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| author | Szymczyk Aleksandra Krion Robert Krzyzaniak Klaudia Lubian Dawid Sieminski Mariusz |
| author_facet | Szymczyk Aleksandra Krion Robert Krzyzaniak Klaudia Lubian Dawid Sieminski Mariusz |
| author_sort | Szymczyk Aleksandra |
| collection | DOAJ |
| description |
Introduction: The burgeoning burden on emergency departments is a global challenge that we have been confronting for many years. Emerging artificial intelligence (AI)-based solutions may constitute a critical component in the optimization of these units. This systematic review was conducted to thoroughly examine and summarize the currently available AI solutions, assess potential benefits from their implementation, and identify anticipated directions of further development in this fascinating and rapidly evolving field.
Methods: This systematic review utilized data compiled from three key scientific databases: PubMed (2045 publications), Scopus (877 publications), and Web of Science (2495 publications). After meticulous removal of duplicates, we conducted a detailed analysis of 2052 articles, including 147 full-text papers. From these, we selected 51 of the most pertinent and representative publications for the review.
Results: Overall the present research indicates that due to high accuracy and sensitivity of machine learning (ML) models it's reasonable to use AI in support of doctors as it can show them the potential diagnosis, which could save time and resources. However, AI-generated diagnoses should be verified by a doctor as AI is not infallible
Conclusions: Currently available AI algorithms are capable of analysing complex medical data with unprecedented precision and speed. Despite AI's vast potential, it is still a nascent technology that is often perceived as complicated and challenging to implement. We propose that a pivotal point in effectively harnessing this technology is the close collaboration between medical professionals and AI experts. Future research should focus on further refining AI algorithms, performing comprehensive validation, and introducing suitable legal regulations and standard procedures, thereby fully leveraging the potential of AI to enhance the quality and efficiency of healthcare delivery.
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| first_indexed | 2024-03-08T08:51:34Z |
| format | Article |
| id | doaj.art-b04bd468c1534802943eb1a8e9c5ce16 |
| institution | Directory Open Access Journal |
| issn | 2645-4904 |
| language | English |
| last_indexed | 2024-03-08T08:51:34Z |
| publishDate | 2024-01-01 |
| publisher | Shahid Beheshti University of Medical Sciences |
| record_format | Article |
| series | Archives of Academic Emergency Medicine |
| spelling | doaj.art-b04bd468c1534802943eb1a8e9c5ce162024-02-01T08:35:28ZengShahid Beheshti University of Medical SciencesArchives of Academic Emergency Medicine2645-49042024-01-0112110.22037/aaem.v12i1.2110Artificial Intelligence in Optimizing the Functioning of Emergency Departments; a Systematic Review of Current SolutionsSzymczyk Aleksandra0Krion Robert1Krzyzaniak Klaudia2Lubian DawidSieminski Mariusz3M.D.M.D.Medical University of Gdanskprofessor Introduction: The burgeoning burden on emergency departments is a global challenge that we have been confronting for many years. Emerging artificial intelligence (AI)-based solutions may constitute a critical component in the optimization of these units. This systematic review was conducted to thoroughly examine and summarize the currently available AI solutions, assess potential benefits from their implementation, and identify anticipated directions of further development in this fascinating and rapidly evolving field. Methods: This systematic review utilized data compiled from three key scientific databases: PubMed (2045 publications), Scopus (877 publications), and Web of Science (2495 publications). After meticulous removal of duplicates, we conducted a detailed analysis of 2052 articles, including 147 full-text papers. From these, we selected 51 of the most pertinent and representative publications for the review. Results: Overall the present research indicates that due to high accuracy and sensitivity of machine learning (ML) models it's reasonable to use AI in support of doctors as it can show them the potential diagnosis, which could save time and resources. However, AI-generated diagnoses should be verified by a doctor as AI is not infallible Conclusions: Currently available AI algorithms are capable of analysing complex medical data with unprecedented precision and speed. Despite AI's vast potential, it is still a nascent technology that is often perceived as complicated and challenging to implement. We propose that a pivotal point in effectively harnessing this technology is the close collaboration between medical professionals and AI experts. Future research should focus on further refining AI algorithms, performing comprehensive validation, and introducing suitable legal regulations and standard procedures, thereby fully leveraging the potential of AI to enhance the quality and efficiency of healthcare delivery. https://journals.sbmu.ac.ir/aaem/index.php/AAEM/article/view/2110Artificial intelligenceEmergency service, hospitalEmergency medicineMachine learning |
| spellingShingle | Szymczyk Aleksandra Krion Robert Krzyzaniak Klaudia Lubian Dawid Sieminski Mariusz Artificial Intelligence in Optimizing the Functioning of Emergency Departments; a Systematic Review of Current Solutions Archives of Academic Emergency Medicine Artificial intelligence Emergency service, hospital Emergency medicine Machine learning |
| title | Artificial Intelligence in Optimizing the Functioning of Emergency Departments; a Systematic Review of Current Solutions |
| title_full | Artificial Intelligence in Optimizing the Functioning of Emergency Departments; a Systematic Review of Current Solutions |
| title_fullStr | Artificial Intelligence in Optimizing the Functioning of Emergency Departments; a Systematic Review of Current Solutions |
| title_full_unstemmed | Artificial Intelligence in Optimizing the Functioning of Emergency Departments; a Systematic Review of Current Solutions |
| title_short | Artificial Intelligence in Optimizing the Functioning of Emergency Departments; a Systematic Review of Current Solutions |
| title_sort | artificial intelligence in optimizing the functioning of emergency departments a systematic review of current solutions |
| topic | Artificial intelligence Emergency service, hospital Emergency medicine Machine learning |
| url | https://journals.sbmu.ac.ir/aaem/index.php/AAEM/article/view/2110 |
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