Current Applications, Challenges, and Future Directions of Artificial Intelligence in Emergency Medicine: A Narrative Review
Artificial intelligence (AI) systems have witnessed notable advancements, revolutionizing various fields of research and medicine. Specifically, advancements of AI and the rapid growth of machine learning hold immense potential to significantly impact emergency medicine. This narrative review aimed...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Shahid Beheshti University of Medical Sciences
2025-05-01
|
| Series: | Archives of Academic Emergency Medicine |
| Subjects: | |
| Online Access: | https://journals.sbmu.ac.ir/aaem/index.php/AAEM/article/view/2712 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849722202994245632 |
|---|---|
| author | Mehrdad Farrokhi Amir H Fallahian Erfan Rahmani Ali Aghajan Morteza Alipour Parisa Jafari Khouzani Hossein Boustani Hezarani Hamed Sabzehie Mohammad Pirouzan Zahra Pirouzan Behnaz Dalvandi Reza Dalvandi Parisa Doroudgar Habib Azimi Fatemeh Moradi Amitis Nozari Maryam Sharifi Hamed Ghorbani Sara Moghimi Fatemeh Azarkish Soheil Bolandi Hooman Esfahani Sara Hosseinmirzaei Arezou Niknam Farzaneh Nikfarjam Parham Talebi Boroujeni Mahyar Noorbakhsh Parham Rahmani Fatemeh Rostamian Motlagh Khadijeh Harati Masoud Farrokhi Sina Talebi Lida Zare Lahijan |
| author_facet | Mehrdad Farrokhi Amir H Fallahian Erfan Rahmani Ali Aghajan Morteza Alipour Parisa Jafari Khouzani Hossein Boustani Hezarani Hamed Sabzehie Mohammad Pirouzan Zahra Pirouzan Behnaz Dalvandi Reza Dalvandi Parisa Doroudgar Habib Azimi Fatemeh Moradi Amitis Nozari Maryam Sharifi Hamed Ghorbani Sara Moghimi Fatemeh Azarkish Soheil Bolandi Hooman Esfahani Sara Hosseinmirzaei Arezou Niknam Farzaneh Nikfarjam Parham Talebi Boroujeni Mahyar Noorbakhsh Parham Rahmani Fatemeh Rostamian Motlagh Khadijeh Harati Masoud Farrokhi Sina Talebi Lida Zare Lahijan |
| author_sort | Mehrdad Farrokhi |
| collection | DOAJ |
| description |
Artificial intelligence (AI) systems have witnessed notable advancements, revolutionizing various fields of research and medicine. Specifically, advancements of AI and the rapid growth of machine learning hold immense potential to significantly impact emergency medicine. This narrative review aimed to summarize AI applications in prehospital emergency care, emergency radiology, triage and patient classification, emergency diagnosis and interventions, pediatric emergency care, trauma care, outcome prediction, as well as the legal and ethical challenges and limitations of AI use in emergency medicine.
A comprehensive literature search was conducted in Web of Science, Scopus, and Medline using a wide range of artificial intelligence and machine learning-related keywords combined with terms related to emergency medicine to identify relevant published studies. The findings show that AI-powered tools can assist clinicians in emergency departments in improving the management of prehospital emergency care, emergency radiology, triage, emergency department workflow, complex diagnoses, treatment, clinical decision-making, pediatric emergency care, trauma care, and the prediction of admissions, discharges, complications, and outcomes. However, the majority of these applications have been reported in retrospective studies, whereas randomized controlled trials (RCTs) are essential to determine the true value of AI in emergency settings. These applications can serve as effective tools in emergency departments when they are continuously supplied with high-quality real-time data and are adopted through collaboration between skilled data scientists and clinicians. Implementing these AI-assisted tools in emergency departments requires adequate infrastructure and machine learning operation systems.
Since emergency medicine involves various clinical decision-making scenarios based on classifications, flowcharts, and well-structured approaches, future well-designed prospective studies are necessary to achieve the goal of replacing conventional methods with new AI and machine learning techniques.
|
| format | Article |
| id | doaj-art-d2d9e8dc63e34cb29cb8ad8ccef988fd |
| institution | DOAJ |
| issn | 2645-4904 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Shahid Beheshti University of Medical Sciences |
| record_format | Article |
| series | Archives of Academic Emergency Medicine |
| spelling | doaj-art-d2d9e8dc63e34cb29cb8ad8ccef988fd2025-08-20T03:11:25ZengShahid Beheshti University of Medical SciencesArchives of Academic Emergency Medicine2645-49042025-05-0113110.22037/aaemj.v13i1.2712Current Applications, Challenges, and Future Directions of Artificial Intelligence in Emergency Medicine: A Narrative ReviewMehrdad Farrokhi0Amir H FallahianErfan RahmaniAli AghajanMorteza AlipourParisa Jafari KhouzaniHossein Boustani HezaraniHamed SabzehieMohammad PirouzanZahra PirouzanBehnaz DalvandiReza DalvandiParisa DoroudgarHabib AzimiFatemeh MoradiAmitis NozariMaryam SharifiHamed GhorbaniSara MoghimiFatemeh AzarkishSoheil BolandiHooman EsfahaniSara HosseinmirzaeiArezou NiknamFarzaneh NikfarjamParham Talebi BoroujeniMahyar NoorbakhshParham RahmaniFatemeh Rostamian MotlaghKhadijeh HaratiMasoud FarrokhiSina TalebiLida Zare LahijanShahid Beheshti University of Medical Sciences, Tehran, Iran Artificial intelligence (AI) systems have witnessed notable advancements, revolutionizing various fields of research and medicine. Specifically, advancements of AI and the rapid growth of machine learning hold immense potential to significantly impact emergency medicine. This narrative review aimed to summarize AI applications in prehospital emergency care, emergency radiology, triage and patient classification, emergency diagnosis and interventions, pediatric emergency care, trauma care, outcome prediction, as well as the legal and ethical challenges and limitations of AI use in emergency medicine. A comprehensive literature search was conducted in Web of Science, Scopus, and Medline using a wide range of artificial intelligence and machine learning-related keywords combined with terms related to emergency medicine to identify relevant published studies. The findings show that AI-powered tools can assist clinicians in emergency departments in improving the management of prehospital emergency care, emergency radiology, triage, emergency department workflow, complex diagnoses, treatment, clinical decision-making, pediatric emergency care, trauma care, and the prediction of admissions, discharges, complications, and outcomes. However, the majority of these applications have been reported in retrospective studies, whereas randomized controlled trials (RCTs) are essential to determine the true value of AI in emergency settings. These applications can serve as effective tools in emergency departments when they are continuously supplied with high-quality real-time data and are adopted through collaboration between skilled data scientists and clinicians. Implementing these AI-assisted tools in emergency departments requires adequate infrastructure and machine learning operation systems. Since emergency medicine involves various clinical decision-making scenarios based on classifications, flowcharts, and well-structured approaches, future well-designed prospective studies are necessary to achieve the goal of replacing conventional methods with new AI and machine learning techniques. https://journals.sbmu.ac.ir/aaem/index.php/AAEM/article/view/2712Artificial IntelligenceData ScienceDeep LearningEmergency MedicineMachine LearningPrediction Algorithms |
| spellingShingle | Mehrdad Farrokhi Amir H Fallahian Erfan Rahmani Ali Aghajan Morteza Alipour Parisa Jafari Khouzani Hossein Boustani Hezarani Hamed Sabzehie Mohammad Pirouzan Zahra Pirouzan Behnaz Dalvandi Reza Dalvandi Parisa Doroudgar Habib Azimi Fatemeh Moradi Amitis Nozari Maryam Sharifi Hamed Ghorbani Sara Moghimi Fatemeh Azarkish Soheil Bolandi Hooman Esfahani Sara Hosseinmirzaei Arezou Niknam Farzaneh Nikfarjam Parham Talebi Boroujeni Mahyar Noorbakhsh Parham Rahmani Fatemeh Rostamian Motlagh Khadijeh Harati Masoud Farrokhi Sina Talebi Lida Zare Lahijan Current Applications, Challenges, and Future Directions of Artificial Intelligence in Emergency Medicine: A Narrative Review Archives of Academic Emergency Medicine Artificial Intelligence Data Science Deep Learning Emergency Medicine Machine Learning Prediction Algorithms |
| title | Current Applications, Challenges, and Future Directions of Artificial Intelligence in Emergency Medicine: A Narrative Review |
| title_full | Current Applications, Challenges, and Future Directions of Artificial Intelligence in Emergency Medicine: A Narrative Review |
| title_fullStr | Current Applications, Challenges, and Future Directions of Artificial Intelligence in Emergency Medicine: A Narrative Review |
| title_full_unstemmed | Current Applications, Challenges, and Future Directions of Artificial Intelligence in Emergency Medicine: A Narrative Review |
| title_short | Current Applications, Challenges, and Future Directions of Artificial Intelligence in Emergency Medicine: A Narrative Review |
| title_sort | current applications challenges and future directions of artificial intelligence in emergency medicine a narrative review |
| topic | Artificial Intelligence Data Science Deep Learning Emergency Medicine Machine Learning Prediction Algorithms |
| url | https://journals.sbmu.ac.ir/aaem/index.php/AAEM/article/view/2712 |
| work_keys_str_mv | AT mehrdadfarrokhi currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT amirhfallahian currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT erfanrahmani currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT aliaghajan currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT mortezaalipour currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT parisajafarikhouzani currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT hosseinboustanihezarani currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT hamedsabzehie currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT mohammadpirouzan currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT zahrapirouzan currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT behnazdalvandi currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT rezadalvandi currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT parisadoroudgar currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT habibazimi currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT fatemehmoradi currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT amitisnozari currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT maryamsharifi currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT hamedghorbani currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT saramoghimi currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT fatemehazarkish currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT soheilbolandi currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT hoomanesfahani currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT sarahosseinmirzaei currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT arezouniknam currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT farzanehnikfarjam currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT parhamtalebiboroujeni currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT mahyarnoorbakhsh currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT parhamrahmani currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT fatemehrostamianmotlagh currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT khadijehharati currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT masoudfarrokhi currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT sinatalebi currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview AT lidazarelahijan currentapplicationschallengesandfuturedirectionsofartificialintelligenceinemergencymedicineanarrativereview |