Archives of Academic Emergency Medicine

Shafaf N, Malek H. Applications of Machine Learning Approaches in Emergency Medicine; a Review Article. Archives of Academic Emergency Medicine. 2019;7(1):e34.

Hosseini SM, Mohtarami SA, Shadnia S, Rahimi M, Erfan Talab Evini P, Mostafazadeh B, et al. Detection of Body Packs in Abdominal CT scans Through Artificial Intelligence; Developing a Machine Learning-based Model. Archives of Academic Emergency Medicine. 2024;13(1):e23.

Aleksandra S, Robert K, Klaudia K, Dawid L, Mariusz S. Artificial Intelligence in Optimizing the Functioning of Emergency Departments; a Systematic Review of Current Solutions. Archives of Academic Emergency Medicine. 2024;12(1):e22.

Emami P, Javanmardi K. Enhancing Emergency Response through Artificial Intelligence in Emergency Medical Services Dispatching; a Letter to Editor. Archives of Academic Emergency Medicine. 2023;11(1):e60.

Momeni pour Z, Beheshti Shirazi AA. Identifying COVID-19-Infected Segments in Lung CT Scan Through Two Innovative Artificial Intelligence-Based Transformer Models. Archives of Academic Emergency Medicine. 2024;13(1):e21.

Kaboudi N, Firouzbakht S, Shahir Eftekhar M, Fayazbakhsh F, Joharivarnoosfaderani N, Ghaderi S, et al. Diagnostic Accuracy of ChatGPT for Patients’ Triage; a Systematic Review and Meta-Analysis. Archives of Academic Emergency Medicine. 2024;12(1):e60.

Lee S, Mohr NM, Street WN, Nadkarni P. Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview. West J Emerg Med. 2019;20(2):219-27.

Ramgopal S, Sanchez-Pinto LN, Horvat CM, Carroll MS, Luo Y, Florin TA. Artificial intelligence-based clinical decision support in pediatrics. Pediatr Res. 2023;93(2):334-41.

Rajula HSR, Verlato G, Manchia M, Antonucci N, Fanos V. Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment. Medicina (Kaunas). 2020;56(9).

Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719-31.

Scerbo M, Radhakrishnan H, Cotton B, Dua A, Del Junco D, Wade C, et al. Prehospital triage of trauma patients using the Random Forest computer algorithm. J Surg Res. 2014;187(2):371-6.

Kim JS, Cho Y, Lim TH. Prediction of the location of the glottis in laryngeal images by using a novel deep-learning algorithm. Ieee Access. 2019;7:79545-54.

Kim S, Guo W, Williams R, Stankovic J, Alemzadeh H, editors. Information extraction from patient care reports for intelligent emergency medical services. 2021 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE); 2021: IEEE.

Liu N, Zhang Z, Ho AFW, Ong MEH. Artificial intelligence in emergency medicine. Journal of Emergency and Critical Care Medicine. 2018;2.

Piliuk K, Tomforde S. Artificial intelligence in emergency medicine. A systematic literature review. Int J Med Inform. 2023;180:105274.

Paranjape K, Schinkel M, Nannan Panday R, Car J, Nanayakkara P. Introducing Artificial Intelligence Training in Medical Education. JMIR Med Educ. 2019;5(2):e16048.

Toy J, Warren J, Wilhelm K, Putnam B, Whitfield D, Gausche-Hill M, et al. Use of artificial intelligence to support prehospital traumatic injury care: A scoping review. J Am Coll Emerg Physicians Open. 2024;5(5):e13251.

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Chee ML, Chee ML, Huang H, Mazzochi K, Taylor K, Wang H, et al. Artificial intelligence and machine learning in prehospital emergency care: A scoping review. iScience. 2023;26(8):107407.

Blackwell TH. Prehospital care. Emerg Med Clin North Am. 1993;11(1):1-14.

Brice JH, Valenzuela T, Ornato JP, Swor RA, Overton J, Pirrallo RG, et al. Optimal prehospital cardiovascular care. Prehosp Emerg Care. 2001;5(1):65-72.

George L, Ramamoorthy L, Satheesh S, Saya RP, Subrahmanyam DK. Prehospital delay and time to reperfusion therapy in ST elevation myocardial infarction. J Emerg Trauma Shock. 2017;10(2):64-9.

Chen CH, Shin SD, Sun JT, Jamaluddin SF, Tanaka H, Song KJ, et al. Association between prehospital time and outcome of trauma patients in 4 Asian countries: A cross-national, multicenter cohort study. PLoS Med. 2020;17(10):e1003360.

Chen C, Kan T, Li S, Qiu C, Gui L. Use and implementation of standard operating procedures and checklists in prehospital emergency medicine: a literature review. Am J Emerg Med. 2016;34(12):2432-9.

Ishikawa K, Yanagawa Y, Ota S, Muramatsu KI, Nagasawa H, Jitsuiki K, et al. Preliminary study of prehospital use of smart glasses. Acute Med Surg. 2022;9(1):e807.

Kim JH, Kim MJ, Kim HC, Kim HY, Sung JM, Chang HJ. A Novel Artificial Intelligence-Enhanced Digital Network for Prehospital Emergency Support: Community Intervention Study. J Med Internet Res. 2025;27:e58177.

Mueller B, Kinoshita T, Peebles A, Graber MA, Lee S. Artificial intelligence and machine learning in emergency medicine: a narrative review. Acute Med Surg. 2022;9(1):e740.

Ventura CAI, Denton EE. Artificial Intelligence Chatbots and Emergency Medical Services: Perspectives on the Implications of Generative AI in Prehospital Care. Open Access Emerg Med. 2023;15:289-92.

Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts H. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500-10.

Chong J, Patlas MN. Radiology Artificial Intelligence: Bringing Theory to Clinical Practice. Can Assoc Radiol J. 2021;72(1):6.

Tadavarthi Y, Makeeva V, Wagstaff W, Zhan H, Podlasek A, Bhatia N, et al. Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice. Radiol Artif Intell. 2022;4(2):e210114.

Liu J, Nazeri A, Mintz A. Role of Artificial Intelligence in Emergency Radiology. Atlas of Emergency Imaging from Head-to-Toe: Springer; 2022. p. 1-7.

Nakaura T, Higaki T, Awai K, Ikeda O, Yamashita Y. A primer for understanding radiology articles about machine learning and deep learning. Diagn Interv Imaging. 2020;101(12):765-70.

Jalal S, Parker W, Ferguson D, Nicolaou S. Exploring the Role of Artificial Intelligence in an Emergency and Trauma Radiology Department. Can Assoc Radiol J. 2021;72(1):167-74.

Katzman BD, van der Pol CB, Soyer P, Patlas MN. Artificial intelligence in emergency radiology: A review of applications and possibilities. Diagn Interv Imaging. 2023;104(1):6-10.

Wu JT, Wong KCL, Gur Y, Ansari N, Karargyris A, Sharma A, et al. Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents. JAMA Netw Open. 2020;3(10):e2022779.

Winkel DJ, Heye T, Weikert TJ, Boll DT, Stieltjes B. Evaluation of an AI-Based Detection Software for Acute Findings in Abdominal Computed Tomography Scans: Toward an Automated Work List Prioritization of Routine CT Examinations. Invest Radiol. 2019;54(1):55-9.

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Chenais G, Lagarde E, Gil-Jardiné C. Artificial Intelligence in Emergency Medicine: Viewpoint of Current Applications and Foreseeable Opportunities and Challenges. J Med Internet Res. 2023;25:e40031.

Morley C, Unwin M, Peterson GM, Stankovich J, Kinsman L. Emergency department crowding: A systematic review of causes, consequences and solutions. PLoS One. 2018;13(8):e0203316.

Parkinson B, Meacock R, Checkland K, Sutton M. Clarifying the concept of avoidable emergency department attendance. J Health Serv Res Policy. 2021;26(1):68-73.

Kadum SY, Salman OH, Taha ZK, Said AB, Ali MA, Qassim QS, et al. Machine learning-based telemedicine framework to prioritize remote patients with multi-chronic diseases for emergency healthcare services. Network Modeling Analysis in Health Informatics and Bioinformatics. 2023;12(1):11.

Gao Z, Qi X, Zhang X, Gao X, He X, Guo S, et al. Developing and Validating an Emergency Triage Model Using Machine Learning Algorithms with Medical Big Data. Risk Manag Healthc Policy. 2022;15:1545-51.

Tyler S, Olis M, Aust N, Patel L, Simon L, Triantafyllidis C, et al. Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review. Cureus. 2024;16(5):e59906.

Berlyand Y, Raja AS, Dorner SC, Prabhakar AM, Sonis JD, Gottumukkala RV, et al. How artificial intelligence could transform emergency department operations. Am J Emerg Med. 2018;36(8):1515-7.

Elayan H, Aloqaily M, Guizani M. Digital twin for intelligent context-aware IoT healthcare systems. IEEE Internet of Things Journal. 2021;8(23):16749-57.

Chaou CH, Chen HH, Chang SH, Tang P, Pan SL, Yen AM, et al. Predicting Length of Stay among Patients Discharged from the Emergency Department-Using an Accelerated Failure Time Model. PLoS One. 2017;12(1):e0165756.

Bacchi S, Tan Y, Oakden-Rayner L, Jannes J, Kleinig T, Koblar S. Machine learning in the prediction of medical inpatient length of stay. Intern Med J. 2022;52(2):176-85.

Iyengar SS, Lepper MR. When choice is demotivating: Can one desire too much of a good thing? Journal of personality and social psychology. 2000;79(6):995.

Cook DA, Sherbino J, Durning SJ. Management Reasoning: Beyond the Diagnosis. Jama. 2018;319(22):2267-8.

Patel JJ, Bergl PA. Diagnostic vs Management Reasoning. Jama. 2018;320(17):1818.

Howard L, Wibberley C, Crowe L, Body R. How events in emergency medicine impact doctors' psychological well-being. Emerg Med J. 2018;35(10):595-9.

Kuhn G. Circadian rhythm, shift work, and emergency medicine. Ann Emerg Med. 2001;37(1):88-98.

Norman GR, Monteiro SD, Sherbino J, Ilgen JS, Schmidt HG, Mamede S. The Causes of Errors in Clinical Reasoning: Cognitive Biases, Knowledge Deficits, and Dual Process Thinking. Acad Med. 2017;92(1):23-30.

Graber ML, Kissam S, Payne VL, Meyer AN, Sorensen A, Lenfestey N, et al. Cognitive interventions to reduce diagnostic error: a narrative review. BMJ Qual Saf. 2012;21(7):535-57.

Patterson BW, Pulia MS, Ravi S, Hoonakker PLT, Schoofs Hundt A, Wiegmann D, et al. Scope and Influence of Electronic Health Record-Integrated Clinical Decision Support in the Emergency Department: A Systematic Review. Ann Emerg Med. 2019;74(2):285-96.

Graber ML. Reaching 95%: decision support tools are the surest way to improve diagnosis now. BMJ Qual Saf. 2022;31(6):415-8.

Douthit BJ, Musser RC, Lytle KS, Richesson RL. A Closer Look at the "Right" Format for Clinical Decision Support: Methods for Evaluating a Storyboard BestPractice Advisory. J Pers Med. 2020;10(4).

Chen RJ, Wang JJ, Williamson DFK, Chen TY, Lipkova J, Lu MY, et al. Algorithmic fairness in artificial intelligence for medicine and healthcare. Nat Biomed Eng. 2023;7(6):719-42.

Shah-Mohammadi F, Finkelstein J. Accuracy Evaluation of GPT-Assisted Differential Diagnosis in Emergency Department. Diagnostics (Basel). 2024;14(16).

McDuff D, Schaekermann M, Tu T, Palepu A, Wang A, Garrison J, et al. Towards accurate differential diagnosis with large language models. arXiv preprint arXiv:231200164. 2023.

Huang Y, Li W, Macheret F, Gabriel RA, Ohno-Machado L. A tutorial on calibration measurements and calibration models for clinical prediction models. J Am Med Inform Assoc. 2020;27(4):621-33.

Liévin V, Hother CE, Motzfeldt AG, Winther O. Can large language models reason about medical questions? Patterns (N Y). 2024;5(3):100943.

Savage T, Wang J, Gallo R, Boukil A, Patel V, Safavi-Naini SAA, et al. Large language model uncertainty proxies: discrimination and calibration for medical diagnosis and treatment. J Am Med Inform Assoc. 2025;32(1):139-49.

Alam L, Mueller S. Examining the effect of explanation on satisfaction and trust in AI diagnostic systems. BMC Med Inform Decis Mak. 2021;21(1):178.

Taylor RA, Sangal RB, Smith ME, Haimovich AD, Rodman A, Iscoe MS, et al. Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions. Acad Emerg Med. 2025;32(3):327-39.

Kose E, Ozturk N, Karahan SR. Artificial intelligence in surgery. Eur Arch Med Res. 2018;34(Suppl 1):4-6.

Panesar S, Cagle Y, Chander D, Morey J, Fernandez-Miranda J, Kliot M. Artificial Intelligence and the Future of Surgical Robotics. Ann Surg. 2019;270(2):223-6.

Rahmani E, Bayat Z, Farrokhi M, Karimian S, Zahedpasha R, Sabzehie H, et al. Monkeypox: A Comprehensive Review of Virology, Epidemiology, Transmission, Diagnosis, Prevention, Treatment, and Artificial Intelligence Applications. Arch Acad Emerg Med. 2024;12(1):e70.

Kaan HL, Ho KY. Robot-Assisted Endoscopic Resection: Current Status and Future Directions. Gut Liver. 2020;14(2):150-2.

Lin Y-P, Lin C-C, editors. The application of artificial intelligence technology in the diagnosis of acute pancreatitis. 2019 Prognostics and System Health Management Conference (PHM-Paris); 2019: IEEE.

Bertsimas D, Dunn J, Velmahos GC, Kaafarani HMA. Surgical Risk Is Not Linear: Derivation and Validation of a Novel, User-friendly, and Machine-learning-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator. Ann Surg. 2018;268(4):574-83.

Bihorac A, Ozrazgat-Baslanti T, Ebadi A, Motaei A, Madkour M, Pardalos PM, et al. MySurgeryRisk: Development and Validation of a Machine-learning Risk Algorithm for Major Complications and Death After Surgery. Ann Surg. 2019;269(4):652-62.

Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17(1):195.

Rimmer L, Howard C, Picca L, Bashir M. The automaton as a surgeon: the future of artificial intelligence in emergency and general surgery. Eur J Trauma Emerg Surg. 2021;47(3):757-62.

Malhotra A, Molloy EJ, Bearer CF, Mulkey SB. Emerging role of artificial intelligence, big data analysis and precision medicine in pediatrics. Pediatr Res. 2023;93(2):281-3.

Harrou F, Dairi A, Kadri F, Sun Y. Forecasting emergency department overcrowding: A deep learning framework. Chaos Solitons Fractals. 2020;139:110247.

Lee YC, Ng CJ, Hsu CC, Cheng CW, Chen SY. Machine learning models for predicting unscheduled return visits to an emergency department: a scoping review. BMC Emerg Med. 2024;24(1):20.

Akbasli IT, Birbilen AZ, Teksam O. Artificial intelligence-driven forecasting and shift optimization for pediatric emergency department crowding. JAMIA Open. 2025;8(2):ooae138.

Sahni NR, Carrus B. Artificial Intelligence in U.S. Health Care Delivery. N Engl J Med. 2023;389(4):348-58.

Moylan A, Maconochie I. Demand, overcrowding and the pediatric emergency department. Cmaj. 2019;191(23):E625-e6.

Doan Q, Wong H, Meckler G, Johnson D, Stang A, Dixon A, et al. The impact of pediatric emergency department crowding on patient and health care system outcomes: a multicentre cohort study. Cmaj. 2019;191(23):E627-e35.

Weiss SJ, Derlet R, Arndahl J, Ernst AA, Richards J, Fernández-Frackelton M, et al. Estimating the degree of emergency department overcrowding in academic medical centers: results of the National ED Overcrowding Study (NEDOCS). Acad Emerg Med. 2004;11(1):38-50.

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Ali S, Manaloor R, Ma K, Sivakumar M, Vandermeer B, Beran T, et al. LO63: Humanoid robot-based distraction to reduce pain and distress during venipuncture in the pediatric emergency department: A randomized controlled trial. CJEM. 2019;21(S1):S30-S1.

Trost MJ, Ford AR, Kysh L, Gold JI, Matarić M. Socially Assistive Robots for Helping Pediatric Distress and Pain: A Review of Current Evidence and Recommendations for Future Research and Practice. Clin J Pain. 2019;35(5):451-8.

Pongsakonpruttikul N, Angthong C, Kittichai V, Chuwongin S, Puengpipattrakul P, Thongpat P, et al. Artificial intelligence assistance in radiographic detection and classification of knee osteoarthritis and its severity: a cross-sectional diagnostic study. Eur Rev Med Pharmacol Sci. 2022;26(5):1549-58.

Twinprai N, Boonrod A, Boonrod A, Chindaprasirt J, Sirithanaphol W, Chindaprasirt P, et al. Artificial intelligence (AI) vs. human in hip fracture detection. Heliyon. 2022;8(11):e11266.

van Breugel JMM, Niemeyer MJS, Houwert RM, Groenwold RHH, Leenen LPH, van Wessem KJP. Global changes in mortality rates in polytrauma patients admitted to the ICU-a systematic review. World J Emerg Surg. 2020;15(1):55.

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Kawai Y, Yamamoto K, Miyazaki K, Asai H, Fukushima H. Explainable Prediction Model of the Need for Emergency Hemostasis Using Field Information During Physician-Staffed Helicopter Emergency Medical Service Interventions: A Single-Center, Retrospective, Observational Pilot Study. Air Med J. 2023;42(5):336-42.

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Shoghli A, Darvish M, Sadeghian Y. Balancing Innovation and Privacy: Ethical Challenges in AI-Driven Healthcare. Journal of Reviews in Medical Sciences. 2024;4(1):1-11.

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