Archives of Academic Emergency Medicine

Wilson T. No longer science fiction, AI and robotics are transforming healthcare. PWC Accessed October. 2017;31:2021.

Langlotz CP. Will artificial intelligence replace radiologists? : Radiological Society of North America; 2019. p. e190058.

Shuaib A, Arian H, Shuaib A. The Increasing Role of Artificial Intelligence in Health Care: Will Robots Replace Doctors in the Future? Int J Gen Med. 2020; 13:891-6.

Chen M, Decary M. Artificial intelligence in healthcare: An essential guide for health leaders. Healthc Manage Forum. 2020;33(1):10-8.

Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4): e000101-115

Bitterman DS, Aerts HJ, Mak RH. Approaching autonomy in medical artificial intelligence. Lancet Digit Health. 2020;2(9):e447-e9.

Bin KJ, Melo AAR, da Rocha JGMF, de Almeida RP, Cobello Junior V, Maia FL, et al. The Impact of Artificial Intelligence on Waiting Time for Medical Care in an Urgent Care Service for COVID-19: Single-Center Prospective Study. JMIR Form Res. 2022;6(2):e29012-e.

Nyce A, Gandhi S, Freeze B, Bosire J, Ricca T, Kupersmith E, et al. Association of Emergency Department Waiting Times With Patient Experience in Admitted and Discharged Patients. J Patient Exp. 2021;8:23743735211011404-.

Kang D-Y, Cho K-J, Kwon O, Kwon J-m, Jeon K-H, Park H, et al. Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services. Scand J Trauma Resusc Emerg Med. 2020;28(1):1-8.

Li X, Tian D, Li W, Hu Y, Dong B, Wang H, et al. Using artificial intelligence to reduce queuing time and improve satisfaction in pediatric outpatient service: A randomized clinical trial. Front Pediatr. 2022;10: 929834

Lorenzi NM, Riley RT, Blyth AJ, Southon G, Dixon BJ. Antecedents of the people and organizational aspects of medical informatics: review of the literature. J Am Med Inform Assoc. 1997;4(2):79-93.

Kukafka R, Johnson SB, Linfante A, Allegrante JP. Grounding a new information technology implementation framework in behavioral science: a systematic analysis of the literature on IT use. J Biomed Inform. 2003;36(3):218-27.

Gooding P, Kariotis T. Ethics and law in research on algorithmic and data-driven technology in mental health care: scoping review. JMIR Ment Health. 2021;8(6):e24668.

Čartolovni A, Tomičić A, Mosler EL. Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review. Int J Healthc Inform Res. 2022;161:104738.

Murphy K, Di Ruggiero E, Upshur R, Willison DJ, Malhotra N, Cai JC, et al. Artificial intelligence for good health: a scoping review of the ethics literature. BMC Med Ethics. 2021;22(1):1-17.

Siala H, Wang Y. SHIFTing artificial intelligence to be responsible in healthcare: A systematic review. Soc Sci Med. 2022:114782.

Yokoi R, Eguchi Y, Fujita T, Nakayachi K. Artificial intelligence is trusted less than a doctor in medical treatment decisions: Influence of perceived care and value similarity. Int J Hum Comput Interact. 2021;37(10):981-90.

Choudhury A, Asan O. Role of artificial intelligence in patient safety outcomes: systematic literature review. JMIR Med Inform. 2020;8(7):e18599.

Yin J, Ngiam KY, Teo HH. Role of artificial intelligence applications in real-life clinical practice: systematic review. J Med Internet Res. 2021;23(4):e25759.

Trocin C, Mikalef P, Papamitsiou Z, Conboy K. Responsible AI for digital health: a synthesis and a research agenda. Inf Syst Front. 2021:1-19.

Wolff J, Pauling J, Keck A, Baumbach J. The economic impact of artificial intelligence in health care: systematic review. J Med Internet Res. 2020;22(2):e16866.

Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak. 2021;21:1-23.

de Hond AA, Leeuwenberg AM, Hooft L, Kant IM, Nijman SW, van Os HJ, et al. Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review. NPJ Digit Med. 2022;5(1):2.

Mirbabaie M, Stieglitz S, Frick NR. Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction. Health Technol (Berl). 2021;11(4):693-731.

Zidaru T, Morrow EM, Stockley R. Ensuring patient and public involvement in the transition to AI‐assisted mental health care: A systematic scoping review and agenda for design justice.

Health Expect. 2021;24(4):1072-124.

Gama F, Tyskbo D, Nygren J, Barlow J, Reed J, Svedberg P. Implementation frameworks for artificial intelligence translation into health care practice: scoping review. J Med Internet Res. 2022;24(1):e32215.

Alhashmi SF, Alshurideh M, Al Kurdi B, Salloum SA, editors. A systematic review of the factors affecting the artificial intelligence implementation in the health care sector. Proc Int Conf Artif Intell Comput Visi (AICV2020); 2020: Springer.

Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19-32.

Wohlin C, editor Guidelines for snowballing in systematic literature studies and a replication in software engineering. Proc 18th Int Conf Eval Assess Softw Eng; 2014.

Eldawlatly A, Alshehri H, Alqahtani A, Ahmad A, Al-Dammas F, Marzouk A. Appearance of Population, Intervention, Comparison, and Outcome as research question in the title of articles of three different anesthesia journals: A pilot study. Saudi J Anaesth. 2018;12(2):283.

Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77-101.

Munn Z, Peters MD, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol. 2018;18:1-7.

Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24-9.

Mzoughi H, Njeh I, Wali A, Slima MB, BenHamida A, Mhiri C, et al. Deep multi-scale 3D convolutional neural network (CNN) for MRI gliomas brain tumor classification. J Digit Imaging. 2020;33(4):903-15.

Safaripour R, June Lim HJ. Comparative analysis of machine learning approaches for predicting frequent emergency department visits. J Health Inform. 2022;28(2):14604582221106396.

Feretzakis G, Sakagianni A, Kalles D, Loupelis E, Panteris V, Tzelves L, et al. Using Machine Learning for Predicting the Hospitalization of Emergency Department Patients. Stud Health Technol Inform. 2022;295:405-8.

Feretzakis G, Karlis G, Loupelis E, Kalles D, Chatzikyriakou R, Trakas N, et al. Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department. J Crit Care Med (Targu Mures). 2022;8(2):107-16.

Wilson MH, Habig K, Wright C, Hughes A, Davies G, Imray CH. Pre-hospital emergency medicine. Lancet. 2015;386(10012):2526-34.

Morrill J, Qirko K, Kelly J, Ambrosy A, Toro B, Smith T, et al. A Machine Learning Methodology for Identification and Triage of Heart Failure Exacerbations. J Cardiovasc Transl Res. 2022;15(1):103-15.

Blomberg SN, Folke F, Ersbøll AK, Christensen HC, Torp-Pedersen C, Sayre MR, et al. Machine learning as a supportive tool to recognize cardiac arrest in emergency calls. Resuscitation. 2019;138:322-9.

Al-Dury N, Ravn-Fischer A, Hollenberg J, Israelsson J, Nordberg P, Strömsöe A, et al. Identifying the relative importance of predictors of survival in out of hospital cardiac arrest: a machine learning study. Scand J Trauma Resusc Emerg Med. 2020;28(1):1-8.

Lin AX, Ho AFW, Cheong KH, Li Z, Cai W, Chee ML, et al. Leveraging machine learning techniques and engineering of multi-nature features for national daily regional ambulance demand prediction. Int J Environ Res Public Health. 2020;17(11):4179.

Fernandes M, Vieira SM, Leite F, Palos C, Finkelstein S, Sousa JM. Clinical decision support systems for triage in the emergency department using intelligent systems: a review. Artif Intell Med. 2020;102:101762.

Feretzakis G, Sakagianni A, Loupelis E, Kalles D, Panteris V, Tzelves L, et al. Prediction of Hospitalization Using Machine Learning for Emergency Department Patients. Stud Health Technol Inform. 2022;294:145-6.

Hwang S, Lee B. Machine learning-based prediction of critical illness in children visiting the emergency department. Plos one. 2022;17(2):e0264184.

Levin S, Toerper M, Hamrock E, Hinson JS, Barnes S, Gardner H, et al. Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index. Ann Emerg Med. 2018;71(5):565-74. e2.

Choi SW, Ko T, Hong KJ, Kim KH. Machine learning-based prediction of Korean triage and acuity scale level in emergency department patients. Healthc Inform Res. 2019;25(4):305-12.

Kwon J-m, Lee Y, Lee Y, Lee S, Park H, Park J. Validation of deep-learning-based triage and acuity score using a large national dataset. Plos one. 2018;13(10):e0205836.

Yu JY, Jeong GY, Jeong OS, Chang DK, Cha WC. Machine learning and initial nursing assessment-based triage system for emergency department. Healthc Inform Res. 2020;26(1):13-9.

Farahmand S, Shabestari O, Pakrah M, Hossein-Nejad H, Arbab M, Bagheri-Hariri S. Artificial intelligence-based triage for patients with acute abdominal pain in emergency department; a diagnostic accuracy study. Adv J Emerg Med. 2017;1(1): e5

Kim SW, Li JY, Hakendorf P, Teubner DJ, Ben‐Tovim DI, Thompson CH. Predicting admission of patients by their presentation to the emergency department. Emerg Med Australas. 2014;26(4):361-7.

Hong WS, Haimovich AD, Taylor RA. Predicting hospital admission at emergency department triage using machine learning. Plos one. 2018;13(7):e0201016.

Raita Y, Goto T, Faridi MK, Brown DF, Camargo CA, Hasegawa K. Emergency department triage prediction of clinical outcomes using machine learning models. Crit Care. 2019;23(1):1-13.

Goto T, Camargo CA, Faridi MK, Freishtat RJ, Hasegawa K. Machine learning–based prediction of clinical outcomes for children during emergency department triage. JAMA Netw Open. 2019;2(1):e186937-e.

Zhang X, Kim J, Patzer RE, Pitts SR, Patzer A, Schrager JD. Prediction of emergency department hospital admission based on natural language processing and neural networks. Methods Inf Med. 2017;56(05):377-89.

Chen C-H, Hsieh J-G, Cheng S-L, Lin Y-L, Lin P-H, Jeng J-H. Emergency department disposition prediction using a deep neural network with integrated clinical narratives and structured data. Int J Med Inform. 2020;139:104146.

Sterling NW, Patzer RE, Di M, Schrager JD. Prediction of emergency department patient disposition based on natural language processing of triage notes. Int J Med Inform. 2019;129:184-8.

Fernandes M, Mendes R, Vieira SM, Leite F, Palos C, Johnson A, et al. Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing. Plos one. 2020;15(3):e0229331.

Resnikoff S, Lansingh VC, Washburn L, Felch W, Gauthier T-M, Taylor HR, et al. Estimated number of ophthalmologists worldwide (International Council of Ophthalmology update): will we meet the needs? Br J Ophthalmol. 2020;104(4):588-92.

Vu T, Nguyen A, Brown N, Hughes J, editors. Identifying patients with pain in emergency departments using conventional machine learning and deep learning. Proc 17th Annu Australas Lang Technol Assoc; 2019: Australasian Language Technology Association (ALTA).

Pineda AL, Ye Y, Visweswaran S, Cooper GF, Wagner MM, Tsui FR. Comparison of machine learning classifiers for influenza detection from emergency department free-text reports. J Biomed Inform. 2015;58:60-9.

Sanderson M, Bulloch AG, Wang J, Williams KG, Williamson T, Patten SB. Predicting death by suicide following an emergency department visit for parasuicide with administrative health care system data and machine learning. EClinicalMedicine. 2020;20:100281.

Lindsey R, Daluiski A, Chopra S, Lachapelle A, Mozer M, Sicular S, et al. Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci. 2018;115(45):11591-6.

Olczak J, Fahlberg N, Maki A, Razavian AS, Jilert A, Stark A, et al. Artificial intelligence for analyzing orthopedic trauma radiographs: deep learning algorithms—are they on par with humans for diagnosing fractures? Acta Orthop. 2017;88(6):581-6.

Jang D-H, Kim J, Jo YH, Lee JH, Hwang JE, Park SM, et al. Developing neural network models for early detection of cardiac arrest in emergency department. Am J Emerg Med. 2020;38(1):43-9.

Kwon JM, Kim KH, Jeon KH, Lee SY, Park J, Oh BH. Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography. Scand J Trauma Resusc Emerg Med. 2020 Dec;28:1-10.

Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adam M, Gertych A, et al. A deep convolutional neural network model to classify heartbeats. Comput Biol Med. 2017;89:389-96.

Kwon Jm, Lee Y, Lee Y, Lee S, Park J. An algorithm based on deep learning for predicting in‐hospital cardiac arrest. J Am Heart Assoc. 2018;7(13):e008678.

Johnsson J, Björnsson O, Andersson P, Jakobsson A, Cronberg T, Lilja G, et al. Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care. Crit Care. 2020;24(1):1-12.

Kim J, Chang H, Kim D, Jang D-H, Park I, Kim K. Machine learning for prediction of septic shock at initial triage in emergency department. J Critical Care. 2020;55:163-70.

Perng J-W, Kao I-H, Kung C-T, Hung S-C, Lai Y-H, Su C-M. Mortality prediction of septic patients in the emergency department based on machine learning. J Clinical Med. 2019;8(11):1906.

Koh JEW, Raghavendra U, Gudigar A, Ping OC, Molinari F, Mishra S, et al. A novel hybrid approach for automated detection of retinal detachment using ultrasound images. Comput Biol Med. 2020;120:103704.

Patel SJ, Chamberlain DB, Chamberlain JM. A machine learning approach to predicting need for hospitalization for pediatric asthma exacerbation at the time of emergency department triage. Acad Emerg Med. 2018;25(12):1463-70.

Ho AFW, Zheng H, Cheong KH, En WL, Pek PP, Zhao X, et al. The relationship between air pollution and all-cause mortality in Singapore. Atmosphere (Basel). 2019;11(1):9.

Cheong KH, Ngiam NJ, Morgan GG, Pek PP, Tan BY-Q, Lai JW, et al. Acute health impacts of the Southeast Asian transboundary haze problem—A review. Int J Environ Res Public Health. 2019;16(18):3286.

Liu Q, Yang L, Peng Q. Artificial Intelligence Technology-Based Medical Information Processing and Emergency First Aid Nursing Management. Comput Math Methods Med. 2022;2022:8677118.

Wu L, Huang G, Yu X, Ye M, Liu L, Ling Y, et al. Deep Learning Networks Accurately Detect ST-Segment Elevation Myocardial Infarction and Culprit Vessel. Front Cardiovasc Med. 2022;9:797207.

Yamanaka S, Goto T, Morikawa K, Watase H, Okamoto H, Hagiwara Y, et al. Machine Learning Approaches for Predicting Difficult Airway and First-Pass Success in the Emergency Department: Multicenter Prospective Observational Study. Interact J Med Res. 2022;11(1):e28366.

Whitt W, Zhang X. Forecasting arrivals and occupancy levels in an emergency department. Oper Res Health Care. 2019;21:1-18.

Khaldi R, El Afia A, Chiheb R. Forecasting of weekly patient visits to emergency department: real case study. Procedia Comput Sci. 2019;148:532-41.

Yousefi M, Yousefi M, Fathi M, Fogliatto FS. Patient visit forecasting in an emergency department using a deep neural network approach. Kybernetes. 2019; 49(9): 2335-48

Ho AFW, To BZYS, Koh JM, Cheong KH. Forecasting hospital emergency department patient volume using internet search data. IEEE Access. 2019;7:93387-95.

Ram S, Zhang W, Williams M, Pengetnze Y. Predicting asthma-related emergency department visits using big data. IEEE J Biomed Health Inform. 2015;19(4):1216-23.

Kuo Y-H, Chan NB, Leung JM, Meng H, So AM-C, Tsoi KK, et al. An integrated approach of machine learning and systems thinking for waiting time prediction in an emergency department. Int J Med Inform. 2020;139:104143.

Greenbaum NR, Jernite Y, Halpern Y, Calder S, Nathanson LA, Sontag DA, et al. Improving documentation of presenting problems in the emergency department using a domain-specific ontology and machine learning-driven user interfaces. Int J Med Inform. 2019;132:103981.

Tootooni MS, Pasupathy KS, Heaton HA, Clements CM, Sir MY. CCMapper: An adaptive NLP-based free-text chief complaint mapping algorithm. Comput Biol Med. 2019;113:103398.

Frost DW, Vembu S, Wang J, Tu K, Morris Q, Abrams HB. Using the electronic medical record to identify patients at high risk for frequent emergency department visits and high system costs. Am J Med. 2017;130(5):601. e17-. e22.

Yu H, Yang LT, Zhang Q, Armstrong D, Deen MJ. Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives. Neurocomputing. 2021 Jul 15;444:92-110.

Convertino VA, Grudic G, Mulligan J, Moulton S. Estimation of individual-specific progression to impending cardiovascular instability using arterial waveforms. J Appl Physiol. 2013;115(8):1196-202.

Guillame-Bert M, Dubrawski A, Wang D, Hravnak M, Clermont G, Pinsky MR. Learning temporal rules to forecast instability in continuously monitored patients. J Am Med Inform Assoc. 2017;24(1):47-53.

Mao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, et al. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ open. 2018;8(1):e017833.

Kijpaisalratana N, Sanglertsinlapachai D, Techaratsami S, Musikatavorn K, Saoraya J. Machine learning algorithms for early sepsis detection in the emergency department: A retrospective study. Int J Med Inform. 2022;160:104689.

Monkaresi H, Calvo RA, Yan H. A machine learning approach to improve contactless heart rate monitoring using a webcam. IEEE J Biomed Health Inform. 2013;18(4):1153-60.

Simjanoska M, Gjoreski M, Gams M, Madevska Bogdanova A. Non-invasive blood pressure estimation from ECG using machine learning techniques. Sens. 2018;18(4):1160.

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Schroeder M, Lodemann S. A systematic investigation of the integration of machine learning into supply chain risk management. Logist. 2021 Sep 8;5(3):62.

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