cirpublications.com-9wt2-1794468218-i924388275 1794468218 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Artificial Intelligence for Healthcare Systems J. Artif. Intell. Healthc. Syst. 3149-8981 2023 2 2 Maria Gonzalez Javier Ruiz Lucia Torres Elena Ruiz 2023 10.68159/i924388275 https://cirpublications.com/pub/journal/1/article/i924388275 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):565-574.e2. 10.1016/j.annemergmed.2017.08.026 Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Jorde M, et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 2018;1:18. 10.1038/s41746-018-0029-1 Hong WS, Haimovich AD, Taylor RA. Predicting hospital admission at emergency department triage using machine learning. PLoS One. 2018;13(7):e0201016. 10.1371/journal.pone.0201016 Goto T, Camargo CA Jr, 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. 10.1001/jamanetworkopen.2018.6937 Mehta N, Pandit A. Concurrence of big data analytics and healthcare: a systematic review. Int J Med Inform. 2018;114:57-65. 10.1016/j.ijmedinf.2018.03.013 Behera RK, Bala PK, Ray A. Cognitive chatbot for personalised medical symptom detection and human to robotic behavior alignment. J Biomed Inform. 2019;100:103314. 10.1016/j.jbi.2019.103314 Luo Y, Szolovits P, Dighe AS, Baron JM. Using machine learning to predict laboratory test results. Am J Clin Pathol. 2017;148(4):339-46. Wiens J, Saria S, Sendak M, Ghassemi M, Liu VX, Das R, et al. Do no harm: a roadmap for responsible machine learning for health care. Nat Med. 2019;25(9):1337-40. 10.1038/s41591-019-0548-6 Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-53. 10.1126/science.aax2342 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):230-43. 10.1136/svn-2017-000101 Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. 10.1038/s41591-018-0300-7 Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018;15(141):20170387. 10.1098/rsif.2017.0387 Shameer K, Johnson KW, Glicksberg BS, Dudley JT, Sengupta PP. Machine learning in cardiovascular medicine: are we there yet? Heart. 2018;104(14):1156-64. 10.1136/heartjnl-2017-311198 Norgeot B, Glicksberg BS, Butte AJ. A call for deep-learning healthcare. Nat Med. 2019;25(1):14-5. 10.1038/s41591-018-0320-3 Sendak MP, Gao M, Brajer N, Balu S. Presenting machine learning model information to clinical end users with model facts labels. NPJ Digit Med. 2020;3:41. 10.1038/s41746-020-0253-3 Ghassemi M, Naumann T, Schulam P, Beam AL, Chen IY, Ranganath R. Practical guidance on artificial intelligence for health-care data. Lancet Digit Health. 2019;1(4):e157-e159. 10.1016/S2589-7500(19)30023-6 Char DS, Shah NH, Magnus D. Implementing machine learning in health care - addressing ethical challenges. N Engl J Med. 2018;378(11):981-3. 10.1056/NEJMp1714229 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. 10.1186/s12916-019-1426-2 Liu X, Faes L, Kale AU, Challa S, Wagner SK, Fu DJ, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health. 2019;1(6):e271-e297. 10.1016/S2589-7500(19)30123-2 Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719-31. 10.1038/s41551-018-0305-z Beam AL, Drazen JM, Kohane IS, Leong TY, Manrai AK, Rubin EJ. Artificial intelligence in medicine. N Engl J Med. 2021;384(13):1229-31. 10.1056/NEJMe2101803 Vayena E, Blasimme A, Cohen IG. Machine learning in medicine: addressing ethical challenges. PLoS Med. 2018;15(11):e1002689. 10.1371/journal.pmed.1002689 Rajpurkar P, Irvin J, Ball RL, Becker JA, Marklund L, Jephson TD, et al. Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 2018;15(11):e1002686. 10.1371/journal.pmed.1002686 Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1:39. 10.1038/s41746-018-0040-6 Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8. 10.1038/nature21056 De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;24(9):1342-50. 10.1038/s41591-018-0107-6 Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019;25(1):65-9. 10.1038/s41591-018-0268-3 Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019;394(10201):861-7. 10.1016/S0140-6736(19)31721-0 McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94. 10.1038/s41586-019-1799-6 Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019;25(6):954-61. 10.1038/s41591-019-0447-x Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. 2018;2(3):158-64. 10.1038/s41551-018-0195-0 Tomašev N, Glorot X, Rae JW, Zielinski M, Askham H, Saraiva A, et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature. 2019;572(7767):116-9. 10.1038/s41586-019-1390-1