Haleem, 2019, Artificial Intelligence (AI) applications in orthopaedics: an innovative technology to embrace, J Clin Orthop Trauma, 10.1016/j.jcot.2019.06.012
Jha, 2018, Information and artificial intelligence, J Am Coll Radiol, 15, 509, 10.1016/j.jacr.2017.12.025
Lupton, 2018, Some ethical and legal consequences of the application of artificial intelligence in the field of medicine, Trends Med, 18, 1, 10.15761/TiM.1000147
Murdoch, 2013, The inevitable application of big data to health care, J Am Med Assoc, 309, 1351, 10.1001/jama.2013.393
Misawa, 2018, Artificial intelligence-assisted polyp detection for colonoscopy initial experience, Gastroenterology, 154, 2027, 10.1053/j.gastro.2018.04.003
Caocci, 2010, Comparison between an artificial neural network and logistic regression in predicting acute graft-vs-host disease after unrelated donor hematopoietic stem cell transplantation in thalassemia patients, Exp Hematol, 38, 426, 10.1016/j.exphem.2010.02.012
Haleem, 2019, Industry 4.0 and its applications in orthopaedics, J Clin Orthop Trauma, 10, 615, 10.1016/j.jcot.2018.09.015
Guo, 2018, The application of medical artificial intelligence technology in rural areas of developing countries, Health Equity, 2, 174, 10.1089/heq.2018.0037
Atasoy, 2018, The digitization of patient care: a review of the effects of electronic health records on health care quality and utilization, Annu Rev Public Health, 40, 10.1146/annurev-publhealth-040218-044206
Jiang, 2017, Artificial intelligence in healthcare: past, present and future, Stroke Vasc Neurol, 2, 230, 10.1136/svn-2017-000101
Haleem, 2019, Industry 5.0 and its expected applications in medical field, Curr Med Res Pract, 9, 167, 10.1016/j.cmrp.2019.07.002
BuchVH, 2018, Artificial intelligence in medicine: current trends and future possibilities, Br J Gen Pract, 68, 143, 10.3399/bjgp18X695213
Kulikowski, 2019, Beginnings of artificial intelligence in medicine (AIM): computational artifice assisting scientific inquiry and clinical art - with reflections on present AIM challenges, Yearb Med Inform
Upadhyay, 2019, Artificial intelligence-based training learning from application, Dev Learn Org Int J, 33, 20, 10.1108/DLO-05-2018-0058
Hashmi, 2015, ‘Coming of Age’ of artificial intelligence: evolution of survivorship care through information technology, Bone Marrow Transplant, 51, 41, 10.1038/bmt.2015.271
Mintz, 2019, Introduction to artificial intelligence in medicine, Minim Invasive Ther Allied Technol, 28, 73, 10.1080/13645706.2019.1575882
Javaid, 2019, Industry 4.0 applications in medical field: a brief review, Curr Med Res Pract, 9, 102, 10.1016/j.cmrp.2019.04.001
Lee, 2017, Deep into the brain: artificial Intelligence in stroke imaging, J Stroke, 19, 277
Muhsen, 2018, Artificial intelligence approaches in hematopoietic cell transplantation: a review of the current status and future directions, Turk J Haematol, 35, 152
Miller, 2018, Artificial intelligence in medical practice: the question to the answer?, Am J Med, 131, 129, 10.1016/j.amjmed.2017.10.035
Xu, 2019, Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives, Hum Genet, 138, 109, 10.1007/s00439-019-01970-5
Kinnings, 2011, A machine learning-based method to improve docking scoring functions and its application to drug repurposing, J Chem Inf Model, 51, 408, 10.1021/ci100369f
Varnek, 2012, Machine learning methods for property prediction in chemoinformatics: quo Vadis?, J Chem Inf Model, 52, 1413, 10.1021/ci200409x
Ain, 2015, Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening, Wiley Interdiscip Rev Comput Mol Sci, 5, 405, 10.1002/wcms.1225
Erickson, 2017, Machine learning for medical imaging, RadioGraphics, 37, 505, 10.1148/rg.2017160130
Zeng, 2017, Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection, Health Inf Sci Syst, 5, 2, 10.1007/s13755-017-0023-z
Li, 2018, Modelling online user behaviour for medical knowledge learning, Ind Manag Data Syst, 118, 889, 10.1108/IMDS-07-2017-0309
Wesolowski, 2012, Artificial neural networks: theoretical background and pharmaceutical applications: a review, J AOAC Int, 95, 652
Saravanan, 2014, Review on classification based on artificial neural networks, Int. J Ambient Syst Appl (IJASA), 2, 11
Pastur-Romay, 2016, Deep artificial neural networks and neuromorphic chips for big data analysis: pharmaceutical and bioinformatics applications, Int J Mol Sci, 17, 1313, 10.3390/ijms17081313
Li, 2017, Application of artificial neural networks for catalysis: a review, Catalysts, 7, 306, 10.3390/catal7100306
Abiodun, 2018, State-of-the-art in artificial neural network applications: a survey, Heliyon, 23, 10.1016/j.heliyon.2018.e00938
Shahid, 2019, Applications of artificial neural networks in health care organizational decision-making: a scoping review, PLoS One, 14, 10.1371/journal.pone.0212356
Dutta, 2013, Automated detection using natural language processing of radiologists' recommendations for additional imaging of incidental findings, Ann Emerg Med, 62, 162, 10.1016/j.annemergmed.2013.02.001
Heintzelman, 2013, Longitudinal analysis of pain in patients with metastatic prostate cancer using natural language processing of medical record text, J Am Med Inform Assoc, 20, 898, 10.1136/amiajnl-2012-001076
Cai, 2016, natural language processing technologies in radiology research and clinical applications, RadioGraphics, 36, 176, 10.1148/rg.2016150080
Savova, 2017, DeepPhe: a natural language processing system for extracting cancer phenotypes from clinical records, Cancer Res, 77, e115, 10.1158/0008-5472.CAN-17-0615
Verma, 2012, A Support Vector Machine based method to distinguish proteobacterial proteins from eukaryotic plant proteins, BMC Bioinf, 13
Zhu, 2014, Support vector machine model for diagnosing pneumoconiosis based on wavelet texture features of digital chest radiographs, J Digit Imaging, 27, 90, 10.1007/s10278-013-9620-9
Gu, 2014, New fuzzy support vector machine for the class imbalance problem in medical datasets classification, Sci World J
Retico, 2015, Predictive models based on support vector machines: whole-brain versus regional analysis of structural MRI in the alzheimer's disease, J Neuroimaging, 25, 552, 10.1111/jon.12163
Wang, 2017, Support vector machines model of computed Tomography for assessing lymph node metastasis in esophageal cancer with neoadjuvant chemotherapy, J Comput Assist Tomogr, 41, 455, 10.1097/RCT.0000000000000555
Davies, 2015, After the Liverpool Care Pathway--development of heuristics to guide end of life care for people with dementia: protocol of the ALCP study, BMJ Open, 5, 10.1136/bmjopen-2015-008832
Davies, 2016, A co-design process developing heuristics for practitioners providing end of life care for people with dementia, BMC Palliat Care, 15, 68, 10.1186/s12904-016-0146-z
Mohan, 2016, Testing a videogame intervention to recalibrate physician heuristics in trauma triage: study protocol for a randomized controlled trial, BMC Emerg Med, 16, 44, 10.1186/s12873-016-0108-z
Davies, 2018, Guiding practitioners through the end of life care for people with dementia: the use of heuristics, PLoS One, 13, 10.1371/journal.pone.0206422
Patel, 2009, The coming of age of artificial intelligence in medicine, Artif Intell Med, 46, 5, 10.1016/j.artmed.2008.07.017
Wahl, 2018, Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings?, BMJ Glob Health, 3, 10.1136/bmjgh-2018-000798
Bashiri, 2017, Improving the prediction of survival in cancer patients by using machine learning techniques: experience of gene expression data: a narrative review, Iran J Public Health, 46, 165
Long, 2017, An artificial intelligence platform for the multihospital collaborative management of congenital cataracts, Nat Biomed Eng, 1, 24, 10.1038/s41551-016-0024
Yi Paul, 2018, Artificial intelligence and radiology: collaboration is key, J Am Coll Radiol, 15, 781, 10.1016/j.jacr.2017.12.037
Winter, 2019
Hai, 2012, Image classification using support vector machine and artificial neural network, Int J Inf Technol Comput Sci, 4, 32
Balkanyi, 2019, The interplay of knowledge representation with various fields of artificial intelligence in medicine, Yearb Med Inform
Ghahramani, 2015, Probabilistic machine learning and artificial intelligence, Nature, 521, 452, 10.1038/nature14541
Weidlich, 2018, Artificial intelligence in medicine and radiation oncology, Cureus, 10, e2475
Kantarjian, 2015, Artificial intelligence, big data, and cancer, JAMA Oncol, 1, 573, 10.1001/jamaoncol.2015.1203
Wartman, 2018, Medical education must move from the information age to the age of artificial intelligence, Acad Med, 93, 1107, 10.1097/ACM.0000000000002044
Krittanawong, 2017, Artificial intelligence in precision cardiovascular medicine, J Am Coll Cardiol, 69, 2657, 10.1016/j.jacc.2017.03.571
Hamet, 2017, Artificial intelligence in medicine, Metabolism, 69, 36, 10.1016/j.metabol.2017.01.011
Sachs, 2013, Imaging study protocol selection in the electronic medical record, J Am Coll Radiol, 10, 220, 10.1016/j.jacr.2012.11.004
Luxton, 2014, Recommendations for the ethical use and design of artificial intelligent care providers, Artif Intell Med, 62, 1, 10.1016/j.artmed.2014.06.004
Jha, 2016, Adapting to artificial intelligence: radiologists and pathologists as information specialists, J Am Med Assoc, 316, 2353, 10.1001/jama.2016.17438
Noorbakhsh-Sabet, 2019, Artificial intelligence transforms the future of health care, Am J Med, S0002–9343, 30120
Tran, 2019, Global evolution of research in artificial intelligence in health and medicine: a bibliometric study, J Clin Med, 8, 360
Chouard, 2015, Machine intelligence, Nature, 521, 435, 10.1038/521435a
Scherer, 2016, Regulating artificial intelligence systems: risks, challenges, competencies, and strategies, HarvJL Tech, 29, 354
Patel, 2007, Applications of artificial neural networks in medical science, Curr Clin Pharmacol, 2, 217, 10.2174/157488407781668811
Fernandez-Luque, 2018, Humanitarian health computing using artificial intelligence and social media: a narrative literature review, Int J Med Inform, 114, 136, 10.1016/j.ijmedinf.2018.01.015
Ramesh, 2004, Artificial intelligence in medicine, Ann R Coll Surg Engl, 86, 334, 10.1308/147870804290
He, 2019, The practical implementation of artificial intelligence technologies in medicine, Nat Med, 25, 30, 10.1038/s41591-018-0307-0
Yu, 2019, Framing the challenges of artificial intelligence in medicine, BMJ Qual Saf, 28, 238, 10.1136/bmjqs-2018-008551
Dilsizian, 2014, Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment, Curr Cardiol Rep, 16, 441, 10.1007/s11886-013-0441-8
Pesapane, 2018, Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States, Insights Imag, 9, 745, 10.1007/s13244-018-0645-y
Buzeav, 2016, Artificial intelligence: neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes, Chronic Dis Transl Med, 2, 166, 10.1016/j.cdtm.2016.09.007
Shaban-Nejad, 2018, How artificial intelligence transforms population and personalized health, NPJ Digit Med, 1, 53, 10.1038/s41746-018-0058-9
Vuong, 2019, Artificial intelligence vs natural stupidity: evaluating AI readiness for the Vietnamese medical information system, J Clin Med, 8
Douali, 2014, Diagnosis support system based on clinical guidelines: comparison between case-based fuzzy cognitive maps and Bayesian networks, Comput Methods Progr Biomed, 113, 133, 10.1016/j.cmpb.2013.09.012
Mayo, 2018, Artificial intelligence and deep learning – radiology's next frontier?, Clin Imaging, 49, 87, 10.1016/j.clinimag.2017.11.007
Shortliffe, 2019, Artificial intelligence in medicine: weighing the accomplishments, hype, and promise, Yearb Med Inform
Pomprapa, 2015, Artificial intelligence for closed-loop ventilation therapy with hemodynamic control using the open lung concept, Int J Intell Comput Cybern, 8, 50, 10.1108/IJICC-05-2014-0025
Komorowski, 2018, The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care, Nat Med, 24, 1716, 10.1038/s41591-018-0213-5
Tien, 2017, A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modelling at a tropical area, Agric For Meteorol, 233, 32, 10.1016/j.agrformet.2016.11.002
Vellido, 2019, Societal issues concerning the application of artificial intelligence in medicine, Kidney Dis (Basel), 5, 11, 10.1159/000492428
Topol, 2019, High-performance medicine: the convergence of human and artificial intelligence, Nat Med, 25, 44, 10.1038/s41591-018-0300-7
Bur, 2019, Artificial intelligence for the otolaryngologist: a state of the art review, Otolaryngol Head Neck Surg, 160, 603, 10.1177/0194599819827507
Haleem, 2019, Additive manufacturing applications in industry 4.0: a review, J Ind Integrat Manag, 4
Van Hartskamp, 2019, Artificial intelligence in clinical health care applications: viewpoint, Interact J Med Res, 8, 10.2196/12100
Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 115, 10.1038/nature21056
Wang, 2019, Artificial intelligence in reproductive medicine, Reproduction, 10.1530/REP-18-0523
Hashimoto, 2018, Artificial intelligence in surgery: promises and perils, Ann Surg, 268, 70, 10.1097/SLA.0000000000002693
Hosny, 2018, Artificial intelligence in radiology, Nat Rev Cancer, 18, 500, 10.1038/s41568-018-0016-5