cirpublications.com-S3nP-1790899042-e431890076 1790899042 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Health Informatics and Digital Systems J. Health Inform. Digit. Syst. 3149-8973 2021 1 1 Michael Turner Sophia Nguyen David Clark Emma Wilson 2021 10.68159/e431890076 https://cirpublications.com/pub/journal/2/article/e431890076 Rotmensch M, Halpern Y, Tlimat A, Horng S, Sontag D. Learning a health knowledge graph from electronic medical records. Sci Rep. 2017;7(1):5994. 10.1038/s41598-017-05778-z Xu J, Kim S, Song M, Jeong M, Kim D, Kang J, et al. Building a PubMed knowledge graph. Sci Data. 2020;7(1):205. 10.1038/s41597-020-0543-2 Li L, Wang P, Yan J, Wang Y, Li S, Jiang J, et al. Real-world data medical knowledge graph: construction and applications. Artif Intell Med. 2020;103:101817. 10.1016/j.artmed.2020.101817 Ibrahim ZM, Tsamados A, Navab N, Fellner DW, Procter R, Demartini G. A knowledge distillation ensemble framework for predicting short- and long-term hospitalization outcomes from electronic health records data. IEEE J Biomed Health Inform. 2021;25(12):4235-46. 10.1109/JBHI.2021.3089364 Markus AF, Kors JA, Rijnbeek PR. The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies. J Biomed Inform. 2021;113:103655. 10.1016/j.jbi.2020.103655 Richardson JP, Smith C, Curtis S, Watson S, Zhu X, Barry B, et al. Patient apprehensions about the use of artificial intelligence in healthcare. NPJ Digit Med. 2021;4(1):140. 10.1038/s41746-021-00509-1 Bates DW, Levine D, Syrowatka A, Kuznetsova M, Craig KJT, Rui A, et al. The potential of artificial intelligence to improve patient safety: a scoping review. NPJ Digit Med. 2021;4(1):54. 10.1038/s41746-021-00423-6 Kashyap S, Morse RM, Patel B, Kim R. A survey of extant organizational and computational setups for deploying predictive models in health systems. J Am Med Inform Assoc. 2021;28(11):2445-50. Panayides AS, Amini A, Filipovic ND, Sharma A, Tsaftaris SA, Young A, et al. AI in medical imaging informatics: current challenges and future directions. IEEE J Biomed Health Inform. 2020;24(7):1837-57. 10.1109/JBHI.2020.2991043 Yoon J, Drumright LN, van der Schaar M. Anonymization through data synthesis using generative adversarial networks (ADS-GAN). IEEE J Biomed Health Inform. 2020;24(8):2378-88. 10.1109/JBHI.2020.2967842 Panetta K, Gindi S, Raja A, Berkley N, Panetta J, Do Q, et al. Tufts dental database: a multimodal panoramic X-ray dataset for benchmarking diagnostic systems. IEEE J Biomed Health Inform. 2022;26(4):1650-9. 10.1109/JBHI.2021.3116808 Bhowal P, Sen S, Sarkar R, Yoon JH. Choquet integral and coalition game-based ensemble of deep learning models for COVID-19 screening from chest X-ray images. IEEE J Biomed Health Inform. 2021;25(12):4328-39. 10.1109/JBHI.2021.3113536 Lee S, Ha E, Rees P, Hogg P, Lee H. Toward a fully automated artificial pancreas system using a bioinspired reinforcement learning design: in silico validation. IEEE J Biomed Health Inform. 2021;25(2):536-46. 10.1109/JBHI.2020.3002012 Thakur A, Parameswaran L, Dua K, Dinsmore J, Flook R. Dynamic neural graphs based federated reptile for semi-supervised multi-tasking in healthcare applications. IEEE J Biomed Health Inform. 2022;26(4):1761-71. 10.1109/JBHI.2021.3129547 Mirzazadeh A, Mohseni A, Ibrahim S, Giuste F, Zhu Y, Shehata B, et al. Improving heart transplant rejection classification training using progressive generative adversarial networks. Proc IEEE BHI. 2021:1-4. 10.1109/BHI50953.2021.9508532 Howell RS, Liu HH, Khan AA, Woods JS, Lin L, Saxena M, et al. Development of a method for clinical evaluation of artificial intelligence–based digital wound assessment tools. JAMA Netw Open. 2021;4(5):e217234. 10.1001/jamanetworkopen.2021.7234 Homayounieh F, Digumarthy S, Ebrahimian S, Rueckel J, Hoppe BF, Sabel BO, et al. An artificial intelligence–based chest X-ray model on human nodule detection accuracy from a multicenter study. JAMA Netw Open. 2021;4(12):e2141096. 10.1001/jamanetworkopen.2021.41096 Phillips M, Marsden H, Jaffe W, Matin RN, Wali A, Greenhalgh J, et al. Assessment of accuracy of an artificial intelligence algorithm to detect melanoma in images of skin lesions. JAMA Netw Open. 2019;2(10):e1913436. 10.1001/jamanetworkopen.2019.13436 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. 10.1001/jamanetworkopen.2020.22779 Schaffter T, Buist DSM, Lee CI, Nikulin Y, Ribli D, Guan Y, et al. Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms. JAMA Netw Open. 2020;3(3):e200265. 10.1001/jamanetworkopen.2020.0265 Kanagasingam Y, Xiao D, Vignarajan J, Preetham A, Tay-Kearney M, Mehrotra A. Evaluation of artificial intelligence–based grading of diabetic retinopathy in primary care. JAMA Netw Open. 2018;1(5):e182665. 10.1001/jamanetworkopen.2018.2665 Jayakumar P, Moore MG, Furlough KA, Uhler LM, Andrawis JP, Koenig KM, et al. Comparison of an artificial intelligence–enabled patient decision aid vs educational material on decision quality, shared decision-making, patient experience, and functional outcomes. JAMA Netw Open. 2021;4(2):e2037107. 10.1001/jamanetworkopen.2020.37107 Chi EA, Chi G, Tsui CT, Jiang Y, Jarr K, Kulkarni CV, et al. Development and validation of an artificial intelligence system to optimize clinician review of patient records. JAMA Netw Open. 2021;4(7):e2117391. 10.1001/jamanetworkopen.2021.17391 Jain A, Way D, Gupta V, Gao Y, de Oliveira Marinho G, Hartford J, et al. Development and assessment of an artificial intelligence–based tool for skin condition diagnosis. JAMA Netw Open. 2021;4(4):e217249. 10.1001/jamanetworkopen.2021.7249 Ipp E, Liljenquist D, Bode B, Shah VN, Silverstein S, Regillo CD, et al. Pivotal evaluation of an artificial intelligence system for autonomous detection of diabetic retinopathy. JAMA Netw Open. 2021;4(11):e2134254. 10.1001/jamanetworkopen.2021.34254 Nasir-Moin M, Suriawinata AA, Ren B, Liu X, Robertson DJ, Bagchi S, et al. Evaluation of an artificial intelligence–augmented digital system for histologic classification of colorectal polyps. JAMA Netw Open. 2021;4(11):e2135271. 10.1001/jamanetworkopen.2021.35271 Shamai G, Binenbaum Y, Slossberg R, Duek I, Gil Z, Kimmel R. Artificial intelligence algorithms to assess hormonal status from tissue microarrays in patients with breast cancer. JAMA Netw Open. 2019;2(7):e197700. 10.1001/jamanetworkopen.2019.7700 Bitterman DS, Aerts HJWL, Mak RH. Approaching autonomy in medical artificial intelligence. Lancet Digit Health. 2020;2(9):e447-e449. 10.1016/S2589-7500(20)30187-4 DeCamp M, Lindvall C. Why we cannot trust artificial intelligence in medicine. Lancet Digit Health. 2019;1(8):e390. 10.1016/S2589-7500(19)30197-9