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For: Miller DD, Brown EW. Artificial Intelligence in Medical Practice: The Question to the Answer? Am J Med 2018;131:129-33. [PMID: 29126825 DOI: 10.1016/j.amjmed.2017.10.035] [Cited by in Crossref: 187] [Cited by in F6Publishing: 116] [Article Influence: 37.4] [Reference Citation Analysis]
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5 Soejima H, Matsumoto K, Nakashima N, Nohara Y, Yamashita T, Machida J, Nakaguma H. A functional learning health system in Japan: Experience with processes and information infrastructure toward continuous health improvement. Learn Health Syst 2021;5:e10252. [PMID: 34667875 DOI: 10.1002/lrh2.10252] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Choi YI, Chung JW, Kim KO, Kwon KA, Kim YJ, Park DK, Ahn SM, Park SH, Sym SJ, Shin DB, Kim YS, Sung KH, Baek JH, Lee U. Concordance Rate between Clinicians and Watson for Oncology among Patients with Advanced Gastric Cancer: Early, Real-World Experience in Korea. Can J Gastroenterol Hepatol 2019;2019:8072928. [PMID: 30854352 DOI: 10.1155/2019/8072928] [Cited by in Crossref: 16] [Cited by in F6Publishing: 14] [Article Influence: 5.3] [Reference Citation Analysis]
7 Rodríguez-González A, Zanin M, Menasalvas-Ruiz E. Public Health and Epidemiology Informatics: Can Artificial Intelligence Help Future Global Challenges? An Overview of Antimicrobial Resistance and Impact of Climate Change in Disease Epidemiology. Yearb Med Inform 2019;28:224-31. [PMID: 31419836 DOI: 10.1055/s-0039-1677910] [Cited by in Crossref: 8] [Cited by in F6Publishing: 3] [Article Influence: 2.7] [Reference Citation Analysis]
8 Gruson D, Helleputte T, Rousseau P, Gruson D. Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation. Clin Biochem 2019;69:1-7. [PMID: 31022391 DOI: 10.1016/j.clinbiochem.2019.04.013] [Cited by in Crossref: 31] [Cited by in F6Publishing: 19] [Article Influence: 10.3] [Reference Citation Analysis]
9 Siegersma KR, Leiner T, Chew DP, Appelman Y, Hofstra L, Verjans JW. Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist. Neth Heart J 2019;27:403-13. [PMID: 31399886 DOI: 10.1007/s12471-019-01311-1] [Cited by in Crossref: 24] [Cited by in F6Publishing: 17] [Article Influence: 8.0] [Reference Citation Analysis]
10 Wang L, Zhang Y, Wang D, Tong X, Liu T, Zhang S, Huang J, Zhang L, Chen L, Fan H, Clarke M. Artificial Intelligence for COVID-19: A Systematic Review. Front Med (Lausanne) 2021;8:704256. [PMID: 34660623 DOI: 10.3389/fmed.2021.704256] [Reference Citation Analysis]
11 Pellegrino E, Jacques C, Beaufils N, Nanni I, Carlioz A, Metellus P, Ouafik L. Machine learning random forest for predicting oncosomatic variant NGS analysis. Sci Rep 2021;11:21820. [PMID: 34750410 DOI: 10.1038/s41598-021-01253-y] [Reference Citation Analysis]
12 Lalehzarian SP, Gowd AK, Liu JN. Machine learning in orthopaedic surgery. World J Orthop 2021; 12(9): 685-699 [PMID: 34631452 DOI: 10.5312/wjo.v12.i9.685] [Reference Citation Analysis]
13 Shimada K, Mitchison TJ. Unsupervised identification of disease states from high-dimensional physiological and histopathological profiles. Mol Syst Biol 2019;15:e8636. [PMID: 30782979 DOI: 10.15252/msb.20188636] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
14 Lin HL, Wu DC, Cheng SM, Chen CJ, Wang MC, Cheng CA. Association between Electronic Medical Records and Healthcare Quality. Medicine (Baltimore) 2020;99:e21182. [PMID: 32756096 DOI: 10.1097/MD.0000000000021182] [Cited by in Crossref: 5] [Article Influence: 2.5] [Reference Citation Analysis]
15 Miller DD. The Strength of a New Signal. Can J Cardiol 2021;37:1691-4. [PMID: 34715282 DOI: 10.1016/j.cjca.2021.09.001] [Reference Citation Analysis]
16 Heisinger S, Hitzl W, Hobusch GM, Windhager R, Cotofana S. Predicting Total Knee Replacement from Symptomology and Radiographic Structural Change Using Artificial Neural Networks-Data from the Osteoarthritis Initiative (OAI). J Clin Med 2020;9:E1298. [PMID: 32369985 DOI: 10.3390/jcm9051298] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
17 Miller DD. Machine Intelligence in Cardiovascular Medicine. Cardiology in Review 2020;28:53-64. [DOI: 10.1097/crd.0000000000000294] [Cited by in Crossref: 7] [Cited by in F6Publishing: 1] [Article Influence: 3.5] [Reference Citation Analysis]
18 Gupta AK, Hall DC. Diagnosing onychomycosis: A step forward? J Cosmet Dermatol 2021. [PMID: 34918448 DOI: 10.1111/jocd.14681] [Reference Citation Analysis]
19 Li Q, Li JF, Mao XR. Application of artificial intelligence in liver diseases: From diagnosis to treatment. Artif Intell Gastroenterol 2021; 2(5): 133-140 [DOI: 10.35712/aig.v2.i5.133] [Reference Citation Analysis]
20 Dekkers T, Hertroijs DFL. Tailored Healthcare: Two Perspectives on the Development and Use of Patient Profiles. Adv Ther 2018;35:1453-9. [DOI: 10.1007/s12325-018-0765-2] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
21 Byun H, Yu S, Oh J, Bae J, Yoon MS, Lee SH, Chung JH, Kim TH. An Assistive Role of a Machine Learning Network in Diagnosis of Middle Ear Diseases. J Clin Med 2021;10:3198. [PMID: 34361982 DOI: 10.3390/jcm10153198] [Reference Citation Analysis]
22 Yadav AK, Verma D, Kumar A, Kumar P, Solanki PR. The perspectives of biomarker-based electrochemical immunosensors, artificial intelligence and the Internet of Medical Things toward COVID-19 diagnosis and management. Mater Today Chem 2021;20:100443. [PMID: 33615086 DOI: 10.1016/j.mtchem.2021.100443] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
23 Gómez Rivas J, Toribio Vázquez C, Ballesteros Ruiz C, Taratkin M, Marenco JL, Cacciamani GE, Checcucci E, Okhunov Z, Enikeev D, Esperto F, Grossmann R, Somani B, Veneziano D. Artificial intelligence and simulation in urology. Actas Urol Esp 2021:S0210-4806(21)00088-7. [PMID: 34127285 DOI: 10.1016/j.acuro.2020.10.012] [Reference Citation Analysis]
24 van Hoek J, Huber A, Leichtle A, Härmä K, Hilt D, von Tengg-kobligk H, Heverhagen J, Poellinger A. A survey on the future of radiology among radiologists, medical students and surgeons: Students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over. European Journal of Radiology 2019;121:108742. [DOI: 10.1016/j.ejrad.2019.108742] [Cited by in Crossref: 20] [Cited by in F6Publishing: 16] [Article Influence: 6.7] [Reference Citation Analysis]
25 Miller JB. Big data and biomedical informatics: Preparing for the modernization of clinical neuropsychology. Clin Neuropsychol 2019;33:287-304. [PMID: 30513257 DOI: 10.1080/13854046.2018.1523466] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 2.5] [Reference Citation Analysis]
26 Harada T, Shimizu T, Kaji Y, Suyama Y, Matsumoto T, Kosaka C, Shimizu H, Nei T, Watanuki S. A Perspective from a Case Conference on Comparing the Diagnostic Process: Human Diagnostic Thinking vs. Artificial Intelligence (AI) Decision Support Tools. Int J Environ Res Public Health 2020;17:E6110. [PMID: 32842581 DOI: 10.3390/ijerph17176110] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
27 Pealing L, Tempest HV, Howick J, Dambha-Miller H. Technology: a help or hindrance to empathic healthcare? J R Soc Med 2018;111:390-3. [PMID: 30175938 DOI: 10.1177/0141076818790669] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 0.8] [Reference Citation Analysis]
28 Schneider P, Walters WP, Plowright AT, Sieroka N, Listgarten J, Goodnow RA, Fisher J, Jansen JM, Duca JS, Rush TS, Zentgraf M, Hill JE, Krutoholow E, Kohler M, Blaney J, Funatsu K, Luebkemann C, Schneider G. Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov 2020;19:353-64. [DOI: 10.1038/s41573-019-0050-3] [Cited by in Crossref: 114] [Cited by in F6Publishing: 92] [Article Influence: 38.0] [Reference Citation Analysis]
29 Noorbakhsh-Sabet N, Zand R, Zhang Y, Abedi V. Artificial Intelligence Transforms the Future of Health Care. Am J Med 2019;132:795-801. [PMID: 30710543 DOI: 10.1016/j.amjmed.2019.01.017] [Cited by in Crossref: 74] [Cited by in F6Publishing: 44] [Article Influence: 24.7] [Reference Citation Analysis]
30 Chen PS, Li YP, Ni HF. Morphology and Evaluation of Renal Fibrosis. Adv Exp Med Biol 2019;1165:17-36. [PMID: 31399959 DOI: 10.1007/978-981-13-8871-2_2] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 1.7] [Reference Citation Analysis]
31 Miller DD. The Big Health Data–Intelligent Machine Paradox. The American Journal of Medicine 2018;131:1272-5. [DOI: 10.1016/j.amjmed.2018.05.038] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
32 Leiner T, Rueckert D, Suinesiaputra A, Baeßler B, Nezafat R, Išgum I, Young AA. Machine learning in cardiovascular magnetic resonance: basic concepts and applications. J Cardiovasc Magn Reson 2019;21:61. [PMID: 31590664 DOI: 10.1186/s12968-019-0575-y] [Cited by in Crossref: 41] [Cited by in F6Publishing: 30] [Article Influence: 13.7] [Reference Citation Analysis]
33 Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. Artificial Intelligence in Healthcare. Elsevier; 2020. pp. 25-60. [DOI: 10.1016/b978-0-12-818438-7.00002-2] [Cited by in Crossref: 24] [Cited by in F6Publishing: 1] [Article Influence: 12.0] [Reference Citation Analysis]
34 Thompson-Brazill KA, Tierney CC, Brien L, Wininger JW, Williams JB. Enhancing Family-Centered Care in Cardiothoracic Surgery. Crit Care Nurs Clin North Am 2020;32:295-311. [PMID: 32402323 DOI: 10.1016/j.cnc.2020.02.010] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
35 Andersson J, Nyholm T, Ceberg C, Almén A, Bernhardt P, Fransson A, Olsson LE. Artificial intelligence and the medical physics profession - A Swedish perspective. Phys Med 2021;88:218-25. [PMID: 34304045 DOI: 10.1016/j.ejmp.2021.07.009] [Reference Citation Analysis]
36 Ishii N, Mochizuki Y, Shiomi K, Nakazato M, Mochizuki H. Spiral drawing: Quantitative analysis and artificial-intelligence-based diagnosis using a smartphone. J Neurol Sci 2020;411:116723. [PMID: 32050132 DOI: 10.1016/j.jns.2020.116723] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
37 Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim HC, Jeste DV. Artificial Intelligence for Mental Health and Mental Illnesses: an Overview. Curr Psychiatry Rep 2019;21:116. [PMID: 31701320 DOI: 10.1007/s11920-019-1094-0] [Cited by in Crossref: 48] [Cited by in F6Publishing: 29] [Article Influence: 16.0] [Reference Citation Analysis]
38 Safavi KC, Driscoll W, Wiener-Kronish JP. Remote Surveillance Technologies: Realizing the Aim of Right Patient, Right Data, Right Time. Anesth Analg 2019;129:726-34. [PMID: 31425213 DOI: 10.1213/ANE.0000000000003948] [Cited by in Crossref: 11] [Cited by in F6Publishing: 4] [Article Influence: 5.5] [Reference Citation Analysis]
39 Kriegmair MC, Hein S, Schoeb DS, Zappe H, Suárez-Ibarrola R, Waldbillig F, Gruene B, Pohlmann PF, Praus F, Wilhelm K, Gratzke C, Miernik A, Bolenz C. [Enhanced imaging in urological endoscopy]. Urologe A 2021;60:8-18. [PMID: 33301070 DOI: 10.1007/s00120-020-01400-9] [Reference Citation Analysis]
40 Mao C, Yang X, Zhu C, Xu J, Yu Y, Shen X, Huang Y. Concordance Between Watson for Oncology and Multidisciplinary Teams in Colorectal Cancer: Prognostic Implications and Predicting Concordance. Front Oncol 2020;10:595565. [PMID: 33425748 DOI: 10.3389/fonc.2020.595565] [Reference Citation Analysis]
41 Garland J, Ondruschka B, Stables S, Morrow P, Kesha K, Glenn C, Tse R. Identifying Fatal Head Injuries on Postmortem Computed Tomography Using Convolutional Neural Network/Deep Learning: A Feasibility Study. J Forensic Sci 2020;65:2019-22. [PMID: 32639630 DOI: 10.1111/1556-4029.14502] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
42 Maitín AM, García-tejedor AJ, Muñoz JPR. Machine Learning Approaches for Detecting Parkinson’s Disease from EEG Analysis: A Systematic Review. Applied Sciences 2020;10:8662. [DOI: 10.3390/app10238662] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 2.5] [Reference Citation Analysis]
43 Aydemir D, Ulusu NN. Correspondence: Importance of the validated serum biochemistry and hemogram parameters for rapid diagnosis and to prevent false negative results during COVID-19 pandemic. Biotechnol Appl Biochem 2021;68:390-1. [PMID: 32362005 DOI: 10.1002/bab.1936] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
44 Asay BC, Edwards BB, Andrews J, Ramey ME, Richard JD, Podell BK, Gutiérrez JFM, Frank CB, Magunda F, Robertson GT, Lyons M, Ben-Hur A, Lenaerts AJ. Digital Image Analysis of Heterogeneous Tuberculosis Pulmonary Pathology in Non-Clinical Animal Models using Deep Convolutional Neural Networks. Sci Rep 2020;10:6047. [PMID: 32269234 DOI: 10.1038/s41598-020-62960-6] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
45 Peng F, Zheng T, Tang X, Liu Q, Sun Z, Feng Z, Zhao H, Gong L. Magnetic Resonance Texture Analysis in Myocardial Infarction. Front Cardiovasc Med 2021;8:724271. [PMID: 34778395 DOI: 10.3389/fcvm.2021.724271] [Reference Citation Analysis]
46 Shen J, Zhang CJP, Jiang B, Chen J, Song J, Liu Z, He Z, Wong SY, Fang PH, Ming WK. Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review. JMIR Med Inform 2019;7:e10010. [PMID: 31420959 DOI: 10.2196/10010] [Cited by in Crossref: 46] [Cited by in F6Publishing: 29] [Article Influence: 15.3] [Reference Citation Analysis]
47 Srinivasa Rao ASR, Diamond MP. Deep Learning of Markov Model-Based Machines for Determination of Better Treatment Option Decisions for Infertile Women. Reprod Sci 2020;27:763-70. [PMID: 31939200 DOI: 10.1007/s43032-019-00082-9] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
48 Balagurunathan Y, Mitchell R, El Naqa I. Requirements and reliability of AI in the medical context. Phys Med 2021;83:72-8. [PMID: 33721700 DOI: 10.1016/j.ejmp.2021.02.024] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
49 Cabitza F, Locoro A, Banfi G. Machine Learning in Orthopedics: A Literature Review. Front Bioeng Biotechnol 2018;6:75. [PMID: 29998104 DOI: 10.3389/fbioe.2018.00075] [Cited by in Crossref: 54] [Cited by in F6Publishing: 41] [Article Influence: 13.5] [Reference Citation Analysis]
50 D’hotman D, Loh E, Savulescu J. AI-enabled suicide prediction tools: ethical considerations for medical leaders. leader 2021;5:102-7. [DOI: 10.1136/leader-2020-000275] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
51 Rodrigues-jr JF, Gutierrez MA, Spadon G, Brandoli B, Amer-yahia S. LIG-Doctor: Efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks. Information Sciences 2021;545:813-27. [DOI: 10.1016/j.ins.2020.09.024] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
52 Cui M, Zhang DY. Artificial intelligence and computational pathology. Lab Invest 2021;101:412-22. [PMID: 33454724 DOI: 10.1038/s41374-020-00514-0] [Cited by in Crossref: 12] [Cited by in F6Publishing: 7] [Article Influence: 12.0] [Reference Citation Analysis]
53 Zorc JJ, Chamberlain JM, Bajaj L. Machine Learning at the Clinical Bedside—The Ghost in the Machine. JAMA Pediatr 2019;173:622. [DOI: 10.1001/jamapediatrics.2019.1075] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
54 Bertsimas D, Masiakos PT, Mylonas KS, Wiberg H. Prediction of cervical spine injury in young pediatric patients: an optimal trees artificial intelligence approach. J Pediatr Surg 2019;54:2353-7. [PMID: 30928154 DOI: 10.1016/j.jpedsurg.2019.03.007] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 1.3] [Reference Citation Analysis]
55 Esmaeilzadeh P, Mirzaei T, Dharanikota S. Patients' Perceptions Toward Human-Artificial Intelligence Interaction in Health Care: Experimental Study. J Med Internet Res 2021;23:e25856. [PMID: 34842535 DOI: 10.2196/25856] [Reference Citation Analysis]
56 Pelaccia T, Forestier G, Wemmert C. Deconstructing the diagnostic reasoning of human versus artificial intelligence. CMAJ 2019;191:E1332-5. [PMID: 31791967 DOI: 10.1503/cmaj.190506] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 3.5] [Reference Citation Analysis]
57 Jazayeri SMHM, Jamshidnezhad A. Top Mobile Applications in Pediatrics and Children's Health: Assessment and Intelligent Analysis Tools for a Systematic Investigation. Malays J Med Sci 2019;26:5-14. [PMID: 30914890 DOI: 10.21315/mjms2019.26.1.2] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
58 Pesapane F, Suter MB, Codari M, Patella F, Volonté C, Sardanelli F. Regulatory issues for artificial intelligence in radiology. Precision Medicine for Investigators, Practitioners and Providers. Elsevier; 2020. pp. 533-43. [DOI: 10.1016/b978-0-12-819178-1.00052-6] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
59 Mellerio JE. Enduring support for the case report. Br J Dermatol 2019;181:429-30. [PMID: 31475346 DOI: 10.1111/bjd.17500] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
60 Olsen TG, Jackson BH, Feeser TA, Kent MN, Moad JC, Krishnamurthy S, Lunsford DD, Soans RE. Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology. J Pathol Inform 2018;9:32. [PMID: 30294501 DOI: 10.4103/jpi.jpi_31_18] [Cited by in Crossref: 28] [Cited by in F6Publishing: 28] [Article Influence: 7.0] [Reference Citation Analysis]
61 Jenkins JF. Genomic Health Care Today and Tomorrow: Expert Perspectives. Semin Oncol Nurs 2019;35:131-43. [PMID: 30683551 DOI: 10.1016/j.soncn.2018.12.006] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
62 Walczak S. The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework: . International Journal of Computers in Clinical Practice 2018;3:31-47. [DOI: 10.4018/ijccp.2018070103] [Cited by in Crossref: 4] [Article Influence: 1.0] [Reference Citation Analysis]
63 Albahri OS, Zaidan AA, Albahri AS, Zaidan BB, Abdulkareem KH, Al-Qaysi ZT, Alamoodi AH, Aleesa AM, Chyad MA, Alesa RM, Kem LC, Lakulu MM, Ibrahim AB, Rashid NA. Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. J Infect Public Health 2020;13:1381-96. [PMID: 32646771 DOI: 10.1016/j.jiph.2020.06.028] [Cited by in Crossref: 49] [Cited by in F6Publishing: 33] [Article Influence: 24.5] [Reference Citation Analysis]
64 Leite ML, de Loiola Costa LS, Cunha VA, Kreniski V, de Oliveira Braga Filho M, da Cunha NB, Costa FF. Artificial intelligence and the future of life sciences. Drug Discov Today 2021:S1359-6446(21)00308-1. [PMID: 34245910 DOI: 10.1016/j.drudis.2021.07.002] [Reference Citation Analysis]
65 Willingham ML Jr, Spencer SYPK, Lum CA, Navarro Sanchez JM, Burnett T, Shepherd J, Cassel K. The potential of using artificial intelligence to improve skin cancer diagnoses in Hawai'i's multiethnic population. Melanoma Res 2021;31:504-14. [PMID: 34744150 DOI: 10.1097/CMR.0000000000000779] [Reference Citation Analysis]
66 Ye Y, Xiong Y, Zhou Q, Wu J, Li X, Xiao X. Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study. J Diabetes Res 2020;2020:4168340. [PMID: 32626780 DOI: 10.1155/2020/4168340] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
67 Fu S, Zarrinpar A. Recent advances in precision medicine for individualized immunosuppression. Curr Opin Organ Transplant. 2020;25:420-425. [PMID: 32520785 DOI: 10.1097/mot.0000000000000771] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
68 Lockshin MD, Crow MK, Barbhaiya M. When a Diagnosis Has No Name: Uncertainty and Opportunity. ACR Open Rheumatol 2021. [PMID: 34806330 DOI: 10.1002/acr2.11368] [Reference Citation Analysis]
69 Pesapane F, Tantrige P, Patella F, Biondetti P, Nicosia L, Ianniello A, Rossi UG, Carrafiello G, Ierardi AM. Myths and facts about artificial intelligence: why machine- and deep-learning will not replace interventional radiologists. Med Oncol 2020;37. [DOI: 10.1007/s12032-020-01368-8] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
70 Maleki F, Muthukrishnan N, Ovens K, Reinhold C, Forghani R. Machine Learning Algorithm Validation: From Essentials to Advanced Applications and Implications for Regulatory Certification and Deployment. Neuroimaging Clin N Am 2020;30:433-45. [PMID: 33038994 DOI: 10.1016/j.nic.2020.08.004] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
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