BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Chang HY, Jung CK, Woo JI, Lee S, Cho J, Kim SW, Kwak TY. Artificial Intelligence in Pathology. J Pathol Transl Med. 2019;53:1-12. [PMID: 30599506 DOI: 10.4132/jptm.2018.12.16] [Cited by in Crossref: 67] [Cited by in F6Publishing: 47] [Article Influence: 16.8] [Reference Citation Analysis]
Number Citing Articles
1 Nawab K, Athwani R, Naeem A, Hamayun M, Wazir M. A Review of Applications of Artificial Intelligence in Gastroenterology. Cureus 2021;13:e19235. [PMID: 34877212 DOI: 10.7759/cureus.19235] [Reference Citation Analysis]
2 Li Y, Li C, Li X, Wang K, Rahaman MM, Sun C, Chen H, Wu X, Zhang H, Wang Q. A Comprehensive Review of Markov Random Field and Conditional Random Field Approaches in Pathology Image Analysis. Arch Computat Methods Eng 2022;29:609-39. [DOI: 10.1007/s11831-021-09591-w] [Reference Citation Analysis]
3 Cho KO, Lee SH, Jang HJ. Feasibility of fully automated classification of whole slide images based on deep learning. Korean J Physiol Pharmacol. 2020;24:89-99. [PMID: 31908578 DOI: 10.4196/kjpp.2020.24.1.89] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
4 Lee SH, Song IH, Jang HJ. Feasibility of deep learning-based fully automated classification of microsatellite instability in tissue slides of colorectal cancer. Int J Cancer 2021;149:728-40. [PMID: 33851412 DOI: 10.1002/ijc.33599] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
5 Janssen BV, Tutucu F, van Roessel S, Adsay V, Basturk O, Campbell F, Doglioni C, Esposito I, Feakins R, Fukushima N, Gill AJ, Hruban RH, Kaplan J, Koerkamp BG, Hong SM, Krasinskas A, Luchini C, Offerhaus J, Sarasqueta AF, Shi C, Singhi A, Stoop TF, Soer EC, Thompson E, van Tienhoven G, Velthuysen MF, Wilmink JW, Besselink MG, Brosens LAA, Wang H, Verbeke CS, Verheij J; International Study Group of Pancreatic Pathologists (ISGPP). Amsterdam International Consensus Meeting: tumor response scoring in the pathology assessment of resected pancreatic cancer after neoadjuvant therapy. Mod Pathol 2021;34:4-12. [PMID: 33041332 DOI: 10.1038/s41379-020-00683-9] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
6 Teschke R, Danan G. Idiosyncratic Drug-Induced Liver Injury (DILI) and Herb-Induced Liver Injury (HILI): Diagnostic Algorithm Based on the Quantitative Roussel Uclaf Causality Assessment Method (RUCAM). Diagnostics (Basel) 2021;11:458. [PMID: 33800917 DOI: 10.3390/diagnostics11030458] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
7 Trachtman AR, Bergamini L, Palazzi A, Porrello A, Capobianco Dondona A, Del Negro E, Paolini A, Vignola G, Calderara S, Marruchella G. Scoring pleurisy in slaughtered pigs using convolutional neural networks. Vet Res 2020;51:51. [PMID: 32276670 DOI: 10.1186/s13567-020-00775-z] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
8 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]
9 Yao Y, Gou S, Tian R, Zhang X, He S. Automated Classification and Segmentation in Colorectal Images Based on Self-Paced Transfer Network. Biomed Res Int. 2021;2021:6683931. [PMID: 33542924 DOI: 10.1155/2021/6683931] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Acs B, Rantalainen M, Hartman J. Artificial intelligence as the next step towards precision pathology. J Intern Med. 2020;288:62-81. [PMID: 32128929 DOI: 10.1111/joim.13030] [Cited by in Crossref: 46] [Cited by in F6Publishing: 44] [Article Influence: 23.0] [Reference Citation Analysis]
11 Moran-Sanchez J, Santisteban-Espejo A, Martin-Piedra MA, Perez-Requena J, Garcia-Rojo M. Translational Applications of Artificial Intelligence and Machine Learning for Diagnostic Pathology in Lymphoid Neoplasms: A Comprehensive and Evolutive Analysis. Biomolecules 2021;11:793. [PMID: 34070632 DOI: 10.3390/biom11060793] [Reference Citation Analysis]
12 Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17:195. [PMID: 31665002 DOI: 10.1186/s12916-019-1426-2] [Cited by in Crossref: 209] [Cited by in F6Publishing: 159] [Article Influence: 69.7] [Reference Citation Analysis]
13 Rahman A, Jahangir C, Lynch SM, Alattar N, Aura C, Russell N, Lanigan F, Gallagher WM. Advances in tissue-based imaging: impact on oncology research and clinical practice. Expert Rev Mol Diagn 2020;20:1027-37. [PMID: 32510287 DOI: 10.1080/14737159.2020.1770599] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Kubach J, Muhlebner-Fahrngruber A, Soylemezoglu F, Miyata H, Niehusmann P, Honavar M, Rogerio F, Kim SH, Aronica E, Garbelli R, Vilz S, Popp A, Walcher S, Neuner C, Scholz M, Kuerten S, Schropp V, Roeder S, Eichhorn P, Eckstein M, Brehmer A, Kobow K, Coras R, Blumcke I, Jabari S. Same same but different: A Web-based deep learning application revealed classifying features for the histopathologic distinction of cortical malformations. Epilepsia 2020;61:421-32. [PMID: 32080846 DOI: 10.1111/epi.16447] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
15 Watson NF, Fernandez CR. Artificial intelligence and sleep: Advancing sleep medicine. Sleep Med Rev 2021;59:101512. [PMID: 34166990 DOI: 10.1016/j.smrv.2021.101512] [Reference Citation Analysis]
16 Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet 2020;395:1579-86. [PMID: 32416782 DOI: 10.1016/S0140-6736(20)30226-9] [Cited by in Crossref: 43] [Cited by in F6Publishing: 18] [Article Influence: 21.5] [Reference Citation Analysis]
17 Deshmukh F, Merchant SS. Explainable Machine Learning Model for Predicting GI Bleed Mortality in the Intensive Care Unit. Am J Gastroenterol 2020;115:1657-68. [DOI: 10.14309/ajg.0000000000000632] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 3.5] [Reference Citation Analysis]
18 Schüffler PJ, Geneslaw L, Yarlagadda DVK, Hanna MG, Samboy J, Stamelos E, Vanderbilt C, Philip J, Jean MH, Corsale L, Manzo A, Paramasivam NHG, Ziegler JS, Gao J, Perin JC, Kim YS, Bhanot UK, Roehrl MHA, Ardon O, Chiang S, Giri DD, Sigel CS, Tan LK, Murray M, Virgo C, England C, Yagi Y, Sirintrapun SJ, Klimstra D, Hameed M, Reuter VE, Fuchs TJ. Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center. J Am Med Inform Assoc 2021;28:1874-84. [PMID: 34260720 DOI: 10.1093/jamia/ocab085] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
19 Lennartz S, Dratsch T, Zopfs D, Persigehl T, Maintz D, Große Hokamp N, Pinto Dos Santos D. Use and Control of Artificial Intelligence in Patients Across the Medical Workflow: Single-Center Questionnaire Study of Patient Perspectives. J Med Internet Res 2021;23:e24221. [PMID: 33595451 DOI: 10.2196/24221] [Reference Citation Analysis]
20 Dimitriou N, Arandjelović O, Caie PD. Deep Learning for Whole Slide Image Analysis: An Overview. Front Med (Lausanne) 2019;6:264. [PMID: 31824952 DOI: 10.3389/fmed.2019.00264] [Cited by in Crossref: 50] [Cited by in F6Publishing: 37] [Article Influence: 16.7] [Reference Citation Analysis]
21 Bédard A, Westerling-Bui T, Zuraw A. Proof of Concept for a Deep Learning Algorithm for Identification and Quantification of Key Microscopic Features in the Murine Model of DSS-Induced Colitis. Toxicol Pathol 2021;49:897-904. [PMID: 33576323 DOI: 10.1177/0192623320987804] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Tayebi RM, Mu Y, Dehkharghanian T, Ross C, Sur M, Foley R, Tizhoosh HR, Campbell CJV. Automated bone marrow cytology using deep learning to generate a histogram of cell types. Commun Med 2022;2. [DOI: 10.1038/s43856-022-00107-6] [Reference Citation Analysis]
23 Kim H, Yoon H, Thakur N, Hwang G, Lee EJ, Kim C, Chong Y. Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain. Sci Rep 2021;11:22520. [PMID: 34795365 DOI: 10.1038/s41598-021-01905-z] [Reference Citation Analysis]
24 Bisdas S, Topriceanu CC, Zakrzewska Z, Irimia AV, Shakallis L, Subhash J, Casapu MM, Leon-Rojas J, Pinto Dos Santos D, Andrews DM, Zeicu C, Bouhuwaish AM, Lestari AN, Abu-Ismail L, Sadiq AS, Khamees A, Mohammed KMG, Williams E, Omran AI, Ismail DYA, Ebrahim EH. Artificial Intelligence in Medicine: A Multinational Multi-Center Survey on the Medical and Dental Students' Perception. Front Public Health 2021;9:795284. [PMID: 35004598 DOI: 10.3389/fpubh.2021.795284] [Reference Citation Analysis]
25 Sharedalal P, Singh A, Shah N, Jain D. Automated abstraction of myocardial perfusion imaging reports using natural language processing. J Nucl Cardiol 2021. [PMID: 33474697 DOI: 10.1007/s12350-020-02507-4] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
26 Li X, Li C, Rahaman MM, Sun H, Li X, Wu J, Yao Y, Grzegorzek M. A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches. Artif Intell Rev. [DOI: 10.1007/s10462-021-10121-0] [Reference Citation Analysis]
27 Basu S, Agarwal R, Srivastava V. Deep discriminative learning model with calibrated attention map for the automated diagnosis of diffuse large B-cell lymphoma. Biomedical Signal Processing and Control 2022;76:103728. [DOI: 10.1016/j.bspc.2022.103728] [Reference Citation Analysis]
28 Wang W, Zhao W, Xu H, Liu S, Huang W, Zhao Q. Fabrication of ultra-thin 2D covalent organic framework nanosheets and their application in functional electronic devices. Coordination Chemistry Reviews 2021;429:213616. [DOI: 10.1016/j.ccr.2020.213616] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 7.0] [Reference Citation Analysis]
29 Zhao S, Li F, Guo X, Guo T, Mizutani KI, Yamada S, Gu C, Uramoto H. New additional scoring formula on the Pathological Features in Stage I Lung Adenocarcinoma Patients: Impact on Survival. Int J Med Sci 2020;17:1871-8. [PMID: 32788866 DOI: 10.7150/ijms.45002] [Reference Citation Analysis]
30 Sultan AS, Elgharib MA, Tavares T, Jessri M, Basile JR. The use of artificial intelligence, machine learning and deep learning in oncologic histopathology. J Oral Pathol Med 2020;49:849-56. [PMID: 32449232 DOI: 10.1111/jop.13042] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
31 Thakur N, Yoon H, Chong Y. Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review. Cancers (Basel). 2020;12. [PMID: 32668721 DOI: 10.3390/cancers12071884] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 5.5] [Reference Citation Analysis]
32 Pallua JD, Brunner A, Zelger B, Schirmer M, Haybaeck J. The future of pathology is digital. Pathol Res Pract 2020;216:153040. [PMID: 32825928 DOI: 10.1016/j.prp.2020.153040] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
33 Jang HJ, Lee A, Kang J, Song IH, Lee SH. Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning. World J Gastroenterol 2020; 26(40): 6207-6223 [PMID: 33177794 DOI: 10.3748/wjg.v26.i40.6207] [Cited by in CrossRef: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
34 Zhao Y, McCracken J, Wang E. Yeast-like organisms phagocytosed by circulating neutrophils: Evidence of disseminated histoplasmosis. Int J Lab Hematol 2021. [PMID: 34464006 DOI: 10.1111/ijlh.13693] [Reference Citation Analysis]
35 Zuraw A, Aeffner F. Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review. Vet Pathol 2021;:3009858211040484. [PMID: 34521285 DOI: 10.1177/03009858211040484] [Reference Citation Analysis]
36 Xie X, Wang X, Liang Y, Yang J, Wu Y, Li L, Sun X, Bing P, He B, Tian G, Shi X. Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review. Front Oncol 2021;11:763527. [PMID: 34900711 DOI: 10.3389/fonc.2021.763527] [Reference Citation Analysis]
37 Krishna AB, Tanveer A, Bhagirath PV, Gannepalli A. Role of artificial intelligence in diagnostic oral pathology-A modern approach. J Oral Maxillofac Pathol 2020;24:152-6. [PMID: 32508465 DOI: 10.4103/jomfp.JOMFP_215_19] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
38 Kurc T, Bakas S, Ren X, Bagari A, Momeni A, Huang Y, Zhang L, Kumar A, Thibault M, Qi Q, Wang Q, Kori A, Gevaert O, Zhang Y, Shen D, Khened M, Ding X, Krishnamurthi G, Kalpathy-Cramer J, Davis J, Zhao T, Gupta R, Saltz J, Farahani K. Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches. Front Neurosci 2020;14:27. [PMID: 32153349 DOI: 10.3389/fnins.2020.00027] [Cited by in Crossref: 20] [Cited by in F6Publishing: 9] [Article Influence: 10.0] [Reference Citation Analysis]
39 Vali-Betts E, Krause KJ, Dubrovsky A, Olson K, Graff JP, Mitra A, Datta-Mitra A, Beck K, Tsirigos A, Loomis C, Neto AG, Adler E, Rashidi HH. Effects of Image Quantity and Image Source Variation on Machine Learning Histology Differential Diagnosis Models. J Pathol Inform 2021;12:5. [PMID: 34012709 DOI: 10.4103/jpi.jpi_69_20] [Reference Citation Analysis]
40 Alqudah A, Alqudah AM. Sliding window based deep ensemble system for breast cancer classification. J Med Eng Technol 2021;45:313-23. [PMID: 33769183 DOI: 10.1080/03091902.2021.1896814] [Reference Citation Analysis]
41 Jang H, Song IH, Lee SH. Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers. Applied Sciences 2021;11:808. [DOI: 10.3390/app11020808] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
42 Kanavati F, Toyokawa G, Momosaki S, Rambeau M, Kozuma Y, Shoji F, Yamazaki K, Takeo S, Iizuka O, Tsuneki M. Weakly-supervised learning for lung carcinoma classification using deep learning. Sci Rep. 2020;10:9297. [PMID: 32518413 DOI: 10.1038/s41598-020-66333-x] [Cited by in Crossref: 19] [Cited by in F6Publishing: 15] [Article Influence: 9.5] [Reference Citation Analysis]
43 Li Z, Goebel S, Reimann A, Ungerer M. Histo-ELISA technique for quantification and localization of tissue components. Sci Rep 2020;10:19849. [PMID: 33199754 DOI: 10.1038/s41598-020-76950-1] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
44 Parwani AV, Amin MB. Convergence of Digital Pathology and Artificial Intelligence Tools in Anatomic Pathology Practice: Current Landscape and Future Directions. Adv Anat Pathol 2020;27:221-6. [PMID: 32541593 DOI: 10.1097/PAP.0000000000000271] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
45 Wang Y, Coudray N, Zhao Y, Li F, Hu C, Zhang YZ, Imoto S, Tsirigos A, Webb GI, Daly RJ, Song J. HEAL: an automated deep learning framework for cancer histopathology image analysis. Bioinformatics 2021:btab380. [PMID: 34009289 DOI: 10.1093/bioinformatics/btab380] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
46 Tizhoosh HR, Diamandis P, Campbell CJV, Safarpoor A, Kalra S, Maleki D, Riasatian A, Babaie M. Searching Images for Consensus: Can AI Remove Observer Variability in Pathology? Am J Pathol 2021:S0002-9440(21)00072-9. [PMID: 33636179 DOI: 10.1016/j.ajpath.2021.01.015] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
47 Nam S, Chong Y, Jung CK, Kwak TY, Lee JY, Park J, Rho MJ, Go H. Introduction to digital pathology and computer-aided pathology. J Pathol Transl Med 2020;54:125-34. [PMID: 32045965 DOI: 10.4132/jptm.2019.12.31] [Cited by in Crossref: 16] [Cited by in F6Publishing: 15] [Article Influence: 8.0] [Reference Citation Analysis]
48 Shavlokhova V, Sandhu S, Flechtenmacher C, Koveshazi I, Neumeier F, Padrón-Laso V, Jonke Ž, Saravi B, Vollmer M, Vollmer A, Hoffmann J, Engel M, Ristow O, Freudlsperger C. Deep Learning on Oral Squamous Cell Carcinoma Ex Vivo Fluorescent Confocal Microscopy Data: A Feasibility Study. J Clin Med 2021;10:5326. [PMID: 34830608 DOI: 10.3390/jcm10225326] [Reference Citation Analysis]
49 Royston D, Mead AJ, Psaila B. Application of Single-Cell Approaches to Study Myeloproliferative Neoplasm Biology. Hematol Oncol Clin North Am 2021;35:279-93. [PMID: 33641869 DOI: 10.1016/j.hoc.2021.01.002] [Reference Citation Analysis]
50 Kang Y, Kim YJ, Park S, Ro G, Hong C, Jang H, Cho S, Hong WJ, Kang DU, Chun J, Lee K, Kang GH, Moon KC, Choe G, Lee KS, Park JH, Jeong WK, Chun SY, Park P, Choi J. Development and operation of a digital platform for sharing pathology image data. BMC Med Inform Decis Mak 2021;21:114. [PMID: 33812383 DOI: 10.1186/s12911-021-01466-1] [Reference Citation Analysis]
51 Sieviläinen M, Wirsing AM, Hyytiäinen A, Almahmoudi R, Rodrigues P, Bjerkli IH, Åström P, Toppila-Salmi S, Paavonen T, Coletta RD, Hadler-Olsen E, Salo T, Al-Samadi A. Evaluation Challenges in the Validation of B7-H3 as Oral Tongue Cancer Prognosticator. Head Neck Pathol 2021;15:469-78. [PMID: 32959211 DOI: 10.1007/s12105-020-01222-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
52 Shimizu H, Nakayama KI. Artificial intelligence in oncology. Cancer Sci. 2020;111:1452-1460. [PMID: 32133724 DOI: 10.1111/cas.14377] [Cited by in Crossref: 24] [Cited by in F6Publishing: 20] [Article Influence: 12.0] [Reference Citation Analysis]
53 Elkhader J, Elemento O. Artificial intelligence in oncology: From bench to clinic. Semin Cancer Biol 2021:S1044-579X(21)00114-0. [PMID: 33915289 DOI: 10.1016/j.semcancer.2021.04.013] [Reference Citation Analysis]
54 Saini G, Joshi S, Garlapati C, Li H, Kong J, Krishnamurthy J, Reid MD, Aneja R. Polyploid giant cancer cell characterization: New frontiers in predicting response to chemotherapy in breast cancer. Semin Cancer Biol 2021:S1044-579X(21)00067-5. [PMID: 33766651 DOI: 10.1016/j.semcancer.2021.03.017] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
55 Lopez-jimenez F, Attia Z, Arruda-olson AM, Carter R, Chareonthaitawee P, Jouni H, Kapa S, Lerman A, Luong C, Medina-inojosa JR, Noseworthy PA, Pellikka PA, Redfield MM, Roger VL, Sandhu GS, Senecal C, Friedman PA. Artificial Intelligence in Cardiology: Present and Future. Mayo Clinic Proceedings 2020;95:1015-39. [DOI: 10.1016/j.mayocp.2020.01.038] [Cited by in Crossref: 28] [Cited by in F6Publishing: 23] [Article Influence: 14.0] [Reference Citation Analysis]
56 Dudgeon SN, Wen S, Hanna MG, Gupta R, Amgad M, Sheth M, Marble H, Huang R, Herrmann MD, Szu CH, Tong D, Werness B, Szu E, Larsimont D, Madabhushi A, Hytopoulos E, Chen W, Singh R, Hart SN, Sharma A, Saltz J, Salgado R, Gallas BD. A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study. J Pathol Inform 2021;12:45. [PMID: 34881099 DOI: 10.4103/jpi.jpi_83_20] [Reference Citation Analysis]
57 Jang HJ, Cho KO. Applications of deep learning for the analysis of medical data. Arch Pharm Res. 2019;42:492-504. [PMID: 31140082 DOI: 10.1007/s12272-019-01162-9] [Cited by in Crossref: 20] [Cited by in F6Publishing: 14] [Article Influence: 6.7] [Reference Citation Analysis]
58 Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford). 2020;2020. [PMID: 32185396 DOI: 10.1093/database/baaa010] [Cited by in Crossref: 53] [Cited by in F6Publishing: 32] [Article Influence: 53.0] [Reference Citation Analysis]