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For: Abels E, Pantanowitz L, Aeffner F, Zarella MD, van der Laak J, Bui MM, Vemuri VN, Parwani AV, Gibbs J, Agosto-Arroyo E, Beck AH, Kozlowski C. Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association. J Pathol 2019;249:286-94. [PMID: 31355445 DOI: 10.1002/path.5331] [Cited by in Crossref: 68] [Cited by in F6Publishing: 59] [Article Influence: 22.7] [Reference Citation Analysis]
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2 Parwani AV. Commentary: Automated Diagnosis and Gleason Grading of Prostate Cancer - Are Artificial Intelligence Systems Ready for Prime Time? J Pathol Inform 2019;10:41. [PMID: 32089952 DOI: 10.4103/jpi.jpi_56_19] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
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5 Li J, Garfinkel J, Zhang X, Wu D, Zhang Y, de Haan K, Wang H, Liu T, Bai B, Rivenson Y, Rubinstein G, Scumpia PO, Ozcan A. Biopsy-free in vivo virtual histology of skin using deep learning. Light Sci Appl 2021;10:233. [PMID: 34795202 DOI: 10.1038/s41377-021-00674-8] [Reference Citation Analysis]
6 Rashid R, Chen YA, Hoffer J, Muhlich JL, Lin JR, Krueger R, Pfister H, Mitchell R, Santagata S, Sorger PK. Narrative online guides for the interpretation of digital-pathology images and tissue-atlas data. Nat Biomed Eng 2021. [PMID: 34750536 DOI: 10.1038/s41551-021-00789-8] [Reference Citation Analysis]
7 Jahn SW, Plass M, Moinfar F. Digital Pathology: Advantages, Limitations and Emerging Perspectives. J Clin Med 2020;9:E3697. [PMID: 33217963 DOI: 10.3390/jcm9113697] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 5.0] [Reference Citation Analysis]
8 Huss R, Coupland SE. Software‐assisted decision support in digital histopathology. J Pathol 2020;250:685-92. [DOI: 10.1002/path.5388] [Cited by in Crossref: 15] [Cited by in F6Publishing: 14] [Article Influence: 7.5] [Reference Citation Analysis]
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10 Duan K, Jang GH, Grant RC, Wilson JM, Notta F, O'Kane GM, Knox JJ, Gallinger S, Fischer S. The value of GATA6 immunohistochemistry and computer-assisted diagnosis to predict clinical outcome in advanced pancreatic cancer. Sci Rep 2021;11:14951. [PMID: 34294813 DOI: 10.1038/s41598-021-94544-3] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Carboni E, Marxfeld H, Tuoken H, Klukas C, Eggers T, Gröters S, van Ravenzwaay B. A Workflow for the Performance of the Differential Ovarian Follicle Count Using Deep Neuronal Networks. Toxicol Pathol 2021;49:843-50. [PMID: 33287654 DOI: 10.1177/0192623320969130] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
12 Barisoni L, Lafata KJ, Hewitt SM, Madabhushi A, Balis UGJ. Digital pathology and computational image analysis in nephropathology. Nat Rev Nephrol 2020;16:669-85. [PMID: 32848206 DOI: 10.1038/s41581-020-0321-6] [Cited by in Crossref: 19] [Cited by in F6Publishing: 18] [Article Influence: 9.5] [Reference Citation Analysis]
13 Meuten DJ, Moore FM, Donovan TA, Bertram CA, Klopfleisch R, Foster RA, Smedley RC, Dark MJ, Milovancev M, Stromberg P, Williams BH, Aubreville M, Avallone G, Bolfa P, Cullen J, Dennis MM, Goldschmidt M, Luong R, Miller AD, Miller MA, Munday JS, Roccabianca P, Salas EN, Schulman FY, Laufer-Amorim R, Asakawa MG, Craig L, Dervisis N, Esplin DG, George JW, Hauck M, Kagawa Y, Kiupel M, Linder K, Meichner K, Marconato L, Oblak ML, Santos RL, Simpson RM, Tvedten H, Whitley D. International Guidelines for Veterinary Tumor Pathology: A Call to Action. Vet Pathol 2021;:3009858211013712. [PMID: 34282984 DOI: 10.1177/03009858211013712] [Reference Citation Analysis]
14 Korzynska A, Roszkowiak L, Zak J, Siemion K. A review of current systems for annotation of cell and tissue images in digital pathology. Biocybernetics and Biomedical Engineering 2021;41:1436-53. [DOI: 10.1016/j.bbe.2021.04.012] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Black-Schaffer WS, Robboy SJ, Gross DJ, Crawford JM, Johnson K, Austin M, Karcher DS, Johnson RL, Powell SZ, Sanfrancesco J, Cohen MB. Evidence-Based Alignment of Pathology Residency With Practice II: Findings and Implications. Acad Pathol 2021;8:23742895211002816. [PMID: 33889716 DOI: 10.1177/23742895211002816] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
16 Rakha EA, Toss M, Shiino S, Gamble P, Jaroensri R, Mermel CH, Chen PC. Current and future applications of artificial intelligence in pathology: a clinical perspective. J Clin Pathol 2021;74:409-14. [PMID: 32763920 DOI: 10.1136/jclinpath-2020-206908] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
17 McAlpine ED, Michelow P. The cytopathologist's role in developing and evaluating artificial intelligence in cytopathology practice. Cytopathology 2020;31:385-92. [PMID: 31957101 DOI: 10.1111/cyt.12799] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
18 Dumitrascu OM, Wang Y, Chen JJ. Clinical Machine Learning Modeling Studies: Methodology and Data Reporting. J Neuroophthalmol 2022. [PMID: 35439230 DOI: 10.1097/WNO.0000000000001605] [Reference Citation Analysis]
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20 Lidbury BA, Koerbin G, Richardson AM, Badrick T. Gamma-Glutamyl Transferase (GGT) Is the Leading External Quality Assurance Predictor of ISO15189 Compliance for Pathology Laboratories. Diagnostics (Basel) 2021;11:692. [PMID: 33924582 DOI: 10.3390/diagnostics11040692] [Reference Citation Analysis]
21 Cummins DM, Chaudhry IH, Harries M. Scarring Alopecias: Pathology and an Update on Digital Developments. Biomedicines 2021;9:1755. [DOI: 10.3390/biomedicines9121755] [Reference Citation Analysis]
22 Herrmann MD, Lennerz JK. [Technical, operational, and regulatory considerations for the adoption of digital and computational pathology]. Pathologe 2020;41:103-10. [PMID: 33263808 DOI: 10.1007/s00292-020-00871-z] [Reference Citation Analysis]
23 Oliveira SP, Neto PC, Fraga J, Montezuma D, Monteiro A, Monteiro J, Ribeiro L, Gonçalves S, Pinto IM, Cardoso JS. CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance. Sci Rep 2021;11:14358. [PMID: 34257363 DOI: 10.1038/s41598-021-93746-z] [Reference Citation Analysis]
24 Pantanowitz L. Improving the Pap test with artificial intelligence. Cancer Cytopathol 2022. [PMID: 35291050 DOI: 10.1002/cncy.22561] [Reference Citation Analysis]
25 Farris AB, Moghe I, Wu S, Hogan J, Cornell LD, Alexander MP, Kers J, Demetris AJ, Levenson RM, Tomaszewski J, Barisoni L, Yagi Y, Solez K. Banff Digital Pathology Working Group: Going digital in transplant pathology. Am J Transplant 2020;20:2392-9. [PMID: 32185875 DOI: 10.1111/ajt.15850] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
26 Amgad M, Atteya L, Hussein H, Mohammed KH, Hafiz E, Elsebaie MAT, Mobadersany P, Manthey D, Gutman DA, Elfandy H, Cooper LAD. Explainable nucleus classification using Decision Tree Approximation of Learned Embeddings. Bioinformatics 2021:btab670. [PMID: 34586355 DOI: 10.1093/bioinformatics/btab670] [Reference Citation Analysis]
27 Schumacher VL, Aeffner F, Barale-Thomas E, Botteron C, Carter J, Elies L, Engelhardt JA, Fant P, Forest T, Hall P, Hildebrand D, Klopfleisch R, Lucotte T, Marxfeld H, Mckinney L, Moulin P, Neyens E, Palazzi X, Piton A, Riccardi E, Roth DR, Rousselle S, Vidal JD, Williams B. The Application, Challenges, and Advancement Toward Regulatory Acceptance of Digital Toxicologic Pathology: Results of the 7th ESTP International Expert Workshop (September 20-21, 2019). Toxicol Pathol 2021;49:720-37. [PMID: 33297858 DOI: 10.1177/0192623320975841] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
28 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]
29 Jackson BR, Ye Y, Crawford JM, Becich MJ, Roy S, Botkin JR, de Baca ME, Pantanowitz L. The Ethics of Artificial Intelligence in Pathology and Laboratory Medicine: Principles and Practice. Acad Pathol 2021;8:2374289521990784. [PMID: 33644301 DOI: 10.1177/2374289521990784] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
30 Li Q, Liu X, Han K, Guo C, Jiang J, Ji X, Wu X. Learning to autofocus in whole slide imaging via physics-guided deep cascade networks. Opt Express 2022;30:14319. [DOI: 10.1364/oe.416824] [Reference Citation Analysis]
31 Allenby MC, Woodruff MA. Image analyses for engineering advanced tissue biomanufacturing processes. Biomaterials 2022;284:121514. [DOI: 10.1016/j.biomaterials.2022.121514] [Reference Citation Analysis]
32 Homeyer A, Lotz J, Schwen LO, Weiss N, Romberg D, Höfener H, Zerbe N, Hufnagl P. Artificial Intelligence in Pathology: From Prototype to Product. J Pathol Inform 2021;12:13. [PMID: 34012717 DOI: 10.4103/jpi.jpi_84_20] [Reference Citation Analysis]
33 Ke J, Shen Y, Lu Y, Deng J, Wright JD, Zhang Y, Huang Q, Wang D, Jing N, Liang X, Jiang F. Quantitative analysis of abnormalities in gynecologic cytopathology with deep learning. Lab Invest 2021;101:513-24. [PMID: 33526806 DOI: 10.1038/s41374-021-00537-1] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
34 van der Kamp A, Waterlander TJ, de Bel T, van der Laak J, van den Heuvel-Eibrink MM, Mavinkurve-Groothuis AMC, de Krijger RR. Artificial Intelligence in Pediatric Pathology: The Extinction of a Medical Profession or the Key to a Bright Future? Pediatr Dev Pathol 2022;:10935266211059809. [PMID: 35238696 DOI: 10.1177/10935266211059809] [Reference Citation Analysis]
35 van der Laak J, Ciompi F, Litjens G. No pixel-level annotations needed. Nat Biomed Eng 2019;3:855-6. [PMID: 31624355 DOI: 10.1038/s41551-019-0472-6] [Cited by in Crossref: 4] [Article Influence: 1.3] [Reference Citation Analysis]
36 Leong TKM, Lo WS, Lee WEZ, Tan B, Lee XZ, Lee LWJN, Lee JJ, Suresh N, Loo LH, Szu E, Yeong J. Leveraging advances in immunopathology and artificial intelligence to analyze in vitro tumor models in composition and space. Adv Drug Deliv Rev 2021;177:113959. [PMID: 34481035 DOI: 10.1016/j.addr.2021.113959] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
37 Turner OC, Aeffner F, Bangari DS, High W, Knight B, Forest T, Cossic B, Himmel LE, Rudmann DG, Bawa B, Muthuswamy A, Aina OH, Edmondson EF, Saravanan C, Brown DL, Sing T, Sebastian MM. Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology. Toxicol Pathol 2020;48:277-94. [DOI: 10.1177/0192623319881401] [Cited by in Crossref: 16] [Cited by in F6Publishing: 19] [Article Influence: 5.3] [Reference Citation Analysis]
38 Saidak Z, Lailler C, Clatot F, Galmiche A. Perineural invasion in head and neck squamous cell carcinoma: background, mechanisms, and prognostic implications. Curr Opin Otolaryngol Head Neck Surg 2020;28:90-5. [PMID: 32011398 DOI: 10.1097/MOO.0000000000000610] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
39 Chong Y, Kim DC, Jung CK, Kim DC, Song SY, Joo HJ, Yi SY; Medical Informatics Study Group of the Korean Society of Pathologists. Recommendations for pathologic practice using digital pathology: consensus report of the Korean Society of Pathologists. J Pathol Transl Med 2020;54:437-52. [PMID: 33027850 DOI: 10.4132/jptm.2020.08.27] [Reference Citation Analysis]
40 Alrafiah AR. Application and performance of artificial intelligence technology in cytopathology. Acta Histochem 2022;124:151890. [PMID: 35366580 DOI: 10.1016/j.acthis.2022.151890] [Reference Citation Analysis]
41 Schwen LO, Schacherer D, Geißler C, Homeyer A. Evaluating generic AutoML tools for computational pathology. Informatics in Medicine Unlocked 2022. [DOI: 10.1016/j.imu.2022.100853] [Reference Citation Analysis]
42 Luong RH. Commentary: Digital histopathology in a private or commercial diagnostic veterinary laboratory. J Vet Diagn Invest 2020;32:353-5. [PMID: 32404028 DOI: 10.1177/1040638720919842] [Reference Citation Analysis]
43 Farris AB, Vizcarra J, Amgad M, Cooper LAD, Gutman D, Hogan J. Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples. Histopathology 2021;78:791-804. [PMID: 33211332 DOI: 10.1111/his.14304] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
44 Marble HD, Huang R, Dudgeon SN, Lowe A, Herrmann MD, Blakely S, Leavitt MO, Isaacs M, Hanna MG, Sharma A, Veetil J, Goldberg P, Schmid JH, Lasiter L, Gallas BD, Abels E, Lennerz JK. A Regulatory Science Initiative to Harmonize and Standardize Digital Pathology and Machine Learning Processes to Speed up Clinical Innovation to Patients. J Pathol Inform 2020;11:22. [PMID: 33042601 DOI: 10.4103/jpi.jpi_27_20] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
45 McAlpine ED, Michelow P, Celik T. The Utility of Unsupervised Machine Learning in Anatomic Pathology. Am J Clin Pathol 2021:aqab085. [PMID: 34302331 DOI: 10.1093/ajcp/aqab085] [Reference Citation Analysis]
46 Chen P, El Hussein S, Xing F, Aminu M, Kannapiran A, Hazle JD, Medeiros LJ, Wistuba II, Jaffray D, Khoury JD, Wu J. Chronic Lymphocytic Leukemia Progression Diagnosis with Intrinsic Cellular Patterns via Unsupervised Clustering. Cancers 2022;14:2398. [DOI: 10.3390/cancers14102398] [Reference Citation Analysis]
47 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]
48 Schlitter AM, Häberle L, Richter C, Huss R, Esposito I. [Standardized diagnosis of pancreatic head carcinoma]. Pathologe 2021;42:453-63. [PMID: 34357472 DOI: 10.1007/s00292-021-00971-4] [Reference Citation Analysis]
49 Van Herck Y, Antoranz A, Andhari MD, Milli G, Bechter O, De Smet F, Bosisio FM. Multiplexed Immunohistochemistry and Digital Pathology as the Foundation for Next-Generation Pathology in Melanoma: Methodological Comparison and Future Clinical Applications. Front Oncol 2021;11:636681. [PMID: 33854972 DOI: 10.3389/fonc.2021.636681] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
50 Ringborg U, Berns A, Celis JE, Heitor M, Tabernero J, Schüz J, Baumann M, Henrique R, Aapro M, Basu P, Beets-Tan R, Besse B, Cardoso F, Carneiro F, van den Eede G, Eggermont A, Fröhling S, Galbraith S, Garralda E, Hanahan D, Hofmarcher T, Jönsson B, Kallioniemi O, Kásler M, Kondorosi E, Korbel J, Lacombe D, Carlos Machado J, Martin-Moreno JM, Meunier F, Nagy P, Nuciforo P, Oberst S, Oliveiera J, Papatriantafyllou M, Ricciardi W, Roediger A, Ryll B, Schilsky R, Scocca G, Seruca R, Soares M, Steindorf K, Valentini V, Voest E, Weiderpass E, Wilking N, Wren A, Zitvogel L. The Porto European Cancer Research Summit 2021. Mol Oncol 2021;15:2507-43. [PMID: 34515408 DOI: 10.1002/1878-0261.13078] [Reference Citation Analysis]
51 Castiglioni I, Rundo L, Codari M, Di Leo G, Salvatore C, Interlenghi M, Gallivanone F, Cozzi A, D'Amico NC, Sardanelli F. AI applications to medical images: From machine learning to deep learning. Phys Med 2021;83:9-24. [PMID: 33662856 DOI: 10.1016/j.ejmp.2021.02.006] [Cited by in Crossref: 4] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
52 Chang MC, Mrkonjic M. Review of the current state of digital image analysis in breast pathology. Breast J 2020;26:1208-12. [PMID: 32342590 DOI: 10.1111/tbj.13858] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
53 Chauhan C, Gullapalli RR. Ethics of AI in Pathology: Current Paradigms and Emerging Issues. Am J Pathol 2021:S0002-9440(21)00303-5. [PMID: 34252382 DOI: 10.1016/j.ajpath.2021.06.011] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
54 Wallace PW, Conrad C, Brückmann S, Pang Y, Caleiras E, Murakami M, Korpershoek E, Zhuang Z, Rapizzi E, Kroiss M, Gudziol V, Timmers HJ, Mannelli M, Pietzsch J, Beuschlein F, Pacak K, Robledo M, Klink B, Peitzsch M, Gill AJ, Tischler AS, de Krijger RR, Papathomas T, Aust D, Eisenhofer G, Richter S. Metabolomics, machine learning and immunohistochemistry to predict succinate dehydrogenase mutational status in phaeochromocytomas and paragangliomas. J Pathol 2020;251:378-87. [PMID: 32462735 DOI: 10.1002/path.5472] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
55 Wilbur DC, Pettus JR, Smith ML, Cornell LD, Andryushkin A, Wingard R, Wirch E. Using Image Registration and Machine Learning to Develop a Workstation Tool for Rapid Analysis of Glomeruli in Medical Renal Biopsies. J Pathol Inform 2020;11:37. [PMID: 33343997 DOI: 10.4103/jpi.jpi_49_20] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
56 Parwani AV. Next generation diagnostic pathology: use of digital pathology and artificial intelligence tools to augment a pathological diagnosis. Diagn Pathol 2019;14:138. [PMID: 31881972 DOI: 10.1186/s13000-019-0921-2] [Cited by in Crossref: 24] [Cited by in F6Publishing: 20] [Article Influence: 8.0] [Reference Citation Analysis]
57 Noh KW, Buettner R, Klein S. Shifting Gears in Precision Oncology-Challenges and Opportunities of Integrative Data Analysis. Biomolecules 2021;11:1310. [PMID: 34572523 DOI: 10.3390/biom11091310] [Reference Citation Analysis]
58 Pantanowitz L. Digital cytology: Look how much has been achieved. Cytopathology 2020;31:370-1. [PMID: 32857883 DOI: 10.1111/cyt.12866] [Reference Citation Analysis]
59 Alpsoy A, Yavuz A, Elpek GO. Artificial intelligence in pathological evaluation of gastrointestinal cancers. Artif Intell Gastroenterol 2021; 2(6): 141-156 [DOI: 10.35712/aig.v2.i6.141] [Reference Citation Analysis]
60 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]
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62 Alaidarous MA. The emergence of new trends in clinical laboratory diagnosis. Saudi Med J 2020;41:1175-80. [PMID: 33130836 DOI: 10.15537/smj.2020.11.25455] [Reference Citation Analysis]
63 Jones-Hall Y. Digital pathology in academia: Implementation and impact. Lab Anim (NY) 2021;50:229-31. [PMID: 34349254 DOI: 10.1038/s41684-021-00828-6] [Reference Citation Analysis]
64 Wharton KA Jr, Wood D, Manesse M, Maclean KH, Leiss F, Zuraw A. Tissue Multiplex Analyte Detection in Anatomic Pathology - Pathways to Clinical Implementation. Front Mol Biosci 2021;8:672531. [PMID: 34386519 DOI: 10.3389/fmolb.2021.672531] [Reference Citation Analysis]
65 Lujan G, Quigley JC, Hartman D, Parwani A, Roehmholdt B, Meter BV, Ardon O, Hanna MG, Kelly D, Sowards C, Montalto M, Bui M, Zarella MD, LaRosa V, Slootweg G, Retamero JA, Lloyd MC, Madory J, Bowman D. Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association. J Pathol Inform 2021;12:17. [PMID: 34221633 DOI: 10.4103/jpi.jpi_67_20] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
66 Atallah NM, Toss MS, Verrill C, Salto-tellez M, Snead D, Rakha EA. Potential quality pitfalls of digitalized whole slide image of breast pathology in routine practice. Mod Pathol. [DOI: 10.1038/s41379-021-01000-8] [Reference Citation Analysis]
67 Dlamini Z, Francies FZ, Hull R, Marima R. Artificial intelligence (AI) and big data in cancer and precision oncology. Comput Struct Biotechnol J 2020;18:2300-11. [PMID: 32994889 DOI: 10.1016/j.csbj.2020.08.019] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
68 Hermsen M, Smeets B, Hilbrands L, van der Laak J. Artificial intelligence: is there a potential role in nephropathology? Nephrol Dial Transplant 2020:gfaa181. [PMID: 32995871 DOI: 10.1093/ndt/gfaa181] [Reference Citation Analysis]
69 Mont MA, Krebs VE, Backstein DJ, Browne JA, Mason JB, Taunton MJ, Callaghan JJ. Artificial Intelligence: Influencing Our Lives in Joint Arthroplasty. J Arthroplasty 2019;34:2199-200. [PMID: 31445865 DOI: 10.1016/j.arth.2019.08.017] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.3] [Reference Citation Analysis]
70 van der Laak J, Litjens G, Ciompi F. Deep learning in histopathology: the path to the clinic. Nat Med 2021;27:775-84. [PMID: 33990804 DOI: 10.1038/s41591-021-01343-4] [Cited by in Crossref: 4] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
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