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Cited by in F6Publishing
For: Zhang J, Song Y, Xia F, Zhu C, Zhang Y, Song W, Xu J, Ma X. Rapid and accurate intraoperative pathological diagnosis by artificial intelligence with deep learning technology. Med Hypotheses 2017;107:98-9. [PMID: 28915974 DOI: 10.1016/j.mehy.2017.08.021] [Cited by in Crossref: 10] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
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1 Martorell A, Martin-gorgojo A, Ríos-viñuela E, Rueda-carnero J, Alfageme F, Taberner R. Artificial intelligence in dermatology: A threat or an opportunity? Actas Dermo-Sifiliográficas (English Edition) 2021. [DOI: 10.1016/j.adengl.2021.11.007] [Reference Citation Analysis]
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7 Zhao E, Chen S. Materials with aggregation-induced emission characteristics for applications in diagnosis, theragnosis, disease mechanism study and personalized medicine. Mater Chem Front 2021;5:3322-43. [DOI: 10.1039/d0qm01132k] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 8.0] [Reference Citation Analysis]
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9 Liu Y, Li L, Zhang W, Chan P, Liu Y. Rapid identification of rainstorm disaster risks based on an artificial intelligence technology using the 2DPCA method. Atmospheric Research 2019;227:157-64. [DOI: 10.1016/j.atmosres.2019.05.006] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
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