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Year Number of Results
1954 1
1957 2
1962 2
1964 4
1965 3
1966 2
1967 1
1968 2
1969 2
1970 3
1971 1
1972 5
1973 1
1974 4
1975 8
1976 6
1977 3
1978 4
1979 6
1980 12
1981 4
1982 10
1983 11
1984 8
1985 10
1986 11
1987 11
1988 11
1989 19
1990 21
1991 24
1992 27
1993 41
1994 36
1995 53
1996 55
1997 57
1998 55
1999 66
2000 105
2001 125
2002 148
2003 170
2004 243
2005 312
2006 411
2007 512
2008 611
2009 709
2010 831
2011 1295
2012 1659
2013 2135
2014 2665
2015 3609
2016 4363
2017 6000
2018 9650
2019 14756
2020 22087
2021 32290
2022 41246
2023 45464
2024 54368
2025 31690
2026 3

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251,244 results

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Page 1
Introduction to Machine Learning, Neural Networks, and Deep Learning.
Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Choi RY, et al. Transl Vis Sci Technol. 2020 Feb 27;9(2):14. doi: 10.1167/tvst.9.2.14. Transl Vis Sci Technol. 2020. PMID: 32704420 Free PMC article.
PURPOSE: To present an overview of current machine learning methods and their use in medical research, focusing on select machine learning techniques, best practices, and deep learning. METHODS: A systematic literature search in PubMed wa …
PURPOSE: To present an overview of current machine learning methods and their use in medical research, focusing on select m …
A guide to machine learning for biologists.
Greener JG, Kandathil SM, Moffat L, Jones DT. Greener JG, et al. Nat Rev Mol Cell Biol. 2022 Jan;23(1):40-55. doi: 10.1038/s41580-021-00407-0. Epub 2021 Sep 13. Nat Rev Mol Cell Biol. 2022. PMID: 34518686 Review.
In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited …
In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the m …
Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review.
Theodore Armand TP, Nfor KA, Kim JI, Kim HC. Theodore Armand TP, et al. Nutrients. 2024 Apr 6;16(7):1073. doi: 10.3390/nu16071073. Nutrients. 2024. PMID: 38613106 Free PMC article.
This study aims to comprehensively investigate the current landscape of AI in nutrition, providing a deep understanding of the potential of AI, machine learning (ML), and deep learning (DL) in nutrition sciences and highlighting eventual challen …
This study aims to comprehensively investigate the current landscape of AI in nutrition, providing a deep understanding of the potent …
Artificial intelligence, machine learning and deep learning: Potential resources for the infection clinician.
Theodosiou AA, Read RC. Theodosiou AA, et al. J Infect. 2023 Oct;87(4):287-294. doi: 10.1016/j.jinf.2023.07.006. Epub 2023 Jul 17. J Infect. 2023. PMID: 37468046 Free article. Review.
BACKGROUND: Artificial intelligence (AI), machine learning and deep learning (including generative AI) are increasingly being investigated in the context of research and management of human infection. ...
BACKGROUND: Artificial intelligence (AI), machine learning and deep learning (including generative AI) are incre …
Review of machine learning and deep learning models for toxicity prediction.
Guo W, Liu J, Dong F, Song M, Li Z, Khan MKH, Patterson TA, Hong H. Guo W, et al. Exp Biol Med (Maywood). 2023 Nov;248(21):1952-1973. doi: 10.1177/15353702231209421. Epub 2023 Dec 6. Exp Biol Med (Maywood). 2023. PMID: 38057999 Free PMC article. Review.
This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. ...The quality of datasets used in the development of toxicity prediction models using machine learning and deep …
This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in rec …
Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging.
Currie G, Hawk KE, Rohren E, Vial A, Klein R. Currie G, et al. J Med Imaging Radiat Sci. 2019 Dec;50(4):477-487. doi: 10.1016/j.jmir.2019.09.005. Epub 2019 Oct 7. J Med Imaging Radiat Sci. 2019. PMID: 31601480 Review.
An understanding of the principles and application of radiomics, artificial neural networks, machine learning, and deep learning is an essential foundation to weave design solutions that accommodate ethical and regulatory requirements, and to craft AI- …
An understanding of the principles and application of radiomics, artificial neural networks, machine learning, and deep …
The use of artificial intelligence, machine learning and deep learning in oncologic histopathology.
Sultan AS, Elgharib MA, Tavares T, Jessri M, Basile JR. Sultan AS, et al. J Oral Pathol Med. 2020 Oct;49(9):849-856. doi: 10.1111/jop.13042. Epub 2020 Jun 15. J Oral Pathol Med. 2020. PMID: 32449232 Review.
DISCUSSION: A foundational overview of AI classification systems used in medicine and a review of common terminology used in machine learning and computational pathology will be presented. This paper provides a focused review on the recent advances in AI and deep …
DISCUSSION: A foundational overview of AI classification systems used in medicine and a review of common terminology used in machine …
A primer on deep learning in genomics.
Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A. Zou J, et al. Nat Genet. 2019 Jan;51(1):12-18. doi: 10.1038/s41588-018-0295-5. Epub 2018 Nov 26. Nat Genet. 2019. PMID: 30478442 Free PMC article. Review.
Deep learning methods are a class of machine learning techniques capable of identifying highly complex patterns in large datasets. ...We include general guidance for how to effectively use deep learning methods as well as a practical guid …
Deep learning methods are a class of machine learning techniques capable of identifying highly complex patterns …
Multimodal deep learning for biomedical data fusion: a review.
Stahlschmidt SR, Ulfenborg B, Synnergren J. Stahlschmidt SR, et al. Brief Bioinform. 2022 Mar 10;23(2):bbab569. doi: 10.1093/bib/bbab569. Brief Bioinform. 2022. PMID: 35089332 Free PMC article. Review.
Biomedical data are becoming increasingly multimodal and thereby capture the underlying complex relationships among biological processes. Deep learning (DL)-based data fusion strategies are a popular approach for modeling these nonlinear relationships. ...The review …
Biomedical data are becoming increasingly multimodal and thereby capture the underlying complex relationships among biological processes. …
Development and Application of Traditional Chinese Medicine Using AI Machine Learning and Deep Learning Strategies.
Pan D, Guo Y, Fan Y, Wan H. Pan D, et al. Am J Chin Med. 2024;52(3):605-623. doi: 10.1142/S0192415X24500265. Epub 2024 May 8. Am J Chin Med. 2024. PMID: 38715181 Review.
The application and advancement of TCM are, however, constrained by the absence of objective measuring standards due to its relatively abstract diagnostic methods and syndrome differentiation theories. Ongoing developments in machine learning (ML) and deep …
The application and advancement of TCM are, however, constrained by the absence of objective measuring standards due to its relatively abstr …
251,244 results
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