Open Access
Med Sci (Paris)
Volume 36, Number 11, Novembre 2020
Page(s) 1059 - 1067
Section Repères
Published online 05 November 2020
  1. Turing A.. Computing machinery and intelligence. Mind 1950 ; 49 : 433–460. [Google Scholar]
  2. Costabala FS, Yaob J, Kuhla E. Predicting the cardiac toxicity of drugs using a novel multiscale exposure-response simulator. Computer Methods Biomechanics Biomedical Engineering 2018 ; 21 : 232–246. [CrossRef] [Google Scholar]
  3. Doblare M, Garcıa JM, Gomez MJ. Modelling bone tissue fracture and healing: a review. Engineering Fracture Mechanics 2004; 71 (13–14). [Google Scholar]
  4. Shim J, Grosberg A, Nawroth JC, et al. Modeling of cardiac muscle thin films: pre-stretch, passive and active behavior. J Biomechanics 2012 ; 45 : 832–841. [CrossRef] [Google Scholar]
  5. Shanahan M. The technological singularity. Essential knowledge series. Cambridge (MA) : The MIT Press, 2015. [CrossRef] [Google Scholar]
  6. De Dombal FT, Leaper DJ, Staniland JR, et al. Computer-aided diagnosis of abdominal pain. Br Med J 1972 ; 2 : 9–13. [CrossRef] [PubMed] [Google Scholar]
  7. Ravdin PM, Siminoff LA, Davis GJ, et al. Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol 2001 ; 19 : 980–991. [CrossRef] [PubMed] [Google Scholar]
  8. Velten K. Mathematical modeling and simulation: introduction for scientists and engineers 2009 ; New York: Wiley, 362 p [Google Scholar]
  9. Jean A, Nyein MK, Zheng JQ, et al. An animal-to-human scaling law for blast-induced traumatic brain injury risk assessment. Proc Natl Acad Sci USA 2014 ; 111 : 15310–15315. [CrossRef] [Google Scholar]
  10. Yeo J, Jung GS, Tarakanova A, et al. Multiscale modeling of keratin, collagen, elastin and related human diseases: Perspectives from atomistic to coarse-grained molecular dynamics simulations. Extreme Mechanics Letters 2018 ; 20 : 112–124. [PubMed] [Google Scholar]
  11. Febvay S, Socrate S, House MD. Biomechanical modeling of cervical tissue: a quantitative investigation of cervical incompetence. Int Mechanical Engineering Congress Exposition 2003; 399–400. [Google Scholar]
  12. Tang A, Tam R, Cadrin-Chenevert A, et al. Canadian association of radiologists white paper on artificial intelligence in radiology. Can Assoc Radiol J 2018 ; 69 : 120135. [Google Scholar]
  13. Bou Assi E, Nguyen DK, Rihana S, Sawan M. Towards accurate prediction of epileptic seizures: a review. Biomedical Signal Processing Control 2017; 34 : 144157. [CrossRef] [Google Scholar]
  14. Marcus G. The next decade in AI: four steps towards robust artificial intelligence. arXiv 2002; 06177 : 2020. [Google Scholar]
  15. Alashwal H, El Halaby M, Crouse JJ, et al. The application of unsupervised clustering methods to Alzheimer’s disease. Front Comput Neurosci 2019; 13–31. [PubMed] [Google Scholar]
  16. Ng HP, Ong SH, Foong KWC, et al. Medical image segmentation using K-means clustering and improved watershed algorithm. Proc IEEE Southwest Symposium Image Analysis Interpretation 2006; 61–65. [Google Scholar]
  17. LeCun Y, Boser B, Denker JS, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput 1989 ; 1 : 541–551. [Google Scholar]
  18. Erhan D, Bengio Y, Courville A, et al. Why does unsupervised pre-training help deep learning?. J Machine Learning Research 2010 ; 11 : 625–660. [Google Scholar]
  19. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015 ; 521 : 436–444. [Google Scholar]
  20. Ekins S, Puhl AC, Zorn KM, et al. Exploiting machine learning for end-to-end drug discovery and development. Nat Mater 2019 ; 18 : 435–441. [CrossRef] [PubMed] [Google Scholar]
  21. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017 ; 542 : 115–118. [Google Scholar]
  22. Deo RC. Machine learning in medicine. Circulation 2015 ; 132 : 1920–1930. [CrossRef] [PubMed] [Google Scholar]
  23. Majkowska A, Mittal S, Steiner DF, et al. Chest radiograph interpretation with deep learning models: assessment with radiologist-adjudicated reference standards and population-adjusted evaluation. Radiology 2019; 294(2). [Google Scholar]
  24. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016 ; 316 : 2402–2410. [CrossRef] [PubMed] [Google Scholar]
  25. Ding Y, Sohn JH, Kawczynski MG, et al. A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the brain. Radiology 2018; 290(2). [Google Scholar]
  26. Xie Y, Ho J, Vemuri BC. multiple atlas construction from a heterogeneous brain MR image collection. IEEE Trans Med Imaging 2013 ; 32 : 628–635. [CrossRef] [PubMed] [Google Scholar]
  27. Zuluaga MA, Hush D, Edgar JF. Learning from only positive and unlabeled data to detect lesions in vascular CT images. medical image computing and computer-assisted intervention – MICCAI, et al. Lecture notes in computer science. Berlin-Heidelberg : Springer. 2011 ; 2011 : 6893. [Google Scholar]
  28. Martin L, Muller B, Ortiz Suárez PJ, et al. CamemBERT: a tasty French language model. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020. [Google Scholar]
  29. Beaudouin V, Bloch I, Bounie D, et al. Flexible and context-specific AI explainability: a multidisciplinary approach. 2020. arXiv:2003.07703 [cs.CY]. [Google Scholar]
  30. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 2019 ; 366 : 447–453. [Google Scholar]
  31. McDermott MBA, Wang S, Marinsek N, et al. Reproducibility in machine learning for Health. International Conference on Learning Representations 2019. [Google Scholar]
  32. Haiech J. Parcourir l’histoire de l’intelligence artificielle, pour mieux la définir et la comprendre. Med Sci (Paris) 2020; 36 : 919–23. [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
  33. Matuchansky C.. Intelligence clinique et intelligence artificielle : une question de nuance. Med Sci (Paris) 2019 ; 35 : 797–803. [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.