Open Access
Numéro
Med Sci (Paris)
Volume 36, Numéro 11, Novembre 2020
Page(s) 1059 - 1067
Section Repères
DOI https://doi.org/10.1051/medsci/2020189
Publié en ligne 5 novembre 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]

Les statistiques affichées correspondent au cumul d'une part des vues des résumés de l'article et d'autre part des vues et téléchargements de l'article plein-texte (PDF, Full-HTML, ePub... selon les formats disponibles) sur la platefome Vision4Press.

Les statistiques sont disponibles avec un délai de 48 à 96 heures et sont mises à jour quotidiennement en semaine.

Le chargement des statistiques peut être long.