La tunisie Medicale - 2022 ; Vol 100 ( n°05 ) : 354-355
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  1. Khanzode KCA, Sarode RD. Advantages and Disadvantages of Artificial Intelligence and Machine Learning: A Literature Review. Int J Libr Inf Sci IJLIS. 2020;9(1):3.
  2. Schank RC. Where’s the AI? AI Mag. 15 déc 1991;12(4):38‑38.
  3. Artificial Intelligence - MeSH - NCBI [Internet]. [cité 31 mai 2022]. Disponible sur: mesh/?term=Artificial+intelligence
  4. Kundu M, Nasipuri M, Basu DK. Knowledge-based ECG interpretation: a critical review. Pattern Recognit. 2000;33(3):351‑73.
  5. De Dombal FT, Leaper DJ, Staniland JR, McCann AP, Horrocks JC. Computer-aided diagnosis of acute abdominal pain. Br Med J. 1972;2(5804):9‑13.
  6. Shortliffe EH, Davis R, Axline SG, Buchanan BG, Green CC, Cohen SN. Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. Comput Biomed Res. 1975;8(4):303‑20.
  7. Miller RA, McNeil MA, Challinor SM, Masarie Jr FE, Myers JD. The INTERNIST-1/quick medical REFERENCE project—Status report. West J Med. 1986;145(6):816.
  8. Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284(2):574‑82.
  9. Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, et al. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. ArXiv Prepr ArXiv171105225. 2017;
  10. Litjens G, Sánchez CI, Timofeeva N, Hermsen M, Nagtegaal I, Kovacs I, et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep. 2016;6(1):1‑11.
  11. Bejnordi BE, Veta M, Van Diest PJ, Van Ginneken B, Karssemeijer N, Litjens G, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. Jama. 2017;318(22):2199‑210.
  12. Samala RK, Chan HP, Hadjiiski L, Helvie MA, Wei J, Cha K. Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography. Med Phys. 2016;43(12):6654‑66.
  13. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. nature. 2017;542(7639):115‑8.
  14. Abràmoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci. 2016;57(13):5200‑6.
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Tunisia treatment Child diagnosis surgery prognosis Children epidemiology Crohn’s disease Risk factors Breast cancer screening Cancer prevalence Mortality
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