Submit your paper : editorIJETjournal@gmail.com Paper Title : Research On How AI And Deep Learning Are Changing the Healthcare Industry ISSN : 2395-1303 Year of Publication : 2016 10.5281/zenodo.6990525 MLA Style: - MANOHARAN. R, Research On How AI And Deep Learning Are Changing the Healthcare Industry , Volume 2 - Issue 5 September-October 2016 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org APA Style: - MANOHARAN. R, Research On How AI And Deep Learning Are Changing the Healthcare Industry , Volume 2 - Issue 5 September-October 2016 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org Abstract A wide range of fields, including medicine, have benefited greatly from advances in artificial intelligence (AI) and machine learning (ML). To put it another way, AI refers to computer systems that mimic and simulate human intellect, such as the way a person approaches problem solving or his capacity to learn. A subset of artificial intelligence, machine learning (ML) is also included. Automatically, it discovers patterns in the data. It has been a decade of incredible progress in artificial intelligence, particularly in the field of machine learning, and in particular deep learning. In the medical industry, significant resources are increasingly devoted to challenges, but disregarding disadvantaged areas and their specific context has the potential to widen the digital divide. Reference 1. De Choudhury M, Gamon M, Counts S, Horvitz E. Predicting depression via social media. Seventh international AAAI conference on weblogs and social media (ICWSM) 2013;8(13):128–37. 2. Neill DB. Using artificial intelligence to improve hospital inpatient care. IEEE Intell Syst 2013;28:92–5. 3. Weng J, McClelland J, Pentland A, Sporns O, Stockman I, Sur M, et al. Autonomous mental development by Robots and Animals Science. 2001;291(5504):599-600. 4. Yu F, Ip HH. Semantic content analysis and annotation of histological images. Comput Biol Med. 2008;38(6):635. 5. Hoffman RA, Kothari S, Wang MD. In: 2014 36th annual international conference of the IEEE engineering in medicine and biology society, IEEE; 2014. pp. 194–7. 6. J. Weng, J. McClelland, A. Pentland, O. Sporns, I. Stockman, M. Sur, et al. Autonomous mental development by robots and animals Science, 291 (5504) (2001), pp. 599-600 7. R.S. Zucker, W.G. Regehr Short-term synaptic plasticity Annu Rev Physiol, 64 (2002), pp. 355-405 8. Mankoo P K, Shen R, Schultz N, Levine D A, Sander C. Time to recurrence and survival in serous ovarian tumors predicted from integrated genomic profiles. PLoS One. 2011;6(11):e24709. 9. Kaplan, W. (2010). Future Public Health Needs: Commonalities and Differences Between High- and Low-Resource Settings. Geneva: World Health Organization. 10. Fakoor R, Ladhak F, Nazi A, Huber M. Using deep learning to enhance cancer diagnosis and classification. A conference presentation The 30th International Conference on Machine Learning, 2013. 11. Sordo M. Introduction to neural networks in healthcare. OpenClinical, 2002. 12. I. Arel, D.C. Rose, T.P. Karnowski Deep machine learning—a new frontier in artificial intelligence research IEEE Comput Intell Mag, 5 (4) (2010), pp. 13-18 13. Fakoor R, Ladhak F, Nazi A, Huber M. Using deep learning to enhance cancer diagnosis and classification. A conference presentation The 30th International Conference on Machine Learning, 2013 14. Sordo M. Introduction to neural networks in healthcare. OpenClinical, 2002. www.openclinical.org/docs/int/neuralnetworks011.pdf 15. Hussain A, Malik A, Halim MU, Ali AM. The use of robotics in surgery: a review. Int J Clin Pract 2014; 68:1376–82. Keywords - artificial intelligence (AI); machine learning (ML); diagnosis; treatment; medicine |