A Study on Applications of AI, ML, DL And Blockchain In Healthcare And Pharmaceuticals And It’s Future
DOI:
https://doi.org/10.26438/ijcse/v6i8.765770Keywords:
Artificial Intelligence (AI), Blockchain (BC), Machine Learning (ML), Deep Learning (DL), Recurrent neural networks (RNN)Abstract
Artificial Intelligence (AI), Blockchain (BC), Machine Learning (ML) and Deep Learning (DL) is a progressive step within pharmaceutical and healthcare business. It's not a dreamland degree of advance any more drawn out, it’s a functional instrument that can enable a relationship to propel their composed game-plan, improve the standard of concern, create more income, and reduction chance. AI and ML have a crucial part to play in increasing crafted by tranquilizing improvement specialists, so that as educated, to begin with, an examination of the mass of scientific data can be coordinated remembering the ultimate objective to outline essential new learning. AI has truly moved from concept towards verity in the pharmaceutical industry. The use of BC in health care is expected to stress the characteristic framework in unimaginable strategies to advantage the impacted individual and enhancements in the audit happens unmistakably thriving and costs. AI, MI, and DL are rapidly getting to be establishment technologies, like the web, and an ocean change of impact is ensured to take hold. Recurrent neural networks (RNNs), which be commonly suitable for sequence evaluation, are one of the largely confident tools for time-series or text investigation. And one of the most effective applications of RNNs in healthcare is digital scientific report analysis. In this script, we characterize the way innovation is moving Pharmaceutical and Healthcare companies and its future
References
[1] McFarlane C. Patientory: A health care peer-to-peer EMR storage network v1.0. Available at: https://patientory.com/patientory_whitepaper.pdf.
[2] Earnest MA, Ross SE, Wittevrongel L, Moore LA, Lin CT. Use of a patient-accessible electronic medical record in a practice for congestive heart failure: patient and physician experiences. J Am Med Inform Assoc. 2004; 11:410–17.
[3] Interoperability with Blockchain Deterministic Methods for Connecting Patient Data to Uniform Patient Identifiers.
[4] State of Blockchain and Artificial Intelligence in Fintech http://news.crowdvalley.com/news/state-of-blockchain-and-artificial-intelligence-ai-in-fintech. Accessed 8/4/2018
[5] M. Swan, “Blockchains as an Equality Technology,” Broader Perspective blog, 2015.
[6] Vitalik Buterin. A next-generation smart contract and decentralized application platform. White Paper, 2014
[7] Ashish K Jha, David Doolan, Daniel Grandt, Tim Scott, and David W Bates. The use of health information technology in seven nations. International Journal of Medical Informatics, 77(12):848{854, 2008.
[8] Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., et al. (2016). Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. Jama, 316(22), 2402.
[9] Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancerwith deep neural networks. Nature, 542(7639), 115– 118.
[10] Translating Artificial Intelligence into Clinical Care, Andrew L. Beam, Isaac S. Kohane, JAMA 316, 2368, 2016
[11] Opportunities and Obstacles for Deep Learning in Biology and Medicine, CS Greene et al., bioRxiv preprint first posted online May. 28, 2017;
[12] Peterson, K., Deeduvanu, R., Kanjamala, P., & Boles, K. (2016). A Blockchain-Based Approach to Health Information Exchange Networks.
[13] Ekblaw, A., Azaria, A., Halamka, J. D., & Lippman, A. (2016, August). A Case Study for Blockchain in Healthcare: “MedRec” prototype for electronic health records and medical research data. In Proceedings of IEEE Open & Big Data Conference.
[14] Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones,et al, Opportunities and obstacles for deep learning in biology and medicine,
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