A Review and Study on AI in Health Care Issues

Authors

  • S. Dinakaran  Ph.D Scholar, Anna University, Salem, Tamilnadu, India
  • P. Anitha  Professor, Department of MCA, KSR College of Engineering, Namakkal, Tamilnadu, India

DOI:

https://doi.org//10.32628/CSEIT183886

Keywords:

Artificial Intelligence, Natural language processing (NTL), Deep learning, Healthcare, Machine learning Algorithm.

Abstract

In this article, the discussions reflect on medical AI research on maturity and influence that has been achieve. Artificial intelligence (AI) aims to imitate human cognitive functions. It is bringing a pattern transfer to healthcare, power-driven by growing accessibility of healthcare records and fast development of analytics methods. This article describes a technique for representing medical performance instructions and facilitating their beginning into the clinical routine. As this technique it be exploited in internet location, it can correspond to the foundation for distributing clinical instructions both connecting dissimilar institutions and between human and software, brokers are cooperating inside a clinical background. AI can be functional to a variety of healthcare records (structured and unstructured). AI methods contain machine learning for structured data, such as the usual support vector mechanism and neural network, and the modern deep learning, since natural language processing for unstructured data. Main disease areas that use AI tools include cancer, neurology and cardiology. This article presents a review in more information of AI applications in Cancer, in the three most important areas of premature detection and diagnosis, treatment, as well as result prediction and prognosis assessment.

References

  1. Jenna Burrell, How the Machine “hinks”: Understanding Opacity in Machine Learning Algorithms, 3 Big Data & Soc’y 1, 5 (2016).
  2. Megan Molteni, hanks to AI, Computers Can Now See Your Health Problems, Wired (Jan. 9, 2017), https://www.wired.com/2017/01/computers-can-tell-instructionance-youve-got-geneticdisorders/.
  3. Megan Molteni, If You Look at X-Rays or Moles for a Living, AI Is Coming for Your Job, Wired (Jan. 25, 2017), https://www.wired.com/2017/01/ look-x-rays-moles-living-ai-coming-job/.
  4. A.S.Syed Navaz, C.Prabhadevi & V.Sangeetha”Data Grid Concepts for Data Security in Distributed Computing” January 2013, International Journal of Computer Applications, Vol 61 – No 13, pp 6-11.
  5. A.S.Syed Navaz, M.Ravi & T.Prabhu, “Preventing Disclosure of Sensitive Knowledge by Hiding Inference” February 2013, International Journal of Computer Applications, Vol 63 – No 1. pp. 32-38.
  6. The development of AI systems usually involves ‘training’ them with data. For an overview of different training models, see Nesta (2015) Machines that learn in the wild: machine learning capabilities, limitations and implications.
  7. CBInsights (2017) AI, healthcare & the future of drug pricing: investment activity, market breakdown, AI in clinical trials.
  8. Future Advocacy (2018) Ethical, social, and political challenges of artificial intelligence in health.
  9. Dilsizian SE and Siegel EL (2013) Artificial intelligence in medicine and cardiac imaging Curr Cardiol Rep 16: 441; Future Advocacy (2018) Ethical, social, and political challenges of artificial intelligence in health.
  10. Shafner L, et al. (2017) Evaluating the use of an artificial intelligence (AI) platform on mobile devices to measure and support tuberculosis medication adherence.
  11. Moore SF, et al. (2018) Harnessing the power of intelligent machines to enhance primary care Bri J Gen Pract 68: 6-7.
  12. Nuffield Council on Bioethics (2015) The collection, linking and use of data in biomedical research and health care: ethical issues; PHG Foundation (2015) Data sharing to support UK clinical genetics & genomics services; and Reform (2018) Thinking on its own: AI in the NHS.
  13. Kolker E, Özdemir V, Kolker E. How Healthcare can refocus on its Super-Customers (Patients, n =1) and Customers (Doctors and Nurses) by Leveraging Lessons from Amazon, Uber, and Watson. OMICS 2016;20:329–33.
  14. Karakülah G, Dicle O, Koşaner O, et al. Computer based extraction of phenoptypic features of human congenital anomalies from the digital literature with natural language processing methods. Stud Health Technol Inform 2014;205:570–4.
  15. Darcy AM, Louie AK, Roberts LW. Machine Learning and the Profession of Medicine. JAMA 2016;315:551–2.
  16. Thornhill RE, Lum C, Jaberi A, et al. Can shape analysis differentiate free-floating internal carotid artery Thrombus from atherosclerotic plaque in patients evaluated with CTA?for cancer or transient ischemic attack? Acad Radiol 2014;21:345–54.
  17. Freiherr G. The seeds of artificial intelligence: SUMEX-AIM (1980). U.S. G.P.O; DHEW publication no.(NIH) 80-2071. Washington, D.C.; U.S. Dept. of Health, Education, and Welfare, Public Health Service, National Institutes of Health; 1980.
  18. A.S.Syed Navaz , T.Dhevisri & Pratap Mazumder “Face Recognition Using Principal Component Analysis and Neural Networks“ March -2013, International Journal of Computer Networking, Wireless and Mobile Communications. Vol No – 3, Issue No - 1, pp. 245-256.
  19. A.S.Syed Navaz, J.Antony Daniel Rex, S.Jensy Mary. “Cluster Based Secure Data Transmission in WSN” July – 2015, International Journal of Scientific & Engineering Research, Vol No - 6, Issue No - 7, pp. 1776 – 1781.
  20. S.Jensy Mary, A.S Syed Navaz & J.Antony Daniel Rex, “QA Generation Using Multimedia Based Harvesting Web Information” November – 2015, International Journal of Innovative Research in Computer and Communication Engineering, Vol No - 3, Issue No - 11, pp.10381-10386.
  21. A.S Syed Navaz & K.Durairaj “Signature Authentication Using Biometric Methods” January – 2016, International Journal of Science and Research, Vol No - 5, Issue No - 1, pp.1581-1584.
  22. A.S.Syed Fiaz, N.Asha, D.Sumathi & A.S.Syed Navaz “Data Visualization: Enhancing Big Data More Adaptable and Valuable” February – 2016, International Journal of Applied Engineering Research, Vol No - 11, Issue No - 4, pp.–2801-2804.
  23. A.S.Syed Navaz & Dr.G.M. Kadhar Nawaz “Flow Based Layer Selection Algorithm for Data Collection in Tree Structure Wireless Sensor Networks” March – 2016, International Journal of Applied Engineering Research, Vol No - 11, Issue No - 5, pp.–3359-3363.
  24. A.S.Syed Navaz & Dr.G.M. Kadhar Nawaz “Layer Orient Time Domain Density Estimation Technique Based Channel Assignment in Tree Structure Wireless Sensor Networks for Fast Data Collection” June - 2016, International Journal of Engineering and Technology,Vol No - 8, Issue No - 3, pp.–1506-1512.
  25. A.S.Syed Fiaz, K.S.Guruprakash & A.S.Syed Navaz “Prediction of Best Cloud Service Provider using the QoS Ranking Framework” January – 2018, International Journal of Engineering & Technology, Vol No - 7, Issue No -1.1, pp.– 486-488.

Downloads

Published

2018-12-30

Issue

Section

Research Articles

How to Cite

[1]
S. Dinakaran, P. Anitha, " A Review and Study on AI in Health Care Issues, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 8, pp.281-288, November-December-2018. Available at doi : https://doi.org/10.32628/CSEIT183886