From Humans to Robots: Machine Learning for Healthcare

Authors

  • Ankita Daghottra   Computer Science and Engineering, The Northcap University,Gurugram, Haryana, India
  • Dr. Divya Jain  Computer Science and Engineering, The Northcap University,Gurugram, Haryana, India

DOI:

https://doi.org//10.32628/CSEIT2173152

Keywords:

Machine learning; Artificial Learning; healthcare; Robotics; Robotic Surgery; Surgical Phase

Abstract

Machine learning is a branch of artificial intelligence (AI) through which identification of patterns in data is done and with help of these patterns, useful outcomes or conclusions are predicted. One of the most prominent or frequently studied applications of machine learning is the surgical phase or robotic surgery. This makes machine learning an important part of research in robotics. The implementation of this technology in the field of healthcare aims in improving medical practices resulting in more precise and advanced surgical assessments. This paper aims in outlining the implementation and applications of machine learning related to robotics in the field of healthcare. Machine learning aims in generating positive outcomes with assumptions. The objective of this paper is to bring light on how these technologies have become an important part of providing more effective and comprehensive strategies which eventually add to positive patient outcomes and more advanced healthcare practices.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-06-30

Issue

Volume 7, Issue 3, May-June-2021

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Ankita Daghottra , Dr. Divya Jain, " From Humans to Robots: Machine Learning for Healthcare, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 3, pp.705-714, May-June-2021. Available at doi : https://doi.org/10.32628/CSEIT2173152