Review of Integration of Machine Learning Techniques in Biomedical Engineering: Enhancing Diagnosis, Treatment, and Healthcare Systems

Authors

  • Ambika Ganapur Department of Computer Science, Karnataka State Open University, Mysore, Karnataka, India
  • Dr. Sumati Ramakrishna Gowda Department of Computer Science, Karnataka State Open University, Mysore, Karnataka, India

DOI:

https://doi.org/10.61808/jsrt153

Keywords:

AI, Machine Learning, Biomedical Engineering, Disease Diagnosis And Prognosis, Image Analysis, Drug Discovery, Patient Outcomes

Abstract

The extensive influence of biomedical engineering's use of AI and ML on healthcare systems, diagnosis, and therapy are the primary foci of this review. By sifting through mountains of data in search of patterns, machine learning algorithms have utterly transformed biological applications. Medical imaging analysis, illness diagnosis and prognosis, individualized treatment planning, medication development, healthcare administration, and other critical areas where machine learning improves biomedical engineering are scrutinized in this study. It lays forth the revolutionary possibilities of machine learning in healthcare delivery, patient outcomes, and precision medicine (drug development) via an exhaustive literature analysis. In addition, it suggests ways forward for study and application while tackling problems including data quality, interpretability, and ethical concerns.

Published

22-11-2024

How to Cite

Ambika Ganapur, & Dr. Sumati Ramakrishna Gowda. (2024). Review of Integration of Machine Learning Techniques in Biomedical Engineering: Enhancing Diagnosis, Treatment, and Healthcare Systems. Journal of Scientific Research and Technology, 2(11), 50–59. https://doi.org/10.61808/jsrt153