• Rashmi P. Dagde
• Aabha N. Yerpude
• Anushka P. Shende
• Dimpal J. Charde
• Tanaya H. Agre

Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have resulted in significant progress in predicting and identifying health emergencies, disease populations, disease states, and immune responses. Despite this progress, skepticism persists regarding the practical application and interpretation of results from ML-based approaches in healthcare settings. Nevertheless, the inclusion of these approaches is rapidly increasing. In this paper, we present a concise overview of machine learning-based approaches and learning algorithms, including supervised, unsupervised, and reinforcement learning, along with relevant examples. Additionally, we discuss the application of ML in various healthcare fields, such as radiology, genetics, electronic health records, and neuroimaging. We also briefly address the risks and challenges of ML application to healthcare, such as system privacy and ethical concerns, and provide recommendations for future applications.

Artificial Intelligence, healthcare, Machine Learning, Electronic health records

"Machine Learning in Healthcare", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 10, page no.e460-e464, October-2023, Available :http://www.jetir.org/papers/JETIR2310370.pdf

"Machine Learning in Healthcare", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 10, page no. ppe460-e464, October-2023, Available at : http://www.jetir.org/papers/JETIR2310370.pdf