AI and Machine Learning in Healthcare - Applications, Challenges and Ethics

Abstract

Purpose: This research aims to discuss how AI and machine learning can be used in healthcare, challenges associated with implementation and the ethics around the widespread adoption of AI in the health care ecosystem while understanding the regulations around the technology implementation. Methodology: By conducting qualitative analysis on various applications of AI and machine learning in health care and its impacts on patient care, the analysis summarizes the challenges and ethics associated with the implementation. Findings: Results indicate that in the last few years, the data collected in the healthcare industry has increased manifold. Some studies suggest that structured data is growing by 40% each year, unstructured data is growing by over 80% and global data produced is forty zettabytes (ZB) as of 2020. With the increased regulatory and compliance requirements, effective data governance is a mandate for industries like healthcare where there is greater focus on data privacy, data security and personal information protection. This rapid explosion of data and the need to ensure the data is available at the right time has led to increased adoption of artificial intelligence (AI) and machine learning solutions across healthcare organizations to gain meaningful insights from the data collected. These technologies are proving to transform many aspects of healthcare ecosystem from patient care to administrative functions. Unique contribution to theory, policy, and practice: Currently AI and machine learning are aiding providers and patients by improving the health outcomes, but further research is necessary to validate to ensure these technologies are complying the regulatory guidelines without comprising on the patient care and the ethics involved when it comes to patient security and privacy.

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