The Moral Implications of Big Data and Machine Learning in Healthcare: A Review

Innocent Uwinama (), Jean Rusanganwa () and Emmanuel Ingabire ()

American Journal of Technology, 2023, vol. 2, issue 1, 45 - 53

Abstract: Aim: The purpose of this study was to explore the moral implications of big data and machine learning in healthcare Methods: Systematic literature review was conducted to identify the existing literature on the moral implications of big data and machine learning in healthcare. This involved a systematic search of relevant academic journals and databases. Results: Based on the reviewed studies, it is evident that the use of big data and machine learning in healthcare comes with both benefits and risks. The benefits include improved accuracy and efficiency in diagnosis and treatment, personalized care, and improved patient outcomes. However, the risks associated with these technologies cannot be overlooked. The potential risks include privacy concerns, bias, fairness and accountability. These risks arise due to the volume, velocity, and variety of data used in these technologies, which can result in inaccurate or incomplete information, or unintended consequences Conclusion: The use of big data and machine learning in healthcare has the potential to revolutionize the way healthcare is delivered, but it is not without its risks. Recommendations: Interdisciplinary collaboration between healthcare professionals, data scientists, and machine learning experts should be fostered to promote responsible and effective use of big data and machine learning in healthcare. Governments should also prioritize the development of robust data governance frameworks that include data privacy, security, and moral considerations.

Keywords: Moral implications; privacy; bias; machine learning; big data; healthcare (search for similar items in EconPapers)
Date: 2023
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