Critical Review on the Contribution of Machine Learning to Health Science

Abstract

Background: The field of machine learning in health science is evolving exponentially, with a focus on accelerating scientific discoveries, improving holistic well-being, and advancing personalized healthcare. Aim: In this same spirit, this critical review article aims to provide a comprehensive understanding of the role, challenges, opportunities, and ethical considerations of integrating machine learning into health science, with an emphasis on healthcare research and practice. Methods: To base its critiques on previous literature, the elucidative survey considered specific criteria, such as the significance and contribution of each source to the field, methodology or approach, and argument, as well as the use of evidence. Results: The study results indicate that machine learning holds great promise to improve evidence-based health science, but significant work is needed to ensure the technology is developed and deployed in a way that is trustworthy and ethical. Conclusion: In conclusion, the literature review presents a balanced assessment of the strengths, weaknesses, and notable features of the current state of machine learning in health science. The key takeaway point is that while machine learning has demonstrated significant potential to improve health science outcomes and strategic management, there are still important challenges, limitations, and research gaps that need to be addressed to facilitate widespread adoption and trust in these technologies.