Exploring the Transformative Impact of Machine Learning in Healthcare: Applications, Challenges, and Future Directions

Abstract

Machine learning (ML) is one of the key driving forces in the development of new technologies and is used in healthcare in various ways. Thus, this paper aims at offering a synthesis of the subject of ML in healthcare, thereby exposing its importance and relevance. The use of ML is diverse from diagnosing the diseases and recommending treatments to the patients to managing hospitals’ operations, patient monitoring. Using big datasets in setting of healthcare, the power of machine learning is in identifying patterns, enhancing diagnostics and fine-tuning of treatments. This survey aims at: investigating the foundations of ML pertinent to the healthcare domain; identifying potential categories and case studies based on various domains of the healthcare sector; discussing issues, including data quality and ethical implications; and suggesting avenues for future research. The awareness of the great potential of ML in healthcare and its ability to change the healthcare sector for the better is vital in the continuous improvement of patient outcomes, development of new knowledge in the field of medicine, and reformation common healthcare systems worldwide.

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