Eruptions and Disruptions of Machine Learning In the Health Sector

Abstract:

Machine learning (ML) has affected the way healthcare is being approached and offers tremendous opportunities for enhanced diagnosis, improved personalized treatment, predictive analytics and drug research and development among several increasing possibilities. For instance, the treatment and management of technostress is gaining the interest of machine learning. However, like any disruptive technology, ML also brings significant challenges and disruptions to the healthcare sector. These disruptions space include security and privacy dilemma, ethical issues, legal concerns, and transparency concerns. In this study, we examined the eruptions and disruptions of ML in the healthcare sector, the prospects and the challenges. Ultimately, this study recommends striking a balance between innovation (disruptive technology eruptions) and cautious mitigation of the disruptions that attend such innovations. This is critical to realizing the full power of ML in healthcare.

Keywords: Machine Learning, Disruptive Technology, Predictive Analytics, Healthcare, Ethical Considerations, Technostress