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This paper aims to explore physicians' adoption of Machine learning models in the healthcare process and barriers that may hinder it. A review of the literature about ML in healthcare included current and potentially beneficial clinical applications and clinicians' adoption and trust towards such applications. While some physicians are looking forward to using ML to improve their outcomes and reduce their load, we uncovered fear of unwanted outcomes and concerns about privacy of data, legal liability, and patient dissatisfaction.

Physician Acceptance of Machine Learning for Diagnostic Purposes: Caution, Bumpy Road Ahead!