Artificial or intelligent? Machine learning and medical selection: possibilities and risks

Abstract

Machine learning approaches form the basis of “artificial intelligence” and have been increasingly applied in health services settings. It has been shown that such approaches may produce more accurate predictions in some contexts, compared to conventional statistical approaches, and may also reduce the costs of decision-making through automation. Nevertheless, there are both general limitations to developing and implementing machine learning approaches that must be borne in mind. To date, relatively little research has been published on the potential for machine learning to support personnel selection. Moreover, there are particular challenges and issues that need to be considered if such methods are to be used to support decision-making in medical selection scenarios. This article describes some of these potential advantages and challenges and presents an illustrative example, based on real-world data, related to the selection of medical undergraduates.

Keywords

• machine learning
• artificial intelligence
• personnel selection
• medical selection
• logistic regression
• XGBoost