| Summary: | Machine learning is a powerful tool that can be used to solve a wide range of problems in various applications and industries. The healthcare sector has faced specific challenges that have kept machine learning algorithms from becoming as widely and quickly adopted as in other industries. Data access and management challenges, ethical considerations, safety, and physician and patient perception present bigger barriers to implementation than model performance. In this paper, we propose adapting and customizing the concept of preconditions and postconditions from software engineering to develop a framework based on required clinical parameters and expected clinical output that will help bridge identified gaps in the implementation of machine learning tools in health care.
|
|---|