AI and Machine Learning In Healthcare: Garbage In, Garbage Out

Much proselytizing has occurred regarding the value and future of artificial intelligence (AI) and machine learning in healthcare. The industry is burgeoning. As with blockchain technology, which continues to evolve in the healthcare marketplace, AI and machine learning are constructs that require a bit of near-term expectation management.

AI missteps are bad enough in businesses, but consider the life-and-death ramifications if you have deployed, say, a cardiology AI protocol that does not have all the right inputs and parameters built-in.

Subject matter experts (SMEs) and data scientists must work hand in glove to delineate the problem to be solved, the data needed, and the nurturing of the algorithms to ensure they remain relevant. Bad “training” of the computer and bad data inputs lead to bad and/or inaccurate outputs.