AI Foibles: A Cautionary Tale

In June of 2020, given the latest bolus of articles re: "technology" applications in healthcare, I ruminated about the deployment (and risk) of artificial intelligence (AI) and machine learning (ML) technologies in the space. The utilization of technology to assist in care delivery, whether off-the-shelf solutions or custom designed AI products to empower decision making/care management, is necessary but should be approached with caution. As I'd noted, and continue to believe, AI and ML are constructs that require a bit of near-term expectation management in healthcare but do have application when deployed with solution-driven clarity. As suggested, while the efficacy and value of AI and ML will improve with time, they are not "the" answer that will remedy the myriad care and cost delivery questions surrounding healthcare in the United States. Owing to space constraints and the fact I am not an AI guru, this column is an overly simplistic noodling of recent AI foibles outside of healthcare that tell a larger story. As in 2020, to level set, I am not an AI programmer, don't code in Python, and have never built a ML algorithm.