Learning from AI ’s Failures

A detailed picture of AI ’s mistakes is the canvas upon which we create better digital solutions.John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.We all tend to ignore clich és because we’ve heard them so often, but some clichés are worth repeating. “We learn more from failure than success” comes to mind. While it may be overused, it nonetheless conveys an important truth for anyone involved in digital health. Two types of failures are worth closer scrutiny: alg orithms that claim to improve diagnosis or treatment but fall short for lack of evidence or fairness; and failure to convince clinicians in community practice that evidence-based algorithms are worth using.As we mentioned inan earlier column,a growing number of thought leaders in medicine have criticized the rush to generate AI-based algorithms because many lack the solid scientific foundation required to justify their use in direct patient care. Among the criticisms being leveled at AI developers are concerns about algorithms derived from a dataset that is not validated with a second, external dataset, overreliance on retrospective analysis, lack of generalizability, and various types of bias. A critical look at the hundreds of healthcare-related digital tools that are now coming to market indicates the need for more scrutiny, and the creation of a set of standards to help clinicians and oth...
Source: Life as a Healthcare CIO - Category: Information Technology Source Type: blogs