All Models Are Wrong, but Some Are Useful
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COMMENTARY
All Models Are Wrong, but Some Are Useful Carol Lynn Curchoe 1 Received: 13 July 2020 / Accepted: 14 July 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Statistician George E.P. Box famously said, “All models are wrong, but some are useful”. Reproduction researchers and clinicians are grappling to critically evaluate the recent deluge of artificial intelligence (AI) studies to determine if they are “useful” for prediction, or at the very least, can automate the manual, routine, and subjective drudgery of day-to day clinical practice. Predictive modeling has evolved into a standalone subdiscipline of reproductive medicine, and the literature have been analyzed and evaluated [1] using formal systems, such as; the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) and prediction model risk of bias assessment tool (PROBST). Predictive modeling’s place as a support tool is growing in clinical practice, but gains in patient outcomes and improved workflows have yet to be fully realized. Will artificial intelligence also fail to live up to the hype? And why is AI so breathtakingly exciting? Despite early promise, global approaches in transcriptomics, epigenomics, and proteomics [2] have largely failed to unambiguously identify statistically relevant clinical signatures for complex problems in reproduction, while simultaneously providing a wealth of basic knowledge that improves our understanding of embryo development. Successful human reproduction is a complex problem with so many variables that even the best-planned studies can quickly become confounded, yielding inconclusive results. Inherent variation and subjectivity are the enemy of consistency and objectivity. AI can address these challenges (presumably with greater accuracy) to assemble solutions that cannot be resolved by the human senses. AI is perfectly suited for the seemingly intractable questions of reproductive medicine, for example, embryo selection [3], the complex dialogue between endometrium and embryo and recurrent miscarriage [4], the physiological function of the uterus and disease states * Carol Lynn Curchoe [email protected] 1
CCRM IVF Network, Lone Tree, CO, USA
like endometriosis and adenomyosis [5], therapeutic targets for biological and chronological ovarian ageing [6], preimplantation genetics to improve pregnancy outcomes [7], and recurrent implantation failure [8]. If you are struggling to understand what artificial intelligence IS let alone how it works, consider this great analogy. Imagine the “gold standard” outcome, i.e., a healthy, live-born infant, conceived quickly through reproductive medicine, as a ball of crumpled paper. The ball has many sheets of paper and each sheet of paper is a different complex problem in and of itself: patient demographics, gamete quality, disease etiology, embryo quality, uterine lining and receptivity, and more. Artificial intelligence “uncrumples” this ball, working backward through each step, slow
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