O-098 Embryo selection using Artificial Intelligence (AI): Epistemic and ethical considerations

Abstract

Study question What are the epistemic and ethical considerations of clinically implementing Artificial Intelligence (AI) algorithms in embryo selection? Summary answer AI embryo selection algorithms used to date are “black-box” models with significant epistemic and ethical issues, and there are no trials assessing their clinical effectiveness. What is known already The innovation of time-lapse imaging offers the potential to generate vast quantities of data for embryo assessment. Computer Vision allows image data to be analysed using algorithms developed via machine learning which learn and adapt as they are exposed to more data. Most algorithms are developed using neural networks and are uninterpretable (or “black box”). Uninterpretable models are either too complicated to understand or proprietary, in which case comprehension is impossible for outsiders. In the IVF context, these outsiders include doctors, embryologists and patients, which raises ethical questions for its use in embryo selection. Study design, size, duration We performed a scoping review of articles evaluating AI for embryo selection in IVF. We considered the epistemic and ethical implications of current approaches. Participants/materials, setting, methods We searched Medline, Embase, ClinicalTrials.gov and the EU Clinical Trials Register for full text papers evaluating AI for embryo selection using the following key words: artificial intelligence* OR AI OR neural network* OR machine learning OR support vector machine OR automatic classification AND IVF OR in vitro fertilisation OR embryo*, as well as relevant MeSH and Emtree terms for Medline and Embase respectively. Main results and the role of chance We found no trials evaluating clinical effectiveness either published or registered. We found efficacy studies which looked at 2 types of outcomes – accuracy for predicting pregnancy or live birth and agreement with embryologist evaluation. Some algorithms were shown to broadly differentiate well between “good-” and “poor-” quality embryos but not between embryos of similar quality, which is the clinical need. Almost universally, the AI models were opaque (“black box”) in that at least some part of the process was uninterpretable. “Black box” models are problematic for epistemic and ethical reasons. Epistemic concerns include information asymmetries between algorithm developers and doctors, embryologists and patients; the risk of biased prediction caused by known and/or unknown confounders during the training process; difficulties in real-time error checking due to limited interpretability; the economics of buying into commercial proprietary models, brittle to variation in the treatment process; and an overall difficulty troubleshooting. Ethical pitfalls include the risk of misrepresenting patient values; concern for the health and well-being of future children; the risk of disvaluing disability; possible societal implications; and a responsibility gap, in the event of adverse events. Limitations, reasons for caution Our search was limited to the two main medical research databases. Although we checked article references for more publications, we were less likely to identify studies that were not indexed in Medline or Embase, especially if they were not cited in studies identified in our search. Wider implications of the findings It is premature to implement AI for embryo selection outside of a clinical trial. AI for embryo selection is potentially useful, but must be done carefully and transparently, as the epistemic and ethical issues are significant. We advocate for the use of interpretable AI models to overcome these issues. Trial registration number not applicable

Ethical Issues of Artificial Intelligence & Assisted Reproductive Technologies

“…This may either be due to the fact that the decision making process is too complicated to explain, or else because the algorithm is proprietary and therefore cannot be understood by outsiders, that is, by doctors, embryologists or patients. 98 Such opaque, or "black box" models present significant epistemic and ethical issues. 99 According to Afnan and colleagues, knowledge-based concerns, on one hand, revolve around asymmetries of information between developers and users (including clinicians and patients); the risk of bias introduced during the training process; difficulties in error checking in real time; buying commercial, and therefore, proprietary models; as well as difficulties related to troubleshooting.…”

“…98 Such opaque, or "black box" models present significant epistemic and ethical issues. 99 According to Afnan and colleagues, knowledge-based concerns, on one hand, revolve around asymmetries of information between developers and users (including clinicians and patients); the risk of bias introduced during the training process; difficulties in error checking in real time; buying commercial, and therefore, proprietary models; as well as difficulties related to troubleshooting. 100 On the other hand, ethical concerns focus on the risk of misrepresentation of patient values; concerns for the wellbeing and health of children born as a result of these technologies; the risk of devaluing disability; possible societal implications; and the accountability gap in cases of bad decisions.…”

“…109 There are no prospective studies which demonstrate better patient outcomes or cost reductions over current practice, 110 and any meaningful application would need large-scale randomised clinical trials and meta-analyses of their data. 111 Indeed, researchers have reported that they found no trials evaluating clinical effectiveness, 112 arguing that it is too early to adopt AI for embryo selection outside of a clinical trial. For example, while studies report that AI can distinguish between good and bad embryos, algorithms may not necessarily be able to differentiate embryos of similar quality which is needed clinically.…”