Is Artificial Intelligence (AI) currently able to provide evidence-based scientific responses on methods that can improve the outcomes of embryo transfers? No.
{"title":"Is Artificial Intelligence (AI) currently able to provide evidence-based scientific responses on methods that can improve the outcomes of embryo transfers? No.","authors":"Argyrios Kolokythas, Michael H Dahan","doi":"10.5935/1518-0557.20240050","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>The rapid development of Artificial Intelligence (AI) has raised questions about its potential uses in different sectors of everyday life. Specifically in medicine, the question arose whether chatbots could be used as tools for clinical decision-making or patients' and physicians' education. To answer this question in the context of fertility, we conducted a test to determine whether current AI platforms can provide evidence-based responses regarding methods that can improve the outcomes of embryo transfers.</p><p><strong>Methods: </strong>We asked nine popular chatbots to write a 300-word scientific essay, outlining scientific methods that improve embryo transfer outcomes. We then gathered the responses and extracted the methods suggested by each chatbot.</p><p><strong>Results: </strong>Out of a total of 43 recommendations, which could be grouped into 19 similar categories, only 3/19 (15.8%) were evidence-based practices, those being \"ultrasound-guided embryo transfer\" in 7/9 (77.8%) chatbots, \"single embryo transfer\" in 4/9 (44.4%) and \"use of a soft catheter\" in 2/9 (22.2%), whereas some controversial responses like \"preimplantation genetic testing\" appeared frequently (6/9 chatbots; 66.7%), along with other debatable recommendations like \"endometrial receptivity assay\", \"assisted hatching\" and \"time-lapse incubator\".</p><p><strong>Conclusions: </strong>Our results suggest that AI is not yet in a position to give evidence-based recommendations in the field of fertility, particularly concerning embryo transfer, since the vast majority of responses consisted of scientifically unsupported recommendations. As such, both patients and physicians should be wary of guiding care based on chatbot recommendations in infertility. Chatbot results might improve with time especially if trained from validated medical databases; however, this will have to be scientifically checked.</p>","PeriodicalId":46364,"journal":{"name":"Jornal Brasileiro de Reproducao Assistida","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jornal Brasileiro de Reproducao Assistida","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5935/1518-0557.20240050","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"OBSTETRICS & GYNECOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: The rapid development of Artificial Intelligence (AI) has raised questions about its potential uses in different sectors of everyday life. Specifically in medicine, the question arose whether chatbots could be used as tools for clinical decision-making or patients' and physicians' education. To answer this question in the context of fertility, we conducted a test to determine whether current AI platforms can provide evidence-based responses regarding methods that can improve the outcomes of embryo transfers.
Methods: We asked nine popular chatbots to write a 300-word scientific essay, outlining scientific methods that improve embryo transfer outcomes. We then gathered the responses and extracted the methods suggested by each chatbot.
Results: Out of a total of 43 recommendations, which could be grouped into 19 similar categories, only 3/19 (15.8%) were evidence-based practices, those being "ultrasound-guided embryo transfer" in 7/9 (77.8%) chatbots, "single embryo transfer" in 4/9 (44.4%) and "use of a soft catheter" in 2/9 (22.2%), whereas some controversial responses like "preimplantation genetic testing" appeared frequently (6/9 chatbots; 66.7%), along with other debatable recommendations like "endometrial receptivity assay", "assisted hatching" and "time-lapse incubator".
Conclusions: Our results suggest that AI is not yet in a position to give evidence-based recommendations in the field of fertility, particularly concerning embryo transfer, since the vast majority of responses consisted of scientifically unsupported recommendations. As such, both patients and physicians should be wary of guiding care based on chatbot recommendations in infertility. Chatbot results might improve with time especially if trained from validated medical databases; however, this will have to be scientifically checked.