AI language models in human reproduction research: exploring ChatGPT's potential to assist academic writing

Artificial intelligence (AI)-driven language models have the potential to serve as an educational tool, facilitate clinical decision-making, and support research and academic writing. The benefits of their use are yet to be evaluated and concerns have been raised regarding the accuracy, transparency, and ethical implications of using this AI technology in academic publishing. At the moment, Chat Generative Pre-trained Transformer (ChatGPT) is one of the most powerful and widely debated AI language models. Here, we discuss its feasibility to answer scientific questions, identify relevant literature, and assist writing in the field of human reproduction. With consideration of the scarcity of data on this topic, we assessed the feasibility of ChatGPT in academic writing, using data from six meta-analyses published in a leading journal of human reproduction. The text generated by ChatGPT was evaluated and compared to the original text by blinded reviewers. While ChatGPT can produce high-quality text and summarize information efficiently, its current ability to interpret data and answer scientific questions is limited, and it cannot be relied upon for a literature search or accurate source citation due to the potential spread of incomplete or false information. We advocate for open discussions within the reproductive medicine research community to explore the advantages and disadvantages of implementing this AI technology. Researchers and reviewers should be informed about AI language models, and we encourage authors to transparently disclose their use.

Female genital mutilation/cutting incidence, diagnostic capacities, and obstetric outcomes among migrant women: a single-center retrospective analysis in a 10-year birth cohort in Austria

INTRODUCTION

Practical experience in the care of women with female genital mutilation/cutting (FGM/C) is uncommon in Austria. However, affected women require specialized gynecological and obstetric care. In our region, there is currently neither an official counseling center nor specially trained medical personnel to address the special needs of women after FGM/C. The aim of this study was to determine the potential need for obstetric care for women who have undergone FGM/C in our region.

METHODS

We retrospectively reviewed women presenting for delivery at the LKH University Hospital Graz from 1.1.2010 until 31.12.2020 regarding the place of birth and/or the nationality of the mother to filter out women from a country with known FGM/C prevalence according to the UNICEF Global Database. Data on the documentation of FGM/C as well as demographic maternal data and peripartal parameters were gathered. Periods before and after the European refugee crisis in 2015 were compared.

RESULTS

During the study period, a total of 35,628 deliveries took place at our hospital. 856 (2.4%) deliveries of 539 women were included due to nationality or birthplace in a country with known FGM/C prevalence. We found only 17/539 (3.2%) documented FGM/C cases. The estimated FGM/C prevalence among those patients was, however, 208/539 (38,6%). Women affected by FGM/C in our collective were most frequently from Nigeria, Egypt, Iraq, Ghana, and Somalia. No statistically significant increase in deliveries during the study period in the overall study cohort was observed, with the exception of deliveries of Somali women (p = 0.000).

DISCUSSION

The discrepancy between documented and expected FGM/C rates (3,2% vs. 38,6%) in our collective suggests that most cases of FGM/C go undetected among women delivering in Austria. These data show the great need for special training for obstetricians and targeted contact points for affected women.