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ChatGPT: a reliable fertility decision-making tool?
Beilby, Kiri; Hammarberg, Karin.
Afiliación
  • Beilby K; Education Program in Reproduction and Development, Department of Obstetrics and Gynaecology, School of Clinical Sciences, Monash University, Melbourne, Australia.
  • Hammarberg K; Global and Women's Health, School of Public and Health and Preventative Medicine, Monash University, Melbourne, Australia.
Hum Reprod ; 39(3): 443-447, 2024 Mar 01.
Article en En | MEDLINE | ID: mdl-38199794
ABSTRACT
The internet is the primary source of infertility-related information for most people who are experiencing fertility issues. Although no longer shrouded in stigma, the privacy of interacting only with a computer provides a sense of safety when engaging with sensitive content and allows for diverse and geographically dispersed communities to connect and share their experiences. It also provides businesses with a virtual marketplace for their products. The introduction of ChatGPT, a conversational language model developed by OpenAI to understand and generate human-like text in response to user input, in November 2022, and other emerging generative artificial intelligence (AI) language models, has changed and will continue to change the way we interact with large volumes of digital information. When it comes to its application in health information seeking, specifically in relation to fertility in this case, is ChatGPT a friend or foe in helping people make well-informed decisions? Furthermore, if deemed useful, how can we ensure this technology supports fertility-related decision-making? After conducting a study into the quality of the information provided by ChatGPT to people seeking information on fertility, we explore the potential benefits and pitfalls of using generative AI as a tool to support decision-making.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Inteligencia Artificial / Infertilidad Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Hum Reprod Asunto de la revista: MEDICINA REPRODUTIVA Año: 2024 Tipo del documento: Article País de afiliación: Australia

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Inteligencia Artificial / Infertilidad Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Hum Reprod Asunto de la revista: MEDICINA REPRODUTIVA Año: 2024 Tipo del documento: Article País de afiliación: Australia