AI-powered fertility assessment tool

Astha Puri 1, *, Rohan Mathur 2 and Kapil D Nayar 3

1 Carlson School of Management, University of Minnesota, USA.
2 UCLA Anderson School of Management, USA.
3 Mayo Clinic, USA.
 
Review
International Journal of Science and Research Archive, 2024, 12(01), 742–744.
Article DOI: 10.30574/ijsra.2024.12.1.0860
Publication history: 
Received on 07 April 2024; revised on 12 May 2024; accepted on 15 May 2024
 
Abstract: 
The integration of artificial intelligence (AI) into fertility assessment and treatment presents a promising solution to the pervasive issue of infertility, offering individuals and couples personalized insights and recommendations on their reproductive health journey. By harnessing machine learning algorithms, an AI-powered fertility assessment tool analyzes diverse datasets encompassing demographic and clinical variables to generate comprehensive fertility profiles. These profiles provide predictions of fertility potential, identification of risk factors for infertility, and tailored recommendations for lifestyle modifications or medical interventions. The tool's adaptability and capacity for continuous improvement through user feedback and updated research ensure the relevance and effectiveness of its recommendations, while stringent privacy measures safeguard sensitive information. Preliminary testing demonstrates high accuracy and user satisfaction, highlighting the tool's potential to transform reproductive healthcare by streamlining assessments, reducing diagnostic uncertainties, and facilitating early interventions. However, the responsible implementation of AI-powered tools requires careful consideration of ethical, regulatory, and societal implications to ensure equitable access and mitigate algorithmic biases. Overall, AI-powered fertility assessment tools signify a significant advancement in reproductive medicine, offering hope and empowerment to individuals and couples navigating the complexities of fertility challenges.
 
Keywords: 
Artificial Intelligence; Fertility; Assessment; Intervention; Healthcare
 
Full text article in PDF: 
 
Copyright information: 

Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0