Revolutionizing Reproductive Health: A Narrative Review on the Role of AI in Transforming Infertility Care in Pakistan
DOI:
https://doi.org/10.48036/apims.v21i3.1486Abstract
The integration of artificial intelligence (AI) into infertility care is transforming the field of gynaecology, providing new solutions that improve diagnosis and treatment. AI technology is being used to increase the accuracy of fertility tests, personalized treatment plans, and more accurately predict outcomes. Intelligent models can measure important information such as hormone levels and ultrasound scans. This review explores how AI transforms patient treatment by decreasing diagnostic time and improving patient care and decision-making efficiency. Since there is a disconnect between male infertility, taken care of by urologists, and female infertility managed by gynaecologists, we want to bridge this gap through the use of AI in treatment. This article looks at the institutional and cultural constraints that
frequently keep husbands from going to infertility consultations with their wives. Infertility is typically viewed as a woman's problem in our conservative settings, which means that she must endure years of treatment. Women continue to receive hormonal treatments until the male element is brought to light because there is an unwillingness on the part of men to get tested. AI can be used to develop treatment regimens that equally incorporate both parties. Current applications, challenges, and future directions of AI in Pakistan and low-resource settings highlight promising opportunities for alleviating the disparity between male and female infertility care. This descriptive review aims to explore the application of AI to improve medical care, provide personalized counseling, and increase diagnostic accuracy. However, the use of AI in infertility care also raises ethical, regulatory, and accessibility issues that must be addressed to ensure that AI is responsible and equitable.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Samina Naeem Khalid, Mohammad Abdullah Naveed, Sabine Khan, Areeba Memon, Mansoor Kazi, Muhammad Mohsin Javaid, Muhammad Rashid Ahmed, Muhammad Farooq Umer

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.








