Shagufta Anjum Allauddin Eksambi, Principal & Research Scholar at Alliance University, Bengaluru 


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Motherhood is a joy cherished through natural conception and in some cases of infertility accomplished through substituted reproduction known as Assisted Reproductive Techniques (ART). In the area of reproduction, ART techniques appear as a boon for infertile couples in begetting their genetic child through Intrauterine Insemination, Intrafallopian transfer, Intracytoplasmic sperm injection, In Vitro Fertilization, Surrogacy.

With the advancement of Science and Technology, Artificial Intelligence (AI) has ventured into the personal space of human beings simplifying ‘life’. AI is the intelligence displayed by machines based on the data stored into these machines. There are 3 major categories of AI methods widely used in medical applications: Machine Learning (ML), Natural Language Processing (NLP) and Robotic Surgery.

Multiple machine learning techniques have been used to improve the performance of assisted reproductive technology there-by minimizing failures, enhancing pregnancy success rates, and avoiding multiple pregnancies. Through personalizing treatment for individual patient based on his/her medical history, clinicians are trying to achieve best results. ML is used for embryo or Oocyte scoring selection through Pre-implantation genetic diagnosis (PGD)1, Sperm morphology through computer-aided sperm analysis (CASA) systems. This indeed is a boon rather than a bane for couples desiring to have their offspring.

Given this breakthrough in the medical field, laws in India should incorporate this new technology. The half-baked laws on ART and Surrogacy should be passed with immediate effect so that the aggrieved can reap benefit of this technology.

Keywords: Embryos, Assisted Reproductive Technology (ART), Intra Vitro Fertilization (IVF), Surrogacy, Artificial Intelligence. 

Journal Details

  • Name: Journal of Law and Legal Studies
  • Abbreviation: JLLS
  • Subject Area: Law
  • ISSN: Under Process
  • Publication Frequency: Annual
  • Language: English
  • Accessibility: Open Access (Creative Commons License)


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