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Current Applications of Machine Learning in Medicine: ART

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Artificial Intelligence in Medicine

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Abstract

Assisted Reproductive Technology (ART) has gained pace over the last 40 years, with 0.1% of the global population estimated to have been born as a result of ART services (Faddy MJ, Gosden MD, Gosden RG, Reprod Biomed Online 36:455–458, 2018). ART spans a greater suite of technologies beyond in vitro fertilisation (IVF) pioneered by Steptoe and Edwards, with superovulation, intracytoplasmic sperm injection (ICSI), cryopreservation and genetic testing all conventional techniques offered in fertility clinics worldwide. Despite technological advancement, the ART process is lengthy, with no guarantee of success – often bestowing significant emotional stress on those undertaking treatment. The application of AI to the field looks strongly positioned to augment success rates, with the process of embryo selection already significantly improved by the assistance of AI in several clinical trials. This chapter explores the use of AI across other parts of the ART journey, such as gamete selection, as well as key challenges in the future development of AI products for ART.

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  1. Harrison.ai, Sydney, NSW, Australia

    Harriet Swearman, John F. Lambert & Aengus Tran

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  1. Department of Emergency Medicine, Monash Health, Clayton, VIC, Australia

    Manda Raz

  2. St Vincent’s Hospital Melbourne, Fitzroy, VIC, Australia

    Tam C. Nguyen

  3. St. Vincent’s Health Australia, Sydney, NSW, Australia

    Erwin Loh

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Swearman, H., Lambert, J.F., Tran, A. (2022). Current Applications of Machine Learning in Medicine: ART. In: Raz, M., Nguyen, T.C., Loh, E. (eds) Artificial Intelligence in Medicine. Springer, Singapore. https://doi.org/10.1007/978-981-19-1223-8_6

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