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What is Autoencoders

Future of AI in Biomedicine and Biotechnology
Specifically utilized in the fields of deep learning and representation learning, autoencoders are a kind of artificial neural network used for unsupervised learning. Their purpose is to learn effective data representations by first encoding the input into a latent space with a smaller dimension, and then decoding it back into the original input space.
Published in Chapter:
Introduction to AI in Biomedical and Biotechnology
R. K. Chaurasia (The ICFAI University, Jaipur, India), Vaibhav Maheswari (The ICFAI University, Jaipur, India), and A. K. Saini (The ICFAI University, Jaipur, India)
Copyright: © 2024 |Pages: 20
DOI: 10.4018/979-8-3693-3629-8.ch002
Abstract
The infusion of biomedical and bio-technology is gaining high visibility as an asset for estimating various health problems at a fast pace as well as making it less expensive than the earlier methodologies. Despite the challenges, AI is very helpful in the near future in many ways such as early detection and diagnoses of a disease, providing more effective and personalized treatment options, reducing the healthcare cost, and improving the resource allocation. AI algorithms are also being used to analyze x-rays, CT scans, and other images to detect disease earlier with a great accuracy which leads to improved health outcomes of the patient. AI also analyzes massive genomic data to recognize infected genes, predict infection risk, and develop personalised therapies. By this, it can be concluded that the infusion of AI in biomedical and biotechnical has pushed healthcare into a transformative era.
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