Artificial Intelligence for Medical Data Processing

Name: Artificial Intelligence for Medical Data Processing
Brand: Taylor & Francis
Price: 118.8 AUD
Availability: PreOrder

Algorithms and Applications

By: Chetna Sharma (Editor), Amandeep Kaur (Editor), Harpreet Singh (Editor)

Write A Review

eText | 25 August 2026 | Edition Number 1

At a Glance

Format
ePUB

eText

$118.80

or 4 interest-free payments of $29.70 with

Available: 25th August 2026

Preorder. Online access available after release.

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

This book showcases the potential of artificial intelligence to transform diagnostics, predictive analytics, personalized treatment, and healthcare resource optimization. It presents real-world applications and emerging trends, such as quantum computing, federated learning, and edge artificial intelligence.

Features:

Explores the intersection of artificial intelligence and neuroscience, including applications in understanding brain disorders, cognitive functions, and neuroimaging data processing.
Provides insights into emerging technologies such as quantum computing and their potential to revolutionize medical data processing, accelerate artificial intelligence algorithms, and solve previously intractable problems in healthcare.
Analyzes advanced strategies for identifying and mitigating biases in artificial intelligence models, ensuring compliance with global healthcare regulations and fostering ethical artificial intelligence adoption.
Discusses the application of multi-agent artificial intelligence systems in optimizing hospital operations, emergency response, and resource management in healthcare ecosystems.
Covers the use of generative models, such as generative adversarial network and variational autoencoders, in drug design, protein structure prediction, and the creation of synthetic biomedical datasets.

The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, biomedical engineering, drug delivery, and information technology.