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The Future of Healthcare: Artificial Intelligence And Medical Big Data

Jese Leos
· 6.6k Followers · Follow
Published in Radiomics And Its Clinical Application: Artificial Intelligence And Medical Big Data (The MICCAI Society Series)
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Artificial Intelligence (AI) is transforming various industries, and healthcare is no exception. With the increasing digitization of medical data, combined with AI technologies, we are witnessing a revolutionary change in the way medical professionals diagnose and treat diseases. The MICCAI Society Series has been at the forefront of this evolution, bringing together experts to explore the potential of AI and Medical Big Data.

The MICCAI Society Series: Exploring the Convergence of AI and Medical Big Data

The Medical Image Computing and Computer-Assisted Intervention (MICCAI) Society is an international organization that aims to explore the intersection of artificial intelligence, medical imaging, and computer-assisted interventions. The annual MICCAI conference serves as a platform for researchers, clinicians, and industry professionals to exchange knowledge, present cutting-edge research, and discuss challenges and advancements in the field.

One of the key focuses of the MICCAI Society Series is the utilization of AI in medical big data analysis. Medical big data refers to large-scale datasets that are generated through various sources, including electronic health records, medical imaging devices, wearable devices, and genomics. These datasets are incredibly valuable, as they hold immense potential to improve patient outcomes, optimize treatment plans, and enhance medical research.

Radiomics and Its Clinical Application: Artificial Intelligence and Medical Big Data (The MICCAI Society book Series)
by Gissel Velarde (1st Edition, Kindle Edition)

4.6 out of 5

Language : English
File size : 46589 KB
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
Print length : 295 pages

The Role of Artificial Intelligence in Medical Big Data Analysis

AI algorithms have the ability to process vast amounts of complex data much faster and more accurately than human experts. By applying machine learning and deep learning techniques to medical big data, AI systems can identify patterns, detect anomalies, and predict outcomes with a high level of accuracy.

For example, AI-powered image recognition algorithms can analyze medical images such as X-rays, CT scans, and MRI scans to assist radiologists in detecting and diagnosing diseases. By comparing millions of images and past cases, AI algorithms can provide timely and accurate analysis, improving the speed and accuracy of diagnoses.

Similarly, AI algorithms can analyze electronic health records and genomic data to identify genetic risk factors, predict disease progression, and personalize treatment plans. This enables healthcare providers to offer precision medicine, tailored to the specific needs of each patient, leading to better treatment outcomes.

Key Applications and Impact of AI in Healthcare

The applications of AI in healthcare are vast and diverse, with the potential to revolutionize a range of medical fields. Some key areas where AI is making a significant impact include:

1. Medical Imaging:

AI algorithms can analyze medical images to detect tumors, lesions, and other abnormalities, improving early detection rates and enabling more effective treatment strategies.

2. Drug Discovery:

AI can expedite the drug discovery process by analyzing vast amounts of scientific literature and genomic data to identify potential drug candidates, acceleratin

Radiomics and Its Clinical Application: Artificial Intelligence and Medical Big Data (The MICCAI Society book Series)
by Gissel Velarde (1st Edition, Kindle Edition)

4.6 out of 5

Language : English
File size : 46589 KB
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
Print length : 295 pages

The rapid development of artificial intelligence technology in medical data analysis has led to the concept of radiomics. This book introduces the essential and latest technologies in radiomics, such as imaging segmentation, quantitative imaging feature extraction, and machine learning methods for model construction and performance evaluation, providing invaluable guidance for the researcher entering the field.

It fully describes three key aspects of radiomic clinical practice: precision diagnosis, the therapeutic effect, and prognostic evaluation, which make radiomics a powerful tool in the clinical setting.

This book is a very useful resource for scientists and computer engineers in machine learning and medical image analysis, scientists focusing on antineoplastic drugs, and radiologists, pathologists, oncologists, as well as surgeons wanting to understand radiomics and its potential in clinical practice.

  • An to the concepts of radiomics
  • In-depth presentation of the core technologies and methods
  • Summary of current radiomics research, perspective on the future of radiomics and the challenges ahead
  • An to several platforms that are planned to be built: cooperation, data sharing, software, and application platforms
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