Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications.

Dimitris Visvikis, Catherine Cheze Le Rest, Vincent Jaouen, Mathieu Hatt

December 2019 Eur J Nucl Med Mol Imaging

Synopsis of Social media discussions

The discussions indicate an initial curiosity about AI concepts, with phrases like 'What Does

A
Agreement
 Neither agree nor disagree

The posts do not clearly support or oppose the publication, indicating a neutral stance.

I
Interest
 Moderate level of interest

There is some curiosity about what artificial intelligence and deep learning mean, reflecting moderate interest.

E
Engagement
 Neutral engagement

The discussion appears superficial, with limited depth in understanding or critique.

I
Impact
 Moderate level of impact

The discussed posts suggest a growing awareness but do not imply a significant immediate influence on practice or policy.

Social Mentions

YouTube

2 Videos

Twitter

4 Posts

Metrics

Video Views

5,589

Total Likes

338

Extended Reach

26,133

Social Features

6

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

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Abstract Synopsis

  • The text explains how artificial intelligence, especially machine deep learning, has become a crucial part of recent advancements in medical imaging, including tasks like image reconstruction, denoising, segmentation, and predictive analysis.
  • It provides definitions of key concepts such as AI, deep learning, and radiogenomics, and explores how these techniques can be applied specifically to nuclear medicine imaging.
  • The review emphasizes the potential and current use of AI and deep learning methods to improve various aspects of nuclear medicine, with a special focus on how radiogenomics integrates imaging and genetic information.