Application Of Ai-Assisted Medical Imaging

Artificial Intelligence (AI) has rapidly gained widespread attention and has made unprecedented strides in recent years, significantly influencing various sectors, especially the medical field. Its applications have revolutionized healthcare by assisting doctors in making faster, more accurate, and data-driven decisions. This paper aims to provide an in-depth analysis of AI's role in medical imaging, focusing on its applications in interpreting X-rays, CT scans, and MRIs. These imaging techniques utilize cutting-edge image recognition algorithms, with Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs) being the most prominent. AI not only improves diagnostic precision but also reduces human error, leading to better patient outcomes. Furthermore, the paper discusses current trends in AI-driven medical imaging and explores potential future directions, emphasizing how AI could continue to advance medical diagnostics and treatment planning, paving the way for more personalized and effective healthcare solutions.

Authors

Institutions

Publication Details

Journal
Highlights in Science Engineering and Technology
Published
2024-12-24
DOI
https://doi.org/10.54097/rf9fh276
Citations
1
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
0.25
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Application Of Ai-Assisted Medical Imaging

Jialin Song
1 citations
Highlights in Science Engineering and Technology
Brain Tumor Detection and Classification
0.25
article

Application Of Ai-Assisted Medical Imaging

Jialin Song
article en
1 citations

Abstract

Artificial Intelligence (AI) has rapidly gained widespread attention and has made unprecedented strides in recent years, significantly influencing various sectors, especially the medical field. Its applications have revolutionized healthcare by assisting doctors in making faster, more accurate, and data-driven decisions. This paper aims to provide an in-depth analysis of AI's role in medical imaging, focusing on its applications in interpreting X-rays, CT scans, and MRIs. These imaging techniques utilize cutting-edge image recognition algorithms, with Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs) being the most prominent. AI not only improves diagnostic precision but also reduces human error, leading to better patient outcomes. Furthermore, the paper discusses current trends in AI-driven medical imaging and explores potential future directions, emphasizing how AI could continue to advance medical diagnostics and treatment planning, paving the way for more personalized and effective healthcare solutions.

Highlights in Science Engineering and TechnologyVol. 123
Capital Medical University (CN)
Openalex Percentile: Top 40%
Brain Tumor Detection and Classification
0.25
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.