Enhancing Diagnostic Accuracy: The Role of AI in Advanced Radiological Imaging

This paper investigates how clinical technologies focusing in radiology enriched by AI technologies have emerged as a pivotal advancement in the field of radiology, offering advancements that enhance both diagnostic accuracy and operational efficiency. These AI systems improve diagnostic performance by identifying subtle patterns that might be overlooked by human radiologists. Additionally, AI integration with Picture Archiving and Communication Systems (PACS) streamlines image management, automating routine tasks such as image analysis and report generation, which boosts workflow efficiency and productivity. Despite these benefits, challenges such as data privacy, algorithm transparency, and the need for continuous validation of AI models remain pertinent. As AI technology evolves, it holds the promise of further enhancing radiological practices, improving patient outcomes, and contributing to a more efficient healthcare system.

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Publication Details

Published
2024-12-01
DOI
https://doi.org/10.1109/tencon61640.2024.10903067
Citations
1
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.12
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article

Enhancing Diagnostic Accuracy: The Role of AI in Advanced Radiological Imaging

Chiang Liang Kok, Yit Yan Koh, Tee Hui Teo, Nguyen Van Thanh et al.
1 citations
Artificial Intelligence in Healthcare and Education
0.12
article

Enhancing Diagnostic Accuracy: The Role of AI in Advanced Radiological Imaging

Chiang Liang Kok, Yit Yan Koh, Tee Hui Teo, Nguyen Van Thanh, Chee Kit Ho
article en
1 citations

Abstract

This paper investigates how clinical technologies focusing in radiology enriched by AI technologies have emerged as a pivotal advancement in the field of radiology, offering advancements that enhance both diagnostic accuracy and operational efficiency. These AI systems improve diagnostic performance by identifying subtle patterns that might be overlooked by human radiologists. Additionally, AI integration with Picture Archiving and Communication Systems (PACS) streamlines image management, automating routine tasks such as image analysis and report generation, which boosts workflow efficiency and productivity. Despite these benefits, challenges such as data privacy, algorithm transparency, and the need for continuous validation of AI models remain pertinent. As AI technology evolves, it holds the promise of further enhancing radiological practices, improving patient outcomes, and contributing to a more efficient healthcare system.

Singapore University of Technology and Design (SG), Singapore Institute of Technology (SG), University of Newcastle Australia (AU)
Openalex Percentile: Top 45%
Artificial Intelligence in Healthcare and Education
0.12
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Enhancing Diagnostic Accuracy: The Role of AI in Advanced Radiological Imaging — Chiang Liang Kok, Yit Yan Koh, et al. · (2024) | TGRS Research Map | TGRS