The impact of artificial intelligence on prostate cancer diagnosis through magnetic resonance imaging

The study examines the impact of artificial intelligence (AI) on the diagnosis of prostate cancer using magnetic resonance imaging (MRI), emphasizing its ability to overcome the limitations of traditional methods. Through a systematic review based on the PRISMA guidelines, 20 recent studies (2023-2024) employing advanced techniques such as convolutional neural networks, deep learning, and computer-aided detection systems were evaluated. Findings revealed that AI significantly enhances diagnostic accuracy, achieving areas under the curve (AUC) of up to 0.997, 99.5% sensitivity, and 99% specificity, while reducing interobserver variability and the need for invasive procedures. Additionally, limitations related to the required technological infrastructure and algorithm transparency were identified. The study concludes that AI is an essential tool in modern diagnosis, complementing traditional methods and improving the precision and efficiency of prostate cancer detection.

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Publication Details

Journal
EthAIca
Published
2024-12-30
DOI
https://doi.org/10.56294/ai2024144
Primary Topic
Quantum and Classical Electrodynamics
Type
article
Field-Weighted Citation Impact
0.00
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article

The impact of artificial intelligence on prostate cancer diagnosis through magnetic resonance imaging

EthAIca
Quantum and Classical Electrodynamics
article

The impact of artificial intelligence on prostate cancer diagnosis through magnetic resonance imaging

article en

Abstract

The study examines the impact of artificial intelligence (AI) on the diagnosis of prostate cancer using magnetic resonance imaging (MRI), emphasizing its ability to overcome the limitations of traditional methods. Through a systematic review based on the PRISMA guidelines, 20 recent studies (2023-2024) employing advanced techniques such as convolutional neural networks, deep learning, and computer-aided detection systems were evaluated. Findings revealed that AI significantly enhances diagnostic accuracy, achieving areas under the curve (AUC) of up to 0.997, 99.5% sensitivity, and 99% specificity, while reducing interobserver variability and the need for invasive procedures. Additionally, limitations related to the required technological infrastructure and algorithm transparency were identified. The study concludes that AI is an essential tool in modern diagnosis, complementing traditional methods and improving the precision and efficiency of prostate cancer detection.

EthAIcaVol. 3
National University of Cajamarca (PE)
Industry, innovation and infrastructure
Openalex Percentile: Top 48%
Quantum and Classical Electrodynamics
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