Transforming Matrix Problem Solving with Intelligent Tutoring Systems

<p>ABSTRACT - Artificial Intelligence is rapidly advancing the medical field by providing innovative disease diagnosis, treatment, and research approaches. This study explores the application of artificial intelligence in breast cancer diagnostics, focusing on using convolutional neural networks and deep learning to analyze mammographic images. ResNet152 and DenseNet121 models were used to classify malignant changes, achieving AUC scores exceeding 0.9, demonstrating their clinical utility. The research emphasizes how artificial intelligence can enhance screening efficiency, expedite diagnostic processes, and facilitate personalized treatment approaches. Ethical considerations, including patient safety and the transparency of artificial intelligence systems, were also analyzed. The findings underscore the potential of artificial intelligence to transform diagnostic procedures for breast cancer and highlight the importance of further research to integrate these technologies into clinical practice.</p>

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Publication Details

Published
2025-03-19
DOI
https://doi.org/10.1109/infoteh64129.2025.10959245
Citations
1
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
2.48
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Transforming Matrix Problem Solving with Intelligent Tutoring Systems

Luka Filipović, Stevan Čakić, Tomo Popović, Elvis Taruh et al.
1 citations
Intelligent Tutoring Systems and Adaptive Learning
2.48
article

Transforming Matrix Problem Solving with Intelligent Tutoring Systems

Luka Filipović, Stevan Čakić, Tomo Popović, Elvis Taruh, Enisa Trubljanin
article en
1 citations

Abstract

<p>ABSTRACT - Artificial Intelligence is rapidly advancing the medical field by providing innovative disease diagnosis, treatment, and research approaches. This study explores the application of artificial intelligence in breast cancer diagnostics, focusing on using convolutional neural networks and deep learning to analyze mammographic images. ResNet152 and DenseNet121 models were used to classify malignant changes, achieving AUC scores exceeding 0.9, demonstrating their clinical utility. The research emphasizes how artificial intelligence can enhance screening efficiency, expedite diagnostic processes, and facilitate personalized treatment approaches. Ethical considerations, including patient safety and the transparency of artificial intelligence systems, were also analyzed. The findings underscore the potential of artificial intelligence to transform diagnostic procedures for breast cancer and highlight the importance of further research to integrate these technologies into clinical practice.</p>

University of Donja Gorica (ME)
Openalex Percentile: Top 11%
Intelligent Tutoring Systems and Adaptive Learning
2.48
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Transforming Matrix Problem Solving with Intelligent Tutoring Systems — Luka Filipović, Stevan Čakić, et al. · (2025) | TGRS Research Map | TGRS