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>
Authors
- Luka Filipović (ORCID: https://orcid.org/0000-0002-0568-6801)
- Stevan Čakić (ORCID: https://orcid.org/0000-0003-2418-4820)
- Tomo Popović (ORCID: https://orcid.org/0000-0001-5245-3691)
- Elvis Taruh
- Enisa Trubljanin
Institutions
- University of Donja Gorica (ME)
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