A novel explainable AI model for medical data analysis

• Autorzy: Shakhovska Nataliya , Shebeko Andrii , Prykarpatskyy Yarema

Identyfikatory

DOI

10.2478/jaiscr-2024-0007

• Języki publikacji: EN

Abstrakty

EN

This research focuses on the development of an explainable artificial intelligence (Explainable AI or XAI) system aimed at the analysis of medical data. Medical imaging and related datasets present inherent complexities due to their high-dimensional nature and the intricate biological patterns they represent. These complexities necessitate sophisticated computational models to decode and interpret, often leading to the employment of deep neural networks. However, while these models have achieved remarkable accuracy, their ”black-box” nature raises legitimate concerns regarding their interpretability and reliability in the clinical context. To address this challenge, we can consider the following approaches: traditional statistical methods, a singular complex neural network, or an ensemble of simpler neural networks. Traditional statistical methods, though transparent, often lack the nuanced sensitivity required for the intricate patterns within medical images. On the other hand, a singular complex neural network, while powerful, can sometimes be too generalized, making specific interpretations challenging. Hence, our proposed strategy employs a hybrid system, combining multiple neural networks with distinct architectures, each tailored to address specific facets of the medical data interpretation challenges. The key components of this proposed technology include a module for anomaly detection within medical images, a module for categorizing detected anomalies into specific medical conditions and a module for generating user-friendly, clinically-relevant interpretations.

Słowa kluczowe

EN

explainable AI; deep neural networks; medical data analysis; interpretability; clinical applications

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2024
• Tom: Vol. 14, No. 2
• Strony: 121--137
• Opis fizyczny: Bibliogr. 24 poz., rys.

Twórcy

autor

Shakhovska Nataliya

• Department of Artificial Intelligence Systems, Lviv Polytechnic National University, 5 Kniazia Romana St., Lviv, Ukraine
• Department of Applied Mathematics, University of Agriculture in Krakow, 21 Mickiewicza al., 31-120 Krakow, Poland

• https://orcid.org/0000-0002-6875-8534

autor

Shebeko Andrii

• Department of Artificial Intelligence Systems, Lviv Polytechnic National University, 5 Kniazia Romana St., Lviv, Ukraine

• https://orcid.org/0000-0002-0212-8855

autor

Prykarpatskyy Yarema

• Department of Applied Mathematics, University of Agriculture in Krakow, 21 Mickiewicza al., 31-120 Krakow, Poland
• Institute of Mathematics of NAS of Ukraine, 3, Tereschenkivska st., 01024 Kiyv-4, Ukraine

• https://orcid.org/0000-0002-3033-7419

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).