<abstract xmlns="http://www.w3.org/1999/xhtml">
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.
</abstract>

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.

A Novel Explainable AI Model for Medical Data Analysis

Department of Artificial Intelligence Systems

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"A Novel Explainable AI Model for Medical Data Analysis"}

This research focuses on the development of an explainable artificial intelligence (Explainable AI or XAI) system aimed at the analysis of medical data....

A Novel Explainable AI Model for Medical Data Analysis

Data publikacji: 19 mar 2024

Zakres stron: 121 - 137

Otrzymano: 05 lis 2023

Przyjęty: 26 sty 2024

DOI: https://doi.org/10.2478/jaiscr-2024-0007

Słowa kluczowe
Explainable AI, deep neural networks, medical data analysis, interpretability, clinical applications

© 2024 Nataliya Shakhovska et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Novel Explainable AI Model for Medical Data Analysis

Data publikacji: 19 mar 2024

Zakres stron: 121 - 137

Otrzymano: 05 lis 2023

Przyjęty: 26 sty 2024

DOI: https://doi.org/10.2478/jaiscr-2024-0007

Słowa kluczoweExplainable AI, deep neural networks, medical data analysis, interpretability, clinical applications

© 2024 Nataliya Shakhovska et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Explainable AI, deep neural networks, medical data analysis, interpretability, clinical applications