Explainable Artificial Intelligence (XAI): Conception, Visualization and Assessment Approaches Towards Amenable XAI

Nizam, Tasleem; Zafar, Sherin

doi:10.1007/978-3-031-18292-1_3

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Two explainable AI-based cardiac disease prediction experiments are compared. This comparison can help AI beginners choose the best techniques [26]. In another study, deep learning models in electronic health records (EHRs) are examined, along with interpretability in medical AI systems [27].…”

Section: Stroke Recognitionmentioning

confidence: 99%

Healthcare Trust Evolution with Explainable Artificial Intelligence: Bibliometric Analysis

Dhiman,

Bonkra,

Kaur

et al. 2023

Information

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Recent developments in IoT, big data, fog and edge networks, and AI technologies have had a profound impact on a number of industries, including medical. The use of AI for therapeutic purposes has been hampered by its inexplicability. Explainable Artificial Intelligence (XAI), a revolutionary movement, has arisen to solve this constraint. By using decision-making and prediction outputs, XAI seeks to improve the explicability of standard AI models. In this study, we examined global developments in empirical XAI research in the medical field. The bibliometric analysis tools VOSviewer and Biblioshiny were used to examine 171 open access publications from the Scopus database (2019–2022). Our findings point to several prospects for growth in this area, notably in areas of medicine like diagnostic imaging. With 109 research articles using XAI for healthcare classification, prediction, and diagnosis, the USA leads the world in research output. With 88 citations, IEEE Access has the greatest number of publications of all the journals. Our extensive survey covers a range of XAI applications in healthcare, such as diagnosis, therapy, prevention, and palliation, and offers helpful insights for researchers who are interested in this field. This report provides a direction for future healthcare industry research endeavors.

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Section: Stroke Recognitionmentioning

confidence: 99%

Healthcare Trust Evolution with Explainable Artificial Intelligence: Bibliometric Analysis

Dhiman,

Bonkra,

Kaur

et al. 2023

Information

View full text Add to dashboard Cite

show abstract

“…There are some approaches to making deep learning and other AI techniques more explainable. Feature visualization 7 : The techniques are used to understand specific image patterns or features the AI model focuses on to arrive at its predictions. Heatmaps or saliency maps highlight the regions of interest within an image contributing to the AI model's decision. Grad‐CAM : Gradient‐weighted class activation mapping 8 (Grad‐CAM) is a technique that can generate visual explanations by analyzing the gradients of the target class concerning the convolutional feature maps of a deep learning model.…”

Section: Introductionmentioning

confidence: 99%

“…1. Feature visualization 7 : The techniques are used to understand specific image patterns or features the AI model focuses on to arrive at its predictions. Heatmaps or saliency maps highlight the regions of interest within an image contributing to the AI model's decision.…”

Section: Introductionmentioning

confidence: 99%

Explainable artificial intelligence for medical imaging: Review and experiments with infrared breast images

Raghavan,

Balasubramanian,

Veezhinathan

2024

Computational Intelligence

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There is a growing trend of using artificial intelligence, particularly deep learning algorithms, in medical diagnostics, revolutionizing healthcare by improving efficiency, accuracy, and patient outcomes. However, the use of artificial intelligence in medical diagnostics comes with the critical need to explain the reasoning behind artificial intelligence‐based predictions and ensure transparency in decision‐making. Explainable artificial intelligence has emerged as a crucial research area to address the need for transparency and interpretability in medical diagnostics. Explainable artificial intelligence techniques aim to provide insights into the decision‐making process of artificial intelligence systems, enabling clinicians to understand the factors the algorithms consider in reaching their predictions. This paper presents a detailed review of saliency‐based (visual) methods, such as class activation methods, which have gained popularity in medical imaging as they provide visual explanations by highlighting the regions of an image most influential in the artificial intelligence's decision. We also present the literature on non‐visual methods, but the focus will be on visual methods. We also use the existing literature to experiment with infrared breast images for detecting breast cancer. Towards the end of this paper, we also propose an “attention guided Grad‐CAM” that enhances the visualizations for explainable artificial intelligence. The existing literature shows that explainable artificial intelligence techniques are not explored in the context of infrared medical images and opens up a wide range of opportunities for further research to make clinical thermography into assistive technology for the medical community.

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Investigating the Quality of Explainable Artificial Intelligence: A Survey on Various Techniques of Post hoc

Nizam,

Zafar,

Biswas

et al. 2024

Lecture Notes in Networks and Systems

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Explainable Artificial Intelligence (XAI): Conception, Visualization and Assessment Approaches Towards Amenable XAI

Cited by 4 publications

References 17 publications

Healthcare Trust Evolution with Explainable Artificial Intelligence: Bibliometric Analysis

Healthcare Trust Evolution with Explainable Artificial Intelligence: Bibliometric Analysis

Explainable artificial intelligence for medical imaging: Review and experiments with infrared breast images

Investigating the Quality of Explainable Artificial Intelligence: A Survey on Various Techniques of Post hoc

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