Explainable AI: A Hybrid Approach to Generate Human-Interpretable Explanation for Deep Learning Prediction

De, Tanusree; Giri, Prasenjit; Mevawala, Ahmeduvesh; Nemani, Ramyasri; Deo, Arati

doi:10.1016/j.procs.2020.02.255

Cited by 33 publications

(14 citation statements)

References 2 publications

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“…e explainability and the transparency of the decisions are exceptionally important in the healthcare models. Regarding human-interpretable explanations [33] through a hybrid model of network hidden layer with TREPAN decision tree [34], the model can produce higher-quality reason codes that provide concise, human-interpretable reasons for model outcomes in the instance stage.…”

Section: Resultsmentioning

confidence: 99%

From Blackbox to Explainable AI in Healthcare: Existing Tools and Case Studies

Srinivasu¹,

Sandhya²,

Jhaveri

et al. 2022

Mobile Information Systems

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Introduction. Artificial intelligence (AI) models have been employed to automate decision-making, from commerce to more critical fields directly affecting human lives, including healthcare. Although the vast majority of these proposed AI systems are considered black box models that lack explainability, there is an increasing trend of attempting to create medical explainable Artificial Intelligence (XAI) systems using approaches such as attention mechanisms and surrogate models. An AI system is said to be explainable if humans can tell how the system reached its decision. Various XAI-driven healthcare approaches and their performances in the current study are discussed. The toolkits used in local and global post hoc explainability and the multiple techniques for explainability pertaining the Rational, Data, and Performance explainability are discussed in the current study. Methods. The explainability of the artificial intelligence model in the healthcare domain is implemented through the Local Interpretable Model-Agnostic Explanations and Shapley Additive Explanations for better comprehensibility of the internal working mechanism of the original AI models and the correlation among the feature set that influences decision of the model. Results. The current state-of-the-art XAI-based and future technologies through XAI are reported on research findings in various implementation aspects, including research challenges and limitations of existing models. The role of XAI in the healthcare domain ranging from the earlier prediction of future illness to the disease’s smart diagnosis is discussed. The metrics considered in evaluating the model’s explainability are presented, along with various explainability tools. Three case studies about the role of XAI in the healthcare domain with their performances are incorporated for better comprehensibility. Conclusion. The future perspective of XAI in healthcare will assist in obtaining research insight in the healthcare domain.

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Section: Resultsmentioning

confidence: 99%

From Blackbox to Explainable AI in Healthcare: Existing Tools and Case Studies

Srinivasu¹,

Sandhya²,

Jhaveri

et al. 2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…The eXplainable artificial intelligence (XAI) is experiencing a rapid transformation due to recent deep learning advances, where many earlier unsolved obstacles have become step by step solvable. The latest progress proved that applications with real-world complexity could gradually be interpretable (Kuo et al 2019;Rudin 2019;De et al 2020;Nguyen et al 2020a). Nevertheless, XAI is a young topic that attracts growing interest, and the number of published articles rises quickly.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the approach preserved the accuracy, sensitivity, specificity with a shallower structure, which the trainable parameters were reduced by 20%. Recently, a hybrid model that combines two prior algorithms, which include the TREPAN decision tree and the clustering of a hidden layer representation, was proposed to deconstruct a deep learning network (De et al 2020). The proposed model aimed to visualize the information flow of an underlying model to make it comprehensible to humans.…”

Section: Hybrid Interpretable Modelmentioning

confidence: 99%

Explainable artificial intelligence: a comprehensive review

et al. 2021

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Thanks to the exponential growth in computing power and vast amounts of data, artificial intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be ubiquitously adopted in our daily lives. Even though AI-powered systems have brought competitive advantages, the black-box nature makes them lack transparency and prevents them from explaining their decisions. This issue has motivated the introduction of explainable artificial intelligence (XAI), which promotes AI algorithms that can show their internal process and explain how they made decisions. The number of XAI research has increased significantly in recent years, but there lacks a unified and comprehensive review of the latest XAI progress. This review aims to bridge the gap by discovering the critical perspectives of the rapidly growing body of research associated with XAI. After offering the readers a solid XAI background, we analyze and review various XAI methods, which are grouped into (i) pre-modeling explainability, (ii) interpretable model, and (iii) post-modeling explainability. We also pay attention to the current methods that dedicate to interpret and analyze deep learning methods. In addition, we systematically discuss various XAI challenges, such as the trade-off between the performance and the explainability, evaluation methods, security, and policy. Finally, we show the standard approaches that are leveraged to deal with the mentioned challenges.

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“…Generalized Linear Models (GLM) provide meaningful, clear, and accessible feature importance that indicates the relative importance of each feature when making a prediction for the regression models. Outputs of regression models are a linear combination of features with different weights depending on the significance of features [35].…”

Section: Explainable Aimentioning

confidence: 99%

The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification

Sarp

Kuzlu

Wilson³

et al. 2021

Electronics

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Artificial Intelligence (AI) has been among the most emerging research and industrial application fields, especially in the healthcare domain, but operated as a black-box model with a limited understanding of its inner working over the past decades. AI algorithms are, in large part, built on weights calculated as a result of large matrix multiplications. It is typically hard to interpret and debug the computationally intensive processes. Explainable Artificial Intelligence (XAI) aims to solve black-box and hard-to-debug approaches through the use of various techniques and tools. In this study, XAI techniques are applied to chronic wound classification. The proposed model classifies chronic wounds through the use of transfer learning and fully connected layers. Classified chronic wound images serve as input to the XAI model for an explanation. Interpretable results can help shed new perspectives to clinicians during the diagnostic phase. The proposed method successfully provides chronic wound classification and its associated explanation to extract additional knowledge that can also be interpreted by non-data-science experts, such as medical scientists and physicians. This hybrid approach is shown to aid with the interpretation and understanding of AI decision-making processes.

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Explainable AI: A Hybrid Approach to Generate Human-Interpretable Explanation for Deep Learning Prediction

Cited by 33 publications

References 2 publications

From Blackbox to Explainable AI in Healthcare: Existing Tools and Case Studies

From Blackbox to Explainable AI in Healthcare: Existing Tools and Case Studies

Explainable artificial intelligence: a comprehensive review

The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification

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