Explainable AI in Healthcare

Pawar, Urja; O’Shea, Donna; Rea, Susan; O’Reilly, Ruairi

doi:10.1109/cybersa49311.2020.9139655

Cited by 116 publications

(49 citation statements)

References 4 publications

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“…For legal, ethical, and scientific reasons, this is a significant issue for high stakes decisions such as clinical diagnosis [85,86]. As such, there has been numerous calls for research on explainable machine learning and AI (sometimes referred to as XAI) [85][86][87] and some small but significant progress has been made in the use of XAI in clinical diagnosis, albeit not in arboviruses, the focus of this paper (see for example [88]).…”

Section: Discussionmentioning

confidence: 99%

Machine learning and deep learning techniques to support the clinical diagnosis of arboviral diseases: A systematic review

Neto¹,

Oliveira²,

Teixeira³

et al. 2021

Preprint

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Background: NTDs primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms, inaccurate serologic tests resulting from cross-reaction and co-infection with other arboviruses. Objective: The goal of this paper is to present evidence on the state of the art of studies investigating the automatic classification of arboviral diseases to support clinical diagnosis based on ML and DL models. Method: We carried out a SLR in which Google Scholar was searched to identify key papers on the topic. From an initial 963 records (956 from string-based search and 7 from single backward snowballing technique), only 15 relevant papers were identified. Results: Results show that current research is focused on the binary classification of Dengue, primarily using Tree based ML algorithms and only one paper was identified using DL. Five papers presented solutions for multi-class problems, covering Dengue (and its levels) and Chikungunya. No papers were identified that investigated models to differentiate between Dengue, Chikungunya, and Zika. Conclusions: The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient's quality of life.

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Section: Discussionmentioning

confidence: 99%

Machine learning and deep learning techniques to support the clinical diagnosis of arboviral diseases: A systematic review

Neto¹,

Oliveira²,

Teixeira³

et al. 2021

Preprint

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“…For instance, in Mason et al [142,144], assurance was applied to reinforcement learning methods for safety-critical systems. Prentzas et al [174] presented an assurance method for machine learning as its applied to stroke predictions, similar to Pawar's et al 's [167] XAI for healthcare framework. Pepe et al [169], and Chittajallu et al 's [42] developed a method for surgery video detection methods.…”

Section: The State Of Ai Assurancementioning

confidence: 99%

A survey on artificial intelligence assurance

2021

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Artificial Intelligence (AI) algorithms are increasingly providing decision making and operational support across multiple domains. AI includes a wide (and growing) library of algorithms that could be applied for different problems. One important notion for the adoption of AI algorithms into operational decision processes is the concept of assurance. The literature on assurance, unfortunately, conceals its outcomes within a tangled landscape of conflicting approaches, driven by contradicting motivations, assumptions, and intuitions. Accordingly, albeit a rising and novel area, this manuscript provides a systematic review of research works that are relevant to AI assurance, between years 1985 and 2021, and aims to provide a structured alternative to the landscape. A new AI assurance definition is adopted and presented, and assurance methods are contrasted and tabulated. Additionally, a ten-metric scoring system is developed and introduced to evaluate and compare existing methods. Lastly, in this manuscript, we provide foundational insights, discussions, future directions, a roadmap, and applicable recommendations for the development and deployment of AI assurance.

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“…For more applications of XAI in healthcare, refer to the surveys by Adadi and Berrada [197] and Pawar et al [198], as well as the tutorial by Ahmad et al [199].…”

Section: A Healthcarementioning

confidence: 99%

Explainable Artificial Intelligence for Tabular Data: A Survey

2021

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Machine learning techniques are increasingly gaining attention due to their widespread use in various disciplines across academia and industry. Despite their tremendous success, many such techniques suffer from the "black-box" problem, which refers to situations where the data analyst is unable to explain why such techniques arrive at certain decisions. This problem has fuelled interest in Explainable Artificial Intelligence (XAI), which refers to techniques that can easily be interpreted by humans. Unfortunately, many of these techniques are not suitable for tabular data, which is surprising given the importance and widespread use of tabular data in critical applications such as finance, healthcare, and criminal justice. Also surprising is the fact that, despite the vast literature on XAI, there are still no survey articles to date that focus on tabular data. Consequently, despite the existing survey articles that cover a wide range of XAI techniques, it remains challenging for researchers working on tabular data to go through all of these surveys and extract the techniques that are suitable for their analysis. Our article fills this gap by providing a comprehensive and up-to-date survey of the XAI techniques that are relevant to tabular data. Furthermore, we categorize the references covered in our survey, indicating the type of the model being explained, the approach being used to provide the explanation, and the XAI problem being addressed. Our article is the first to provide researchers with a map that helps them navigate the XAI literature in the context of tabular data. INDEX TERMSBlack-box models, Explainable Artificial Intelligence, Machine Learning, Model interpretability VOLUME 4, 2016This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2021.3116481, IEEE Access M. Sahakyan et al.: Explainable Artificial Intelligence for Tabular Data: A Survey• Decomposability, reflecting the degree to which each input, parameter, and calculation can be explained intuitively;• Algorithmic transparency, reflecting the degree to which the inner workings of the learning algorithm can be understood.For example, rule-based models [16] are considered transparent since they use a series of if-then rules that can easily be understood without the need for any further explanation. Unlike transparent models, black-box models are those that do not explain their predictions in a way that humans can understand [17]. Examples of black-box models include artificial neural networks [18] and gradient boosting [19]. Although black-box models are hard to interpret by humans, they tend to have higher prediction accuracy compared to their transparent counterparts. This trade-off between accuracy and transparency gives raise to the black-box explanation problem, which involves explaining the rationale behind the decisions made by black-box models. By providing such explanations, one can continue ...

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Explainable AI in Healthcare

Cited by 116 publications

References 4 publications

Machine learning and deep learning techniques to support the clinical diagnosis of arboviral diseases: A systematic review

Machine learning and deep learning techniques to support the clinical diagnosis of arboviral diseases: A systematic review

A survey on artificial intelligence assurance

Explainable Artificial Intelligence for Tabular Data: A Survey

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