Explainable AI in Healthcare

Khedkar, Sujata; Subramanian, Vignesh; Shinde, Gayatri; Gandhi, Priyanka

doi:10.2139/ssrn.3367686

Cited by 28 publications

(12 citation statements)

References 6 publications

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“…This will not only help to build trust of AI systems among medical professionals but also unlocks new possibilities in understanding a disease. 146,147 The implementation of precision medicine itself has its own limitations and has drawn criticism due to the lack of a "transformation in therapeutic medicine" in the last two decades. 148 So far, life expectancies or other public health measures have not shown any dramatic improvements, regardless of the vast amounts of precision medicine research being conducted.…”

Section: Other Sites and Diseasesmentioning

confidence: 99%

Radiomics: from qualitative to quantitative imaging

Rogers

Seetha

Lieverse

et al. 2020

The British Journal of Radiology

204

130

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Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms “handcrafted and deep,” is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.

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Section: Other Sites and Diseasesmentioning

confidence: 99%

Radiomics: from qualitative to quantitative imaging

Rogers

Seetha

Lieverse

et al. 2020

The British Journal of Radiology

204

130

View full text Add to dashboard Cite

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“…Here, the user is able to obtain a local explanation for that particular instance. LIME can also be used to obtain explanations for the entire model by generating multiple instances [17]. Methods of explainability are also extended to document classifiers, where documents are classified based on predicted likelihood.…”

Section: Related Workmentioning

confidence: 99%

A Taxonomy of Explainable Bayesian Networks

Derks,

de Waal

2021

Preprint

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Artificial Intelligence (AI), and in particular, the explainability thereof, has gained phenomenal attention over the last few years. Whilst we usually do not question the decision-making process of these systems in situations where only the outcome is of interest, we do however pay close attention when these systems are applied in areas where the decisions directly influence the lives of humans. It is especially noisy and uncertain observations close to the decision boundary which results in predictions which cannot necessarily be explained that may foster mistrust among end-users. This drew attention to AI methods for which the outcomes can be explained. Bayesian networks are probabilistic graphical models that can be used as a tool to manage uncertainty. The probabilistic framework of a Bayesian network allows for explainability in the model, reasoning and evidence. The use of these methods is mostly ad hoc and not as well organised as explainability methods in the wider AI research field. As such, we introduce a taxonomy of explainability in Bayesian networks. We extend the existing categorisation of explainability in the model, reasoning or evidence to include explanation of decisions. The explanations obtained from the explainability methods are illustrated by means of a simple medical diagnostic scenario. The taxonomy introduced in this paper has the potential not only to encourage end-users to efficiently communicate outcomes obtained, but also support their understanding of how and, more importantly, why certain predictions were made.

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“…This adheres to ethical concern and regulatory considerations that need to be made within the domain, should there be bias or discriminatory results. Healthcare is susceptible to such doubts; and XAI methods have been thus developed and applied in e.g., [6], [7] and [8]. It is believed that XAI will provide the much needed trust in human-AI collaboration for critical applications in healthcare.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Explanations Given by Explainable Artificial Intelligence Methods on Analysing Electronic Health Records

Duell

Fan

Burnett

et al. 2021

2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)

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eXplainable Artificial Intelligence (XAI) aims to provide intelligible explanations to users. XAI algorithms such as SHAP, LIME and Scoped Rules compute feature importance for machine learning predictions. Although XAI has attracted much research attention, applying XAI techniques in healthcare to inform clinical decision making is challenging. In this paper, we provide a comparison of explanations given by XAI methods as a tertiary extension in analysing complex Electronic Health Records (EHRs). With a large-scale EHR dataset, we compare features of EHRs in terms of their prediction importance estimated by XAI models. Our experimental results show that the studied XAI methods circumstantially generate different top features; their aberrations in shared feature importance merit further exploration from domain-experts to evaluate human trust towards XAI.

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Explainable AI in Healthcare

Cited by 28 publications

References 6 publications

Radiomics: from qualitative to quantitative imaging

Radiomics: from qualitative to quantitative imaging

A Taxonomy of Explainable Bayesian Networks

A Comparison of Explanations Given by Explainable Artificial Intelligence Methods on Analysing Electronic Health Records

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