A comprehensive evaluation of explainable Artificial Intelligence techniques in stroke diagnosis: A systematic review

Gurmessa, Daraje Kaba; Jimma, Worku

doi:10.1080/23311916.2023.2273088

Cited by 3 publications

(3 citation statements)

References 41 publications

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“…The study proved that the proposed approach yields substantially more expressive and informative results for our specific use case. To avoid issues like limited meaning and confirmation bias due to low-fidelity explanations unnecessarily, Gurmessa and Jimma 8 suggest four metrics based on performance (D, R, F and S), but none of the selected studies used these metrics.…”

Section: Discussionmentioning

confidence: 99%

“…These concepts are considered critical issues by high-level political and technical bodies (eg, G20, EU expert groups, Association of Computing Machinery in the USA). 8 9 …”

Section: Introductionmentioning

confidence: 99%

“…The research that does not consider objective metrics for evaluating XAI may lack significance and experience controversy, especially if negative reviews are not used. 8 To avoid the issues, a study 8 suggests four metrics based on performance differences,

, between the explanation’s logic and the agent’s actual performance, the number of rules,

outputted by the explanation, the number of features,

, used to generate the explanation, and the stability,

, of the explanation. It is believed that user studies that focus on D, R, F and S metrics in their evaluations are inherently more valid.…”

Section: Introductionmentioning

confidence: 99%

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Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review

Gurmessa,

Jimma

2024

BMJ Health Care Inform

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BackgroundBreast cancer is the most common disease in women. Recently, explainable artificial intelligence (XAI) approaches have been dedicated to investigate breast cancer. An overwhelming study has been done on XAI for breast cancer. Therefore, this study aims to review an XAI for breast cancer diagnosis from mammography and ultrasound (US) images. We investigated how XAI methods for breast cancer diagnosis have been evaluated, the existing ethical challenges, research gaps, the XAI used and the relation between the accuracy and explainability of algorithms.MethodsIn this work, Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist and diagram were used. Peer-reviewed articles and conference proceedings from PubMed, IEEE Explore, ScienceDirect, Scopus and Google Scholar databases were searched. There is no stated date limit to filter the papers. The papers were searched on 19 September 2023, using various combinations of the search terms ‘breast cancer’, ‘explainable’, ‘interpretable’, ‘machine learning’, ‘artificial intelligence’ and ‘XAI’. Rayyan online platform detected duplicates, inclusion and exclusion of papers.ResultsThis study identified 14 primary studies employing XAI for breast cancer diagnosis from mammography and US images. Out of the selected 14 studies, only 1 research evaluated humans’ confidence in using the XAI system—additionally, 92.86% of identified papers identified dataset and dataset-related issues as research gaps and future direction. The result showed that further research and evaluation are needed to determine the most effective XAI method for breast cancer.ConclusionXAI is not conceded to increase users’ and doctors’ trust in the system. For the real-world application, effective and systematic evaluation of its trustworthiness in this scenario is lacking.PROSPERO registration numberCRD42023458665.

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Section: Discussionmentioning

confidence: 99%

“…These concepts are considered critical issues by high-level political and technical bodies (eg, G20, EU expert groups, Association of Computing Machinery in the USA). 8 9 …”

Section: Introductionmentioning

confidence: 99%

, between the explanation’s logic and the agent’s actual performance, the number of rules,

outputted by the explanation, the number of features,

, used to generate the explanation, and the stability,

, of the explanation. It is believed that user studies that focus on D, R, F and S metrics in their evaluations are inherently more valid.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review

Gurmessa,

Jimma

2024

BMJ Health Care Inform

Self Cite

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Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction

Mersha,

Lam,

Wood

et al. 2024

Neurocomputing

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Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis

Fernandes,

Cardoso,

Comesaña-Campos

et al. 2024

Sensors

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Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care.

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A comprehensive evaluation of explainable Artificial Intelligence techniques in stroke diagnosis: A systematic review

Cited by 3 publications

References 41 publications

Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review

Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review

Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction

Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis

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