XAI Unveiled: Revealing the Potential of Explainable AI in Medicine: A Systematic Review
Nowadays, artificial intelligence in medicine plays a leading role. This necessitates the need to ensure that artificial intelligence systems are not only high-performing but also comprehensible to all stakeholders involved, including doctors, patients, healthcare providers, etc. As a result, the ex...
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10787012/ |
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| author | Noemi Scarpato Patrizia Ferroni Fiorella Guadagni |
| author_facet | Noemi Scarpato Patrizia Ferroni Fiorella Guadagni |
| author_sort | Noemi Scarpato |
| collection | DOAJ |
| description | Nowadays, artificial intelligence in medicine plays a leading role. This necessitates the need to ensure that artificial intelligence systems are not only high-performing but also comprehensible to all stakeholders involved, including doctors, patients, healthcare providers, etc. As a result, the explainability of artificial intelligence systems has become a widely discussed subject in recent times, leading to the publication of numerous approaches and solutions. In this paper, we aimed to provide a systematic review of these approaches in order to analyze their role in making artificial intelligence interpretable for everyone. The conducted review was carried out in accordance with the PRISMA statement. We conducted a BIAS analysis, identifying 87 scientific papers from those retrieved as having a low risk of BIAS. Subsequently, we defined a classification framework based on the classification taxonomy and applied it to analyze these papers. The results show that, although most AI approaches in medicine currently incorporate explainability methods, the evaluation of these systems is not always performed. When evaluation does occur, it is most often focused on improving the system itself rather than assessing users’ perception of the system’s effectiveness. To address these limitations, we propose a framework for evaluating explainability approaches in medicine, aimed at guiding developers in designing effective human-centered methods. |
| format | Article |
| id | doaj-art-c4f3a2fdff01413d81fce498e9ae4e85 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c4f3a2fdff01413d81fce498e9ae4e852025-08-20T02:40:13ZengIEEEIEEE Access2169-35362024-01-011219149819151610.1109/ACCESS.2024.351419710787012XAI Unveiled: Revealing the Potential of Explainable AI in Medicine: A Systematic ReviewNoemi Scarpato0https://orcid.org/0000-0002-6573-8095Patrizia Ferroni1https://orcid.org/0000-0002-9877-8712Fiorella Guadagni2https://orcid.org/0000-0003-3652-0457Department of Promotion of Human Sciences and Quality of Life, San Raffaele Roma Open University, Rome, ItalyDepartment of Promotion of Human Sciences and Quality of Life, San Raffaele Roma Open University, Rome, ItalyDepartment of Promotion of Human Sciences and Quality of Life, San Raffaele Roma Open University, Rome, ItalyNowadays, artificial intelligence in medicine plays a leading role. This necessitates the need to ensure that artificial intelligence systems are not only high-performing but also comprehensible to all stakeholders involved, including doctors, patients, healthcare providers, etc. As a result, the explainability of artificial intelligence systems has become a widely discussed subject in recent times, leading to the publication of numerous approaches and solutions. In this paper, we aimed to provide a systematic review of these approaches in order to analyze their role in making artificial intelligence interpretable for everyone. The conducted review was carried out in accordance with the PRISMA statement. We conducted a BIAS analysis, identifying 87 scientific papers from those retrieved as having a low risk of BIAS. Subsequently, we defined a classification framework based on the classification taxonomy and applied it to analyze these papers. The results show that, although most AI approaches in medicine currently incorporate explainability methods, the evaluation of these systems is not always performed. When evaluation does occur, it is most often focused on improving the system itself rather than assessing users’ perception of the system’s effectiveness. To address these limitations, we propose a framework for evaluating explainability approaches in medicine, aimed at guiding developers in designing effective human-centered methods.https://ieeexplore.ieee.org/document/10787012/Explainabilityartificial intelligenceinterpretabilitymedicineexplainable artificial intelligenceinterpretable artificial intelligence |
| spellingShingle | Noemi Scarpato Patrizia Ferroni Fiorella Guadagni XAI Unveiled: Revealing the Potential of Explainable AI in Medicine: A Systematic Review IEEE Access Explainability artificial intelligence interpretability medicine explainable artificial intelligence interpretable artificial intelligence |
| title | XAI Unveiled: Revealing the Potential of Explainable AI in Medicine: A Systematic Review |
| title_full | XAI Unveiled: Revealing the Potential of Explainable AI in Medicine: A Systematic Review |
| title_fullStr | XAI Unveiled: Revealing the Potential of Explainable AI in Medicine: A Systematic Review |
| title_full_unstemmed | XAI Unveiled: Revealing the Potential of Explainable AI in Medicine: A Systematic Review |
| title_short | XAI Unveiled: Revealing the Potential of Explainable AI in Medicine: A Systematic Review |
| title_sort | xai unveiled revealing the potential of explainable ai in medicine a systematic review |
| topic | Explainability artificial intelligence interpretability medicine explainable artificial intelligence interpretable artificial intelligence |
| url | https://ieeexplore.ieee.org/document/10787012/ |
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