Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance
Explainable AI (XAI) is a methodology that complements the black box of artificial intelligence, and its necessity has recently been highlighted in various fields. The purpose of this research is to identify studies in the field of pharmacovigilance using XAI. Though there have been many previous at...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10113317/ |
| _version_ | 1827935739302641664 |
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| author | Seunghee Lee Seonyoung Kim Jieun Lee Jong-Yeup Kim Mi-Hwa Song Suehyun Lee |
| author_facet | Seunghee Lee Seonyoung Kim Jieun Lee Jong-Yeup Kim Mi-Hwa Song Suehyun Lee |
| author_sort | Seunghee Lee |
| collection | DOAJ |
| description | Explainable AI (XAI) is a methodology that complements the black box of artificial intelligence, and its necessity has recently been highlighted in various fields. The purpose of this research is to identify studies in the field of pharmacovigilance using XAI. Though there have been many previous attempts to select papers, with a total of 781 papers being confirmed, only 25 of them manually met the selection criteria. This study presents an intuitive review of the potential of XAI technologies in the field of pharmacovigilance. In the included studies, clinical data, registry data, and knowledge data were used to investigate drug treatment, side effects, and interaction studies based on tree models, neural network models, and graph models. Finally, key challenges for several research issues for the use of XAI in pharmacovigilance were identified. Although artificial intelligence (AI) is actively used in drug surveillance and patient safety, gathering adverse drug reaction information, extracting drug-drug interactions, and predicting effects, XAI is not normally utilized. Therefore, the potential challenges involved in its use alongside future prospects should be continuously discussed. |
| first_indexed | 2024-03-13T07:59:11Z |
| format | Article |
| id | doaj.art-6a7e2b809b2d4b449212f8b9fc74e563 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T07:59:11Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6a7e2b809b2d4b449212f8b9fc74e5632023-06-01T23:00:36ZengIEEEIEEE Access2169-35362023-01-0111508305084010.1109/ACCESS.2023.327163510113317Explainable Artificial Intelligence for Patient Safety: A Review of Application in PharmacovigilanceSeunghee Lee0Seonyoung Kim1Jieun Lee2Jong-Yeup Kim3https://orcid.org/0000-0003-1230-9307Mi-Hwa Song4Suehyun Lee5https://orcid.org/0000-0002-2273-0915Healthcare Data Science Center, Konyang University Hospital, Daejeon, Republic of KoreaHealthcare Data Science Center, Konyang University Hospital, Daejeon, Republic of KoreaHealthcare Data Science Center, Konyang University Hospital, Daejeon, Republic of KoreaHealthcare Data Science Center, Konyang University Hospital, Daejeon, Republic of KoreaSchool of Information and Communication Sciences, Semyung University, Jecheon, Republic of KoreaCollege of IT Convergence, Gachon University, Seongnam, Republic of KoreaExplainable AI (XAI) is a methodology that complements the black box of artificial intelligence, and its necessity has recently been highlighted in various fields. The purpose of this research is to identify studies in the field of pharmacovigilance using XAI. Though there have been many previous attempts to select papers, with a total of 781 papers being confirmed, only 25 of them manually met the selection criteria. This study presents an intuitive review of the potential of XAI technologies in the field of pharmacovigilance. In the included studies, clinical data, registry data, and knowledge data were used to investigate drug treatment, side effects, and interaction studies based on tree models, neural network models, and graph models. Finally, key challenges for several research issues for the use of XAI in pharmacovigilance were identified. Although artificial intelligence (AI) is actively used in drug surveillance and patient safety, gathering adverse drug reaction information, extracting drug-drug interactions, and predicting effects, XAI is not normally utilized. Therefore, the potential challenges involved in its use alongside future prospects should be continuously discussed.https://ieeexplore.ieee.org/document/10113317/Machine learningpharmacovigilanceexplainable artificial intelligence |
| spellingShingle | Seunghee Lee Seonyoung Kim Jieun Lee Jong-Yeup Kim Mi-Hwa Song Suehyun Lee Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance IEEE Access Machine learning pharmacovigilance explainable artificial intelligence |
| title | Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance |
| title_full | Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance |
| title_fullStr | Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance |
| title_full_unstemmed | Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance |
| title_short | Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance |
| title_sort | explainable artificial intelligence for patient safety a review of application in pharmacovigilance |
| topic | Machine learning pharmacovigilance explainable artificial intelligence |
| url | https://ieeexplore.ieee.org/document/10113317/ |
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