Explainable, trustworthy, and ethical machine learning for healthcare: A survey
<p dir="ltr">With the advent of machine learning (ML) and deep learning (DL) empowered applications for critical applications like healthcare, the questions about liability, trust, and interpretability of their outputs are raising. The black-box nature of various DL models is a roadb...
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2022
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| _version_ | 1864513537534066688 |
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| author | Khansa Rasheed (17380573) |
| author2 | Adnan Qayyum (16875936) Mohammed Ghaly (17380576) Ala Al-Fuqaha (4434340) Adeel Razi (17380579) Junaid Qadir (16494902) |
| author2_role | author author author author author |
| author_facet | Khansa Rasheed (17380573) Adnan Qayyum (16875936) Mohammed Ghaly (17380576) Ala Al-Fuqaha (4434340) Adeel Razi (17380579) Junaid Qadir (16494902) |
| author_role | author |
| dc.creator.none.fl_str_mv | Khansa Rasheed (17380573) Adnan Qayyum (16875936) Mohammed Ghaly (17380576) Ala Al-Fuqaha (4434340) Adeel Razi (17380579) Junaid Qadir (16494902) |
| dc.date.none.fl_str_mv | 2022-10-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.compbiomed.2022.106043 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Explainable_trustworthy_and_ethical_machine_learning_for_healthcare_A_survey/24551488 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Health services and systems Information and computing sciences Machine learning Software engineering Philosophy and religious studies Applied ethics Explainable machine learning Interpretable machine learning Trustworthiness Healthcare |
| dc.title.none.fl_str_mv | Explainable, trustworthy, and ethical machine learning for healthcare: A survey |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">With the advent of machine learning (ML) and deep learning (DL) empowered applications for critical applications like healthcare, the questions about liability, trust, and interpretability of their outputs are raising. The black-box nature of various DL models is a roadblock to clinical utilization. Therefore, to gain the trust of clinicians and patients, we need to provide explanations about the decisions of models. With the promise of enhancing the trust and transparency of black-box models, researchers are in the phase of maturing the field of eXplainable ML (XML). In this paper, we provided a comprehensive review of explainable and interpretable ML techniques for various healthcare applications. Along with highlighting security, safety, and robustness challenges that hinder the trustworthiness of ML, we also discussed the ethical issues arising because of the use of ML/DL for healthcare. We also describe how explainable and trustworthy ML can resolve all these ethical problems. Finally, we elaborate on the limitations of existing approaches and highlight various open research problems that require further development.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers in Biology and Medicine<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compbiomed.2022.106043" target="_blank">https://dx.doi.org/10.1016/j.compbiomed.2022.106043</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_2443f2169843b5f7095d83fd6dc5e434 |
| identifier_str_mv | 10.1016/j.compbiomed.2022.106043 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24551488 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Explainable, trustworthy, and ethical machine learning for healthcare: A surveyKhansa Rasheed (17380573)Adnan Qayyum (16875936)Mohammed Ghaly (17380576)Ala Al-Fuqaha (4434340)Adeel Razi (17380579)Junaid Qadir (16494902)Health sciencesHealth services and systemsInformation and computing sciencesMachine learningSoftware engineeringPhilosophy and religious studiesApplied ethicsExplainable machine learningInterpretable machine learningTrustworthinessHealthcare<p dir="ltr">With the advent of machine learning (ML) and deep learning (DL) empowered applications for critical applications like healthcare, the questions about liability, trust, and interpretability of their outputs are raising. The black-box nature of various DL models is a roadblock to clinical utilization. Therefore, to gain the trust of clinicians and patients, we need to provide explanations about the decisions of models. With the promise of enhancing the trust and transparency of black-box models, researchers are in the phase of maturing the field of eXplainable ML (XML). In this paper, we provided a comprehensive review of explainable and interpretable ML techniques for various healthcare applications. Along with highlighting security, safety, and robustness challenges that hinder the trustworthiness of ML, we also discussed the ethical issues arising because of the use of ML/DL for healthcare. We also describe how explainable and trustworthy ML can resolve all these ethical problems. Finally, we elaborate on the limitations of existing approaches and highlight various open research problems that require further development.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers in Biology and Medicine<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compbiomed.2022.106043" target="_blank">https://dx.doi.org/10.1016/j.compbiomed.2022.106043</a></p>2022-10-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.compbiomed.2022.106043https://figshare.com/articles/journal_contribution/Explainable_trustworthy_and_ethical_machine_learning_for_healthcare_A_survey/24551488CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/245514882022-10-01T00:00:00Z |
| spellingShingle | Explainable, trustworthy, and ethical machine learning for healthcare: A survey Khansa Rasheed (17380573) Health sciences Health services and systems Information and computing sciences Machine learning Software engineering Philosophy and religious studies Applied ethics Explainable machine learning Interpretable machine learning Trustworthiness Healthcare |
| status_str | publishedVersion |
| title | Explainable, trustworthy, and ethical machine learning for healthcare: A survey |
| title_full | Explainable, trustworthy, and ethical machine learning for healthcare: A survey |
| title_fullStr | Explainable, trustworthy, and ethical machine learning for healthcare: A survey |
| title_full_unstemmed | Explainable, trustworthy, and ethical machine learning for healthcare: A survey |
| title_short | Explainable, trustworthy, and ethical machine learning for healthcare: A survey |
| title_sort | Explainable, trustworthy, and ethical machine learning for healthcare: A survey |
| topic | Health sciences Health services and systems Information and computing sciences Machine learning Software engineering Philosophy and religious studies Applied ethics Explainable machine learning Interpretable machine learning Trustworthiness Healthcare |