Privacy-preserving artificial intelligence in healthcare: Techniques and applications
<p>There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very few AI-based applications have successful...
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2023
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| _version_ | 1864513529557549056 |
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| author | Nazish Khalid (17685063) |
| author2 | Adnan Qayyum (16875936) Muhammad Bilal (737265) Ala Al-Fuqaha (4434340) Junaid Qadir (16494902) |
| author2_role | author author author author |
| author_facet | Nazish Khalid (17685063) Adnan Qayyum (16875936) Muhammad Bilal (737265) Ala Al-Fuqaha (4434340) Junaid Qadir (16494902) |
| author_role | author |
| dc.creator.none.fl_str_mv | Nazish Khalid (17685063) Adnan Qayyum (16875936) Muhammad Bilal (737265) Ala Al-Fuqaha (4434340) Junaid Qadir (16494902) |
| dc.date.none.fl_str_mv | 2023-05-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.compbiomed.2023.106848 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Privacy-preserving_artificial_intelligence_in_healthcare_Techniques_and_applications/25018835 |
| 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 Artificial intelligence Cybersecurity and privacy Privacy Privacy preservation Electronic health record (EHR) Artificial intelligence (AI) |
| dc.title.none.fl_str_mv | Privacy-preserving artificial intelligence in healthcare: Techniques and applications |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very few AI-based applications have successfully made it to clinics. Key barriers to the widespread adoption of clinically validated AI applications include non-standardized medical records, limited availability of curated datasets, and stringent legal/ethical requirements to preserve patients’ privacy. Therefore, there is a pressing need to improvise new data-sharing methods in the age of AI that preserve patient privacy while developing AI-based healthcare applications. In the literature, significant attention has been devoted to developing privacy-preserving techniques and overcoming the issues hampering AI adoption in an actual clinical environment. To this end, this study summarizes the state-of-the-art approaches for preserving privacy in AI-based healthcare applications. Prominent privacy-preserving techniques such as Federated Learning and Hybrid Techniques are elaborated along with potential privacy attacks, security challenges, and future directions.</p><h2>Other Information</h2> <p> 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.2023.106848" target="_blank">https://dx.doi.org/10.1016/j.compbiomed.2023.106848</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_36b1d081fd245f347e8bd09046737997 |
| identifier_str_mv | 10.1016/j.compbiomed.2023.106848 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25018835 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Privacy-preserving artificial intelligence in healthcare: Techniques and applicationsNazish Khalid (17685063)Adnan Qayyum (16875936)Muhammad Bilal (737265)Ala Al-Fuqaha (4434340)Junaid Qadir (16494902)Health sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceCybersecurity and privacyPrivacyPrivacy preservationElectronic health record (EHR)Artificial intelligence (AI)<p>There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very few AI-based applications have successfully made it to clinics. Key barriers to the widespread adoption of clinically validated AI applications include non-standardized medical records, limited availability of curated datasets, and stringent legal/ethical requirements to preserve patients’ privacy. Therefore, there is a pressing need to improvise new data-sharing methods in the age of AI that preserve patient privacy while developing AI-based healthcare applications. In the literature, significant attention has been devoted to developing privacy-preserving techniques and overcoming the issues hampering AI adoption in an actual clinical environment. To this end, this study summarizes the state-of-the-art approaches for preserving privacy in AI-based healthcare applications. Prominent privacy-preserving techniques such as Federated Learning and Hybrid Techniques are elaborated along with potential privacy attacks, security challenges, and future directions.</p><h2>Other Information</h2> <p> 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.2023.106848" target="_blank">https://dx.doi.org/10.1016/j.compbiomed.2023.106848</a></p>2023-05-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.compbiomed.2023.106848https://figshare.com/articles/journal_contribution/Privacy-preserving_artificial_intelligence_in_healthcare_Techniques_and_applications/25018835CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/250188352023-05-01T00:00:00Z |
| spellingShingle | Privacy-preserving artificial intelligence in healthcare: Techniques and applications Nazish Khalid (17685063) Health sciences Health services and systems Information and computing sciences Artificial intelligence Cybersecurity and privacy Privacy Privacy preservation Electronic health record (EHR) Artificial intelligence (AI) |
| status_str | publishedVersion |
| title | Privacy-preserving artificial intelligence in healthcare: Techniques and applications |
| title_full | Privacy-preserving artificial intelligence in healthcare: Techniques and applications |
| title_fullStr | Privacy-preserving artificial intelligence in healthcare: Techniques and applications |
| title_full_unstemmed | Privacy-preserving artificial intelligence in healthcare: Techniques and applications |
| title_short | Privacy-preserving artificial intelligence in healthcare: Techniques and applications |
| title_sort | Privacy-preserving artificial intelligence in healthcare: Techniques and applications |
| topic | Health sciences Health services and systems Information and computing sciences Artificial intelligence Cybersecurity and privacy Privacy Privacy preservation Electronic health record (EHR) Artificial intelligence (AI) |