Artificial Intelligence-Driven Facial Image Analysis for the Early Detection of Rare Diseases: Legal, Ethical, Forensic, and Cybersecurity Considerations
This narrative review explores the potential, complexities, and consequences of using artificial intelligence (AI) to screen large government-held facial image databases for the early detection of rare genetic diseases. Government-held facial image databases, combined with the power of artificial in...
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MDPI AG
2024-06-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/5/3/49 |
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| author | Peter Kováč Peter Jackuliak Alexandra Bražinová Ivan Varga Michal Aláč Martin Smatana Dušan Lovich Andrej Thurzo |
| author_facet | Peter Kováč Peter Jackuliak Alexandra Bražinová Ivan Varga Michal Aláč Martin Smatana Dušan Lovich Andrej Thurzo |
| author_sort | Peter Kováč |
| collection | DOAJ |
| description | This narrative review explores the potential, complexities, and consequences of using artificial intelligence (AI) to screen large government-held facial image databases for the early detection of rare genetic diseases. Government-held facial image databases, combined with the power of artificial intelligence, offer the potential to revolutionize the early diagnosis of rare genetic diseases. AI-powered phenotyping, as exemplified by the Face2Gene app, enables highly accurate genetic assessments from simple photographs. This and similar breakthrough technologies raise significant privacy and ethical concerns about potential government overreach augmented with the power of AI. This paper explores the concept, methods, and legal complexities of AI-based phenotyping within the EU. It highlights the transformative potential of such tools for public health while emphasizing the critical need to balance innovation with the protection of individual privacy and ethical boundaries. This comprehensive overview underscores the urgent need to develop robust safeguards around individual rights while responsibly utilizing AI’s potential for improved healthcare outcomes, including within a forensic context. Furthermore, the intersection of AI and sensitive genetic data necessitates proactive cybersecurity measures. Current and future developments must focus on securing AI models against attacks, ensuring data integrity, and safeguarding the privacy of individuals within this technological landscape. |
| format | Article |
| id | doaj-art-32125afcc34a433aa43d7b91d77d8c67 |
| institution | OA Journals |
| issn | 2673-2688 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-32125afcc34a433aa43d7b91d77d8c672025-08-20T01:56:09ZengMDPI AGAI2673-26882024-06-0153990101010.3390/ai5030049Artificial Intelligence-Driven Facial Image Analysis for the Early Detection of Rare Diseases: Legal, Ethical, Forensic, and Cybersecurity ConsiderationsPeter Kováč0Peter Jackuliak1Alexandra Bražinová2Ivan Varga3Michal Aláč4Martin Smatana5Dušan Lovich6Andrej Thurzo7Institute of Forensic Medicine, Faculty of Medicine, Comenius University in Bratislava, Sasinkova 4, 81108 Bratislava, Slovakia5th Department of Internal Medicine, Faculty of Medicine, Comenius University in Bratislava, University Hospital, Ružinovská 6, 82606 Bratislava, SlovakiaInstitute of Epidemiology, Faculty of Medicine, Comenius University in Bratislava, Špitálska 24, 81372 Bratislava, SlovakiaInstitute of Histology and Embryology, Faculty of Medicine, Comenius University in Bratislava, Sasinkova 4, 81104 Bratislava, SlovakiaDepartment of Administrative Law, Faculty of Law, Trnava University, Kollárova 545/10, 91701 Trnava, SlovakiaFaculty of Nursing and Health Professional Studies, Slovak Medical University, Limbová 12, 83303 Bratislava, SlovakiaInstitute of Public Law—Department of Criminal Law, Criminalistics and Criminology, Faculty of Law, The Pan-European University, Tomášikova 20, 82102 Bratislava, Slovakiaforensic.sk Institute of Forensic Medical Expertises Ltd., Boženy Němcovej 1004/8, 81104 Bratislava, SlovakiaThis narrative review explores the potential, complexities, and consequences of using artificial intelligence (AI) to screen large government-held facial image databases for the early detection of rare genetic diseases. Government-held facial image databases, combined with the power of artificial intelligence, offer the potential to revolutionize the early diagnosis of rare genetic diseases. AI-powered phenotyping, as exemplified by the Face2Gene app, enables highly accurate genetic assessments from simple photographs. This and similar breakthrough technologies raise significant privacy and ethical concerns about potential government overreach augmented with the power of AI. This paper explores the concept, methods, and legal complexities of AI-based phenotyping within the EU. It highlights the transformative potential of such tools for public health while emphasizing the critical need to balance innovation with the protection of individual privacy and ethical boundaries. This comprehensive overview underscores the urgent need to develop robust safeguards around individual rights while responsibly utilizing AI’s potential for improved healthcare outcomes, including within a forensic context. Furthermore, the intersection of AI and sensitive genetic data necessitates proactive cybersecurity measures. Current and future developments must focus on securing AI models against attacks, ensuring data integrity, and safeguarding the privacy of individuals within this technological landscape.https://www.mdpi.com/2673-2688/5/3/49artificial intelligencecybersecurityforensicsbig datagenetic diseasesgenetic privacy |
| spellingShingle | Peter Kováč Peter Jackuliak Alexandra Bražinová Ivan Varga Michal Aláč Martin Smatana Dušan Lovich Andrej Thurzo Artificial Intelligence-Driven Facial Image Analysis for the Early Detection of Rare Diseases: Legal, Ethical, Forensic, and Cybersecurity Considerations AI artificial intelligence cybersecurity forensics big data genetic diseases genetic privacy |
| title | Artificial Intelligence-Driven Facial Image Analysis for the Early Detection of Rare Diseases: Legal, Ethical, Forensic, and Cybersecurity Considerations |
| title_full | Artificial Intelligence-Driven Facial Image Analysis for the Early Detection of Rare Diseases: Legal, Ethical, Forensic, and Cybersecurity Considerations |
| title_fullStr | Artificial Intelligence-Driven Facial Image Analysis for the Early Detection of Rare Diseases: Legal, Ethical, Forensic, and Cybersecurity Considerations |
| title_full_unstemmed | Artificial Intelligence-Driven Facial Image Analysis for the Early Detection of Rare Diseases: Legal, Ethical, Forensic, and Cybersecurity Considerations |
| title_short | Artificial Intelligence-Driven Facial Image Analysis for the Early Detection of Rare Diseases: Legal, Ethical, Forensic, and Cybersecurity Considerations |
| title_sort | artificial intelligence driven facial image analysis for the early detection of rare diseases legal ethical forensic and cybersecurity considerations |
| topic | artificial intelligence cybersecurity forensics big data genetic diseases genetic privacy |
| url | https://www.mdpi.com/2673-2688/5/3/49 |
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