Federated learning and privacy-preserving in healthcare AI:
The use of artificial intelligence (AI) in data-driven medicine has revolutionized healthcare, presenting practitioners with unprecedented tools for diagnosis and personalized therapy. However, this progress comes with a critical concern: the security and privacy of sensitive patient data. As health...
Gespeichert in:
| Weitere Verfasser: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hershey PA, USA
IGI Global
[2024]
|
| Schriftenreihe: | Advances in healthcare information systems and administration (AHISA) book series
|
| Schlagworte: | |
| Online-Zugang: | DE-91 DE-898 DE-1050 Volltext |
| Zusammenfassung: | The use of artificial intelligence (AI) in data-driven medicine has revolutionized healthcare, presenting practitioners with unprecedented tools for diagnosis and personalized therapy. However, this progress comes with a critical concern: the security and privacy of sensitive patient data. As healthcare increasingly leans on AI, the need for robust solutions to safeguard patient information has become more pressing than ever. |
| Beschreibung: | 1 Online-Ressource (xxii, 351 Seiten) Illustrationen |
| ISBN: | 9798369318751 |
| DOI: | 10.4018/979-8-3693-1874-4 |
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| isbn | 9798369318751 |
| language | English |
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| physical | 1 Online-Ressource (xxii, 351 Seiten) Illustrationen |
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| series2 | Advances in healthcare information systems and administration (AHISA) book series |
| spelling | Federated learning and privacy-preserving in healthcare AI Umesh Kumar Lilhore, Sarita Simaiya, Manoharan Poongodi, Vishal Dutt Federated learning and privacy-preserving in healthcare artificial intelligence Hershey PA, USA IGI Global [2024] © 2024 1 Online-Ressource (xxii, 351 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier Advances in healthcare information systems and administration (AHISA) book series The use of artificial intelligence (AI) in data-driven medicine has revolutionized healthcare, presenting practitioners with unprecedented tools for diagnosis and personalized therapy. However, this progress comes with a critical concern: the security and privacy of sensitive patient data. As healthcare increasingly leans on AI, the need for robust solutions to safeguard patient information has become more pressing than ever. Artificial intelligence Machine learning Lilhore, Umesh Kumar 1982- edt Simaiya, Sarita 1986- edt Poongodi, Manoharan 1988- edt Dutt, Vishal edt Erscheint auch als Druck-Ausgabe 979-8-3693-1874-4 Erscheint auch als Druck-Ausgabe 979-8-3693-6652-3 https://doi.org/10.4018/979-8-3693-1874-4 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Federated learning and privacy-preserving in healthcare AI Artificial intelligence Machine learning |
| title | Federated learning and privacy-preserving in healthcare AI |
| title_alt | Federated learning and privacy-preserving in healthcare artificial intelligence |
| title_auth | Federated learning and privacy-preserving in healthcare AI |
| title_exact_search | Federated learning and privacy-preserving in healthcare AI |
| title_full | Federated learning and privacy-preserving in healthcare AI Umesh Kumar Lilhore, Sarita Simaiya, Manoharan Poongodi, Vishal Dutt |
| title_fullStr | Federated learning and privacy-preserving in healthcare AI Umesh Kumar Lilhore, Sarita Simaiya, Manoharan Poongodi, Vishal Dutt |
| title_full_unstemmed | Federated learning and privacy-preserving in healthcare AI Umesh Kumar Lilhore, Sarita Simaiya, Manoharan Poongodi, Vishal Dutt |
| title_short | Federated learning and privacy-preserving in healthcare AI |
| title_sort | federated learning and privacy preserving in healthcare ai |
| topic | Artificial intelligence Machine learning |
| topic_facet | Artificial intelligence Machine learning |
| url | https://doi.org/10.4018/979-8-3693-1874-4 |
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