Szczegóły publikacji
Opis bibliograficzny
Security strategy of digital medical contents based on blockchain in generative AI model / Hoon Ko, Marek R. OGIELA // Computers, Materials & Continua ; ISSN 1546-2218. — 2025 — vol. 82 no. 1, s. 259-278. — Bibliogr. s. 278, Abstr. — Publikacja dostępna online od: 2025-01-03
Autorzy (2)
- Ko Hoon
- AGHOgiela Marek
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 162792 |
|---|---|
| Data dodania do BaDAP | 2025-09-29 |
| Tekst źródłowy | URL |
| DOI | 10.32604/cmc.2024.057257 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Computers, Materials & Continua |
Abstract
This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology. By combining the strengths of blockchain and generative AI, the research team aimed to address the timely challenge of safeguarding visual medical content. The participating researchers conducted a comprehensive analysis, examining the vulnerabilities of medical AI services, personal information protection issues, and overall security weaknesses. This multifaceted exploration led to an in-depth evaluation of the model’s performance and security. Notably, the correlation between accuracy, detection rate, and error rate was scrutinized. This analysis revealed insights into the model’s strengths and limitations, while the consideration of standard deviation shed light on the model’s stability and performance variability. The study proposed practical improvements, emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical applications. Applying blockchain to generative AI-created medical content addresses key personal information protection issues. By utilizing the distributed ledger system of blockchain, the research team aimed to protect the privacy and integrity of medical data especially medical images. This approach not only enhances security but also enables transparent and tamper-proof record-keeping. Additionally, the use of generative AI models ensures the creation of novel medical content without compromising personal information, further safeguarding patient privacy. In conclusion, this study showcases the potential of blockchain-based solutions in the medical field, particularly in securing sensitive medical data and protecting patient privacy. The proposed approach, combining blockchain and generative AI, offers a promising direction toward more robust and secure medical content management. Further research and advancements in this area will undoubtedly contribute to the development of robust and privacy-preserving healthcare systems, and visual diagnostic systems.
