Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning.

IoT blockchain data storage optimization decentralized applications health system and access homomorphic encryption lightweight authentication permissions-based system smart city

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
07 Sep 2023
Historique:
received: 12 07 2023
revised: 23 08 2023
accepted: 24 08 2023
medline: 4 10 2023
pubmed: 28 9 2023
entrez: 28 9 2023
Statut: epublish

Résumé

The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.

Identifiants

pubmed: 37765797
pii: s23187740
doi: 10.3390/s23187740
pmc: PMC10537957
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Références

Health Informatics J. 2019 Dec;25(4):1398-1411
pubmed: 29692204
Sensors (Basel). 2020 Sep 07;20(18):
pubmed: 32906707
Sensors (Basel). 2022 Jan 12;22(2):
pubmed: 35062530
Sensors (Basel). 2022 Feb 13;22(4):
pubmed: 35214350
Sensors (Basel). 2018 Dec 01;18(12):
pubmed: 30513733
Sensors (Basel). 2023 Aug 28;23(17):
pubmed: 37687931
Sensors (Basel). 2019 Dec 25;20(1):
pubmed: 31881766
Sensors (Basel). 2019 May 14;19(10):
pubmed: 31091799
Sensors (Basel). 2021 Jan 24;21(3):
pubmed: 33498860
Sensors (Basel). 2023 Jul 28;23(15):
pubmed: 37571545
Sensors (Basel). 2019 Jan 15;19(2):
pubmed: 30650612
J Med Syst. 2018 Jun 21;42(8):136
pubmed: 29931655

Auteurs

Aitizaz Ali (A)

School of IT, UNITAR International University, Petaling Jaya 47301, Malaysia.

Hashim Ali (H)

Department of Computer System, Abdul Wali Khan University Mardan (AWKUM), Mardan 23200, Pakistan.

Aamir Saeed (A)

Department of Computer Science and IT, Jalozai Campus, UET Peshawar, Peshawar 25000, Pakistan.

Aftab Ahmed Khan (A)

Department of Computer Science, Abdul Wali Khan University Mardan (AWKUM), Mardan 23200, Pakistan.

Ting Tin Tin (TT)

Faculty of Data Science and Information Technology, INTI International University, Nilai 71800, Malaysia.

Muhammad Assam (M)

Department of Software Engineering, University of Science and Technology Bannu, Bannu 28100, Pakistan.

Yazeed Yasin Ghadi (YY)

Department of Computer Science and Software Engineering, Al Ain University, Abu Dhabi 122612, United Arab Emirates.

Heba G Mohamed (HG)

Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH