Evidence map›Paper›PMID 37765797›Full record

ArticleSensors (Basel, Switzerland)2023

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

Aitizaz Ali, Hashim Ali, Aamir Saeed, Aftab Ahmed Khan, Ting Tin Tin, Muhammad Assam, Yazeed Yasin Ghadi, Heba G Mohamed

RetractedAbstract readRetracted Publication
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Blockchain in Health Information Systems: A Systematic Review.International journal of environmental research and public health · 2024
    Pooled it
  3. Article
  4. Review
  5. Review
  6. Blockchain in Cancer Care: Toward Evidence-Based and Inclusive Practice.Asian Pacific journal of cancer prevention : APJCP · 2026
    Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Enhancing Cancer Care through Blockchain Technology.Asian Pacific journal of cancer prevention : APJCP · 2025
    Review
  17. Article
  18. Review
  19. Review
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Aitizaz AliSchool of IT, UNITAR International University, Petaling Jaya 47301, Malaysia.
Hashim AliDepartment of Computer System, Abdul Wali Khan University Mardan (AWKUM), Mardan 23200, Pakistan.ORCID 0000-0003-0239-8000
Aamir SaeedDepartment of Computer Science and IT, Jalozai Campus, UET Peshawar, Peshawar 25000, Pakistan.
Aftab Ahmed KhanDepartment of Computer Science, Abdul Wali Khan University Mardan (AWKUM), Mardan 23200, Pakistan.
Ting Tin TinFaculty of Data Science and Information Technology, INTI International University, Nilai 71800, Malaysia.
Muhammad AssamDepartment of Software Engineering, University of Science and Technology Bannu, Bannu 28100, Pakistan.
Yazeed Yasin GhadiDepartment of Computer Science and Software Engineering, Al Ain University, Abu Dhabi 122612, United Arab Emirates.ORCID 0000-0002-7121-495X
Heba G MohamedDepartment of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.ORCID 0000-0002-0443-1049

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

BlockchainDeep LearningComputer SecurityDelivery of Health CareEcosystemElectronic Health RecordsHumansblockchaindata storage optimizationdecentralized applicationshealth system and accesshomomorphic encryptionIoTlightweight authenticationpermissions-based systemsmart city

Identifiers

PMID37765797
PMCPMC10537957

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.