This paper is in the following e-collection/theme issue:

Ethics, Privacy, and Legal Issues (485) Mobile Health (mhealth) (2971) Digital Health Reviews (1309) Data Science (276) Clinical Informatics (1042) E-Health Policy and Health Systems Innovation (239) Policy and Policy Proposals (126)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41588, first published .
Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review

Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review

Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review

Authors of this article:

Alissa Brauneck1 Author Orcid Image ;   Louisa Schmalhorst1 Author Orcid Image ;   Mohammad Mahdi Kazemi Majdabadi2 Author Orcid Image ;   Mohammad Bakhtiari2 Author Orcid Image ;   Uwe Völker3 Author Orcid Image ;   Jan Baumbach2, 4 Author Orcid Image ;   Linda Baumbach5 Author Orcid Image ;   Gabriele Buchholtz1 Author Orcid Image
Article Authors Cited by (30) Tweetations (5) Metrics

Journals

  1. Ritahani Ismail A, Mohd Khalili A, Adilah Rahim N, Nisa S. Deep Convolutional Generative Adversarial Networks for Imbalance Medical Image Classification.. International Journal on Perceptive and Cognitive Computing 2023;9(2):98 View
  2. Garcia Santa Cruz B, Husch A, Hertel F. Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions. Frontiers in Aging Neuroscience 2023;15 View
  3. Liang X, Zhao J, Chen Y, Bandara E, Shetty S. Architectural Design of a Blockchain-Enabled, Federated Learning Platform for Algorithmic Fairness in Predictive Health Care: Design Science Study. Journal of Medical Internet Research 2023;25:e46547 View
  4. Pelc M, Vilimkova Kahankova R, Blaszczyszyn M, Mikolajewski D, Konieczny M, Khoma V, Bara G, Zygarlicki J, Martinek R, Gupta M, Gorzelanczyk E, Pawłowski M, Czapiga B, Zygarlicka M, Kawala-Sterniuk A. Initial study on an expert system for spine diseases screening using inertial measurement unit. Scientific Reports 2023;13(1) View
  5. Guan B, Yu L, Li Y, Jia Z, Jin Z. Assessment of patients with Parkinson’s disease based on federated learning. International Journal of Machine Learning and Cybernetics 2024;15(4):1621 View
  6. Giuffrè M, Shung D. Harnessing the power of synthetic data in healthcare: innovation, application, and privacy. npj Digital Medicine 2023;6(1) View
  7. Singh A, Bansod G, Mahajan M, Dietrich P, Singh S, Rav K, Thissen A, Bharde A, Rothenstein D, Kulkarni S, Bill J. Digital Transformation in Toxicology: Improving Communication and Efficiency in Risk Assessment. ACS Omega 2023;8(24):21377 View
  8. Shao L, Chen B, Zhang Z, Zhang Z, Chen X. Artificial intelligence generated content (AIGC) in medicine: A narrative review. Mathematical Biosciences and Engineering 2024;21(1):1672 View
  9. Kisten M, Ezugwu A, Olusanya M. Explainable Artificial Intelligence Model for Predictive Maintenance in Smart Agricultural Facilities. IEEE Access 2024;12:24348 View
  10. Meiser M, Zinnikus I. A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities. Energies 2024;17(9):1992 View
  11. Saeedi M, Gorji H, Vasefi F, Tavakolian K. Federated Versus Central Machine Learning on Diabetic Foot Ulcer Images: Comparative Simulations. IEEE Access 2024;12:58960 View
  12. Brauneck A, Schmalhorst L, Weiss S, Baumbach L, Völker U, Ellinghaus D, Baumbach J, Buchholtz G. Legal aspects of privacy-enhancing technologies in genome-wide association studies and their impact on performance and feasibility. Genome Biology 2024;25(1) View
  13. Zoerle T, Beqiri E, Åkerlund C, Gao G, Heldt T, Hawryluk G, Stocchetti N. Intracranial pressure monitoring in adult patients with traumatic brain injury: challenges and innovations. The Lancet Neurology 2024;23(9):938 View
  14. Tajabadi M, Martin R, Heider D. Privacy-Preserving Decentralized Learning Methods for Biomedical Applications. Computational and Structural Biotechnology Journal 2024 View
  15. Mandal P. A distributed privacy preserving model for the detection of Alzheimer’s disease. Neural Computing and Applications 2024;36(36):22719 View
  16. Souza J, Oliveira S, Oliveira H. The Impact of Federated Learning on Urban Computing. Journal of Internet Services and Applications 2024;15(1):380 View
  17. Singhal S, Cooke D, Villareal R, Stoddard J, Lin C, Dempsey A. Machine Learning for Mental Health: Applications, Challenges, and the Clinician's Role. Current Psychiatry Reports 2024 View
  18. Bhanbhro J, Nisticò S, Palopoli L. Issues in federated learning: some experiments and preliminary results. Scientific Reports 2024;14(1) View
  19. Süwer S, Ullah M, Probul N, Maier A, Baumbach J. Privacy-by-Design with Federated Learning will drive future Rare Disease Research. Journal of Neuromuscular Diseases 2024 View

Books/Policy Documents

  1. Sindiramutty S, Jhanjhi N, Tan C, Yun K, Manchuri A, Ashraf H, Murugesan R, Tee W, Hussain M. Cybersecurity Issues and Challenges in the Drone Industry. View
  2. Ajalkar D, Sharma Y, Shinde J, Nayak S. Applying Machine Learning Techniques to Bioinformatics. View
  3. Kumar M, Kumar A, Bhargava M, Singh R, Shukla A, Shukla V. Cryptology and Network Security with Machine Learning. View
  4. Swathi N, Kavitha S, Karpakavalli M. Improving Healthcare Quality and Patient Engagement. View
  5. Li L. Federated Learning - A Systematic Review [Working Title]. View