Federated AI, Health Data Interoperability, and Digital Twins in Africa: A Framework for Privacy-Preserving Precision Healthcare in Resource-Limited Settings
Christianah Oluwatosin Agboola1, Micheal Abimbola Oladosu*2, Moses Adondua Abah3, Sulaimon Olajuwon Abdul4, Oluwatobiloba Adetunji Abe5, Olaide Ayokunmi Oladosu6
1Christianah Oluwatosin Agboola, Department of Biomolecular Science, Central Connecticut State University, School of Engineering Science and Technology, Connecticut (Connecticut), United States of America (USA).
2Micheal Abimbola Oladosu, Department of Chemical Sciences, Anchor University, Lagos, Faculty of Science, Lagos, Nigeria.
3Moses Adondua Abah, Department of Biochemistry, Federal University Wukari, Faculty of Pure and Applied Sciences, Wukari (Taraba), Nigeria.
4Sulaimon Olajuwon Abdul, Department of Physics, Astronomy, and Mathematics, University of Hertfordshire, School of Physics, Engineering and Computer Science, College Lane, (Hatfield), United Kingdom.
5Oluwatobi Adetunji Abe, Department of Biological Sciences, Anchor University, Ayobo, Lagos, Nigeria.
6Olaide Ayokunmi Oladosu, Department of Computer Science, Anchor University, Lagos, Faculty of Science and Technology, Ilisan-Remo (Ogun), Nigeria.
Manuscript received on 02 August 2026 | First Revised Manuscript received on 14 August 2026 | Second Revised Manuscript received on 03 September 2026 | Manuscript Accepted on 15 September 2026 | Manuscript published on 30 September 2026 | PP: 23-28 | Volume-6 Issue-6 September 2026 | Retrieval Number: 100.1/ijpmh.G117807011126 | DOI: 10.54105/ijpmh.G1178.07011126
Open Access | Ethics and Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: While Africa has a disproportionately high share of the global disease burden, it is also plagued by poor connectivity, a lack of genomic reference data, and diverse digital and network infrastructure that impede the continent’s shift toward precision healthcare. To work around these constraints, three converging technologies offer a way forward: federated artificial intelligence (federated learning), which allows for model training to be done collaboratively acrossinstitutions without sharing sensitive patient data; health data interoperability standards, which make it possible to exchange health information from heterogeneous electronic health record (EHR) and mobile-health systems; and digital twins, dynamically updated virtual patients or virtual population models for simulation-based health-related decisionmaking. This narrative review collates literature published from 2020–2025 on these three technologies in Africa and other resource-constrained environments, including federated-learning pilots for tuberculosis and foetal-ultrasound screening, continentwide scoping of interoperability, and early digital-twin architectures proposed for low-resource African health systems. We propose an integrated, layered structure that connects local federated-learning nodes, an interoperable semantic data layer, and a regional digital-twin simulation layer, linked by privacypreserving mechanisms and Africa-specific data-governance safeguards. The paper discusses obstacles and limitations to cross-border data transfer, such as weak institutional trust in data sharing, algorithmic bias from non-representative training sets, and unreliable connections, as well as measures being taken to overcome them. In conclusion, federated AI, interoperability, and digital twins are all promising and essential for achieving privacypreserving precision healthcare at scale in Africa.
Keywords: Federated Learning, Artificial Intelligence, Health Data Interoperability, Digital Twin, Precision Medicine, Africa, Resource-Limited Settings and Data Privacy.
Scope of the Article: Health Care Management