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mHealth Systems Need a Privacy-by-Design Approach: Commentary on “Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review”

https://doi.org/10.2196/46700
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Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1146/annurev-statistics-033121-112921openalex-snapshot
Statistical Data Privacy: A Song of Privacy and Utility
inferredcites →https://doi.org/10.2196/23728openalex-snapshot
Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data
inferredcites →https://doi.org/10.2196/41588openalex-snapshot
Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review
inferredcites →https://doi.org/10.2196/43664openalex-snapshot
Exploring the Relationship Between Privacy and Utility in Mobile Health: Algorithm Development and Validation via Simulations of Federated Learning, Differential Privacy, and External Attacks
inferredcites →https://doi.org/10.29012/jpc.689openalex-snapshot
Differential Privacy in Practice: Expose your Epsilons!
inferredcites →https://doi.org/10.48550/arxiv.2302.05552openalex-snapshot
Algorithmically Effective Differentially Private Synthetic Data

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-031-60433-1_14openalex-snapshot
Integrating Contextual Integrity in Privacy Requirements Engineering: A Study Case in Personal E-Health Applications
inferred← citeshttps://doi.org/10.1007/978-3-031-99474-6_32openalex-snapshot
Privacy-Preserving Healthcare Analytics: A Hybrid Approach Using AES-256-CBC Encryption and Differential Privacy in Mobile Applications
inferred← citeshttps://doi.org/10.1007/s00330-026-12460-4openalex-snapshot
Artificial intelligence as medical device in radiology in 2025: the regulatory scenario in the EU, USA, and China
inferred← citeshttps://doi.org/10.1016/j.health.2025.100439openalex-snapshot
An image-based analytics framework for early autism detection using eye movements
inferred← citeshttps://doi.org/10.1038/s41598-023-36798-7openalex-snapshot
Initial study on an expert system for spine diseases screening using inertial measurement unit
inferred← citeshttps://doi.org/10.1038/s41746-023-00927-3openalex-snapshot
Harnessing the power of synthetic data in healthcare: innovation, application, and privacy
inferred← citeshttps://doi.org/10.1109/isaeece66033.2025.11160137openalex-snapshot
Offshore wind power capacity prediction based on integrated optimization model
inferred← citeshttps://doi.org/10.1109/tnse.2025.3637602openalex-snapshot
LVFUS: Vertical Federated Unlearning for Intelligent Network Security via Adaptive Optimizer Switching
inferred← citeshttps://doi.org/10.1145/3708036.3708246openalex-snapshot
Stock trend prediction based on multi-factor screening machine
inferred← citeshttps://doi.org/10.21203/rs.3.rs-8477605/v1openalex-snapshot
A Closed-Loop Intelligent Management System for Postoperative Orthopedic Rehabilitation: Integrating Deep Reinforcement Learning with Wearable Sensing and Prognostic Prediction
inferred← citeshttps://doi.org/10.2196/preprints.109786openalex-snapshot
Post Hoc Human Review of a Large Language Model–Generated Benchmark for Hospital Wayfinding Search: Comparative Evaluation Study (Preprint)
inferred← citeshttps://doi.org/10.3389/fpubh.2025.1633487openalex-snapshot
Applications and challenges of biomarker-based predictive models in proactive health management
inferred← citeshttps://doi.org/10.3389/fresc.2026.1822939openalex-snapshot
Closed-Loop digital therapeutics empowered by deep reinforcement learning and wearable sensing for precision orthopedic rehabilitation: a simulation-based proof-of-concept study
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