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"

Harnessing the power of synthetic data in healthcare: innovation, application, and privacy

Data-driven decision-making in modern healthcare underpins innovation and predictive analytics in public health and clinical research. Synthetic data has shown promise in finance and economics to improve risk assessment, portfolio optimization, and algorithmic trading. However, higher stakes, potential liabilities, and healthcare practitioner distrust make clinical use of synthetic data difficult. This paper explores the potential benefits and limitations of synthetic data in the healthcare analytics context. We begin with real-world healthcare applications of synthetic data that informs government policy, enhance data privacy, and augment datasets for predictive analytics. We then preview future applications of synthetic data in the emergent field of digital twin technology. We explore the issues of data quality and data bias in synthetic data, which can limit applicability across different applications in the clinical context, and privacy concerns stemming from data misuse and risk of re-identification. Finally, we evaluate the role of regulatory agencies in promoting transparency and accountability and propose strategies for risk mitigation such as Differential Privacy (DP) and a dataset chain of custody to maintain data integrity, traceability, and accountability. Synthetic data can improve healthcare, but measures to protect patient well-being and maintain ethical standards are key to promote responsible use.

Initial study on an expert system for spine diseases screening using inertial measurement unit

In recent times, widely understood spine diseases have advanced to one of the most urgetn problems where quick diagnosis and treatment are needed. To diagnose its specifics (e.g. to decide whether this is a scoliosis or sagittal imbalance) and assess its extend, various kind of imaging diagnostic methods (such as X-Ray, CT, MRI scan or ST) are used. However, despite their common use, some may be regarded as (to a level) invasive methods and there are cases where there are contraindications to using them. Besides, which is even more of a problem, these are very expensive methods and whilst their use for pure diagnostic purposes is absolutely valid, then due to their cost, they cannot rather be considered as tools which would be equally valid for bad posture screening programs purposes. This paper provides an initial evaluation of the alternative approach to the spine diseases diagnostic/screening using inertial measurement unit and we propose policy-based computing as the core for the inference systems. Although the methodology presented herein is potentially applicable to a variety of spine diseases, in the nearest future we will focus specifically on sagittal imbalance detection.