A Novel Taxonomy for Navigating and Classifying Synthetic Data in Healthcare Applications.

Data-driven technologies have improved the efficiency, reliability and effectiveness of healthcare services, but come with an increasing demand for data, which is challenging due to privacy-related constraints on sharing data in healthcare contexts. Synthetic data has recently gained popularity as potential solution, but in the flurry of current research it can be hard to oversee its potential. This paper proposes a novel taxonomy of synthetic data in healthcare to navigate the landscape in terms of three main varieties.

Local administration of immunotherapy for patients with skin cancer: A systematic review.

Since the introduction of immune checkpoint inhibitors (ICIs) targeting PD-1 and CTLA-4 receptors, survival has improved significantly for patients with irresectable and metastatic skin cancer, including cutaneous squamous cell cancer and melanoma. However, systemic administration of these drugs is associated with immune related adverse events (irAEs), which can be severe, irreversible and even fatal. To reduce the risk of irAEs associated with systemic exposure to immunotherapeutic drugs, local administration of low doses could be considered.

Sinonasal Squamous Cell Carcinoma in the Netherlands: A Population-Based Study of 568 Cases Diagnosed in 2008-2021.

Background: Sinonasal squamous cell carcinoma is known for its rarity and poor prognosis. This study aims to investigate the trends in the incidence of sinonasal squamous cell carcinoma in the Netherlands and to analyze patient characteristics and treatment practices in order to assess their relation with patient outcomes.

Methods: In this prospectively designed population-based cohort study, all patients diagnosed with sinonasal squamous cell carcinoma between 2008 and 2021 in the Netherlands were included.

INSAFEDARE Project: Innovative Applications of Assessment and Assurance of Data and Synthetic Data for Regulatory Decision Support.

Digital health solutions hold promise for enhancing healthcare delivery and patient outcomes, primarily driven by advancements such as machine learning, artificial intelligence, and data science, which enable the development of integrated care systems. Techniques for generating synthetic data from real datasets are highly advanced and continually evolving. This paper aims to present the INSAFEDARE project's ambition regarding medical devices' regulation and how real and synthetic data can be used to check if devices are safe and effective.