Synthetic data generation has emerged as a promising solution to overcome the challenges
which are posed by data scarcity and privacy concerns, as well as, to address the need for …

The landscape of prostate cancer care has rapidly evolved. We have transitioned from the
use of conventional imaging, radical surgeries, and single-agent androgen deprivation …

Artificial intelligence models have significant potential to transform cancer care. Efforts are
underway to deploy artificial intelligence models in the cancer practice, evaluate their …

Digital twins, which are in silico replications of an individual and its environment, have
advanced clinical decision-making and prognostication in cardiovascular medicine. The …

Generating realistic synthetic health data (eg, electronic health records), holds promise for
fundamental research, AI model development, and enhancing data privacy safeguards …

Background: Data sharing in healthcare is vital for advancing research and personalized
medicine. However, the process is hindered by privacy, ethical, and legal challenges …

Background Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort
is often a challenging task. Common data models (CDM) can harmonize disparate sources …

The conventional rules for anti-cancer drug development are no longer sufficient given the
relatively limited number of patients available for therapeutic trials. It is thus a real challenge …

Sharing retrospectively acquired data is essential for both clinical research and training.
Synthetic Data Generation (SDG), using Artificial Intelligence (AI) models, can overcome …

2. NHS England. Joint Dental Foundation Core Training (JDFCT). Available at: https://dental.
hee. nhs. uk/dentaltrainee-recruitment/dental-foundation-training/jointdental-foundation-core …