Synthetic data generation: challenges and perspectives for gastrointestinal medicine

In the era of artificial intelligence, machines are demonstrating an unprecedented capacity to learn from massive amounts of real-world data to perform human-like cognitive processes, enabling them to recognize environments, objects, and conditions and make critical decisions more accurately than ever. In the medical field, the potential to generate realistic, privacy-preserving, unbiased synthetic data can be the key to unlocking the potential of artificial intelligence in medicine and overcoming the current barriers such as data privacy concerns and high data curation costs. Advanced data-driven solutions could lead towards more robust clinical decision support systems and enhanced clinical training. This Perspective critically examines current and emerging advances in synthetic data generation, and highlights its anticipated transformational effect for early and efficient prevention, diagnosis and treatment of gastrointestinal diseases. Research challenges and directions are identified for leveraging the benefits of synthetic data as well as translating and adopting them in clinical workflows.