Synthetic Data and Health Privacy

Wordcount: 1190 words While it remains to be seen whether Generative Artificial intelligence (GenAI) will genuinely revolutionise the core medical activity of clinical decision-making, it has already entered medical practice. AI-powered medical scribe tools (e.g., Nabla, Abridge, Ambience) have emerged as efforts to automate administrative tasks such as discharge summaries and referral notes, reducing physicians' workload. A recent survey revealed that many healthcare practitioners are now using publicly available large language models (LLMs)--a form of GenAI designed to process and generate text--for assisting in documentation, differential diagnosis, and exploring treatment options (1). GenAI creates new content, such as text or images, by learning from data patterns, (while "traditional" AI primarily focuses on classification and prediction). Effective healthcare applications of GenAI require vast amounts of high-quality, domainspecific data. However, as publicly available health data is exhausted, demand for private health data will increase, raising significant privacy concerns.