Health Data, Privacy & Regulation

Health data is among the most sensitive information that exists, and using it for AI raises some of the hardest questions at the intersection of technology, ethics, and law. Patient records, genomic data, medical images, and wearable health data are all enormously valuable for training AI systems - and all carry significant privacy risks. Regulations like the UK's Data Protection Act and the EU's GDPR impose strict requirements on how health data can be collected, stored, processed, and shared. In the US, HIPAA serves a similar function with different specifics. Navigating these frameworks is essential for any organisation working with health AI. De-identification - removing personal details so individuals cannot be recognised - is a common approach but an imperfect one. Research has repeatedly shown that supposedly anonymised health data can be re-identified by combining it with other available information. Newer approaches like federated learning (training models across distributed datasets without centralising the data) and differential privacy (adding mathematical noise to prevent individual identification) offer promising alternatives. Consent is another thorny issue: when you agree to share your health data for research, does that extend to training commercial AI models? The regulatory landscape is evolving rapidly, and organisations that get ahead of these requirements - rather than treating compliance as an afterthought - will be better positioned as rules tighten.