The GDPR will affect AI implementation in healthcare in several ways. First, it requires explicit and informed consent before any collection of personal data. Informed consent has been a long-standing component of medical practice (unlike in social media or online- based marketing), but having to obtain informed consent for any collection of data still represents a higher bar than obtaining consent for specific items, such as procedures or surgical interventions. Second, the new regulation essentially lends power to the person providing the data to track what data is being collected and to be able to request removal of their data. In the healthcare context, this will shift some of the power balance toward the patient and highlights the importance of ongoing work needed to protect patient privacy and to determine appropriate governance regarding data ownership. However, this will mean that getting a hold of large amount of data will be harder for institutions and companies that are developing AI models and need mega datasets to build and train their models. This is one of the main barriers for the rapid progress of this discipline in western countries.
Historically, China has been an easier place to get this data due to their lax data privacy laws. That, combined with their large population, made China a very attractive place for building AI models. However, most recently, China has also passed some strict data privacy laws that are slated to take effect on November 2021. Personal Information Protection Law that is in some ways inspired by GDPR has raised the prospect that moving forward, accessing data for training AI models will not be as easy as in the past and combined with the data and security laws in western countries, it will be increasingly difficult for developers of these technologies to get the needed data
In US, the current healthcare environment holds little incentive for data sharing . This may change with ongoing healthcare reforms that favor bundled-outcome-based reimbursement over fee-for-service models. This would create greater incentive to compile and exchange information. Furthermore, the government should promote data sharing. The National Science and Technology Council Committee on Technology recommended that open data standards for AI should be a key priority for federal agencies.
