Machine learning in medicine must offer data protection, algorithmic transparency, and accountability to earn the trust of patients and clinicians...

As use of artificial intelligence and machine learning (AI/ML) in medicine continues to grow, regulators face a fundamental problem...

Big data has become the ubiquitous watch word of medical innovation. But with big data comes big risks and challenges, among them significant questions about patient privacy...

This chapter maps the ethical and legal challenges posed by artificial intelligence (AI) in healthcare and suggest directions for resolving them...

How will regulators evaluate Medical Machine Learning-based medical devices to ensure their safety and effectiveness? and What additional MML considerations should be taken into account in the international context?...

To inform and support Health Technology Assessment organizations and regulators in adapting their processes to AIHTs, we conducted a systematic review of the literature on the challenges posed by AIHTs in HTA and health regulation...

Artificial intelligence (AI) is changing health and health care on an unprecedented scale. Though the potential benefits are massive, so are the risks. The JAMA Summit ...

The market for patient data has never been more active. Technology companies, from startups to giants, are eager to access electronic health record (EHR) data...

In March 2018, the Trump administration announced a new initiative, MyHealthEData, to give patients greater access to their electronic health record and insurance claims information...

In the US, health and health care artificial intelligence (AI) tools grow in a liminal regulatory space. This Viewpoint examines prospects for populating the regulatory landscape for health and health care AI in the coming years...