The start-up deploys real-time analytics and machine learning to automatically check for new cases and ease the payment process for its clients.

If the implementation science community is to facilitate the adoption of machine learning across healthcare, a team of researchers writes, issues such as privacy and algorithmic bias will need to be addressed.

From an AI standpoint, it’s critical for healthcare stakeholders to assure patients of the opportunity for human connection.

Recent advances in AI technologies, coupled with the explosive growth of large-scale multi-source data on food, drugs and diseases, offer a unique opportunity to identify molecules within foods to potentially prevent or fight disease phenotypes.

Increasingly, healthcare organizations of all sizes need people with the authority to drive AI applications into clinical settings where they can make a difference to patients while also meeting organizational needs.

The key is not to stifle AI development, say providers with ethical concerns, but to ensure that new technologies assist, not replace, the doctors on the front line of healthcare.

Engendering trust in machine learning is gradual rather than explosive, say stakeholders, and no one is going to risk applying these systems without rigorous testing.

UK tech leaders say outdated computer systems, a lack of investment and a gap in skills and awareness are threatening the health service. 

The new strategic plan comes just four months after President Trump signed the American AI Initiative, which calls for the federal government to prioritize research and development for artificial intelligence.