. As machine learning, deep learning, and other aspects of AI start to mature, they bring nearly endless possibilities to supplement, streamline, and enhance the way humans interact with data. May 14, 2018 - Healthcare is on the edge of entering the era of artificial intelligence. . This, in a nutshell, is the problem with the healthcare profession’s attitude towards mistakes, humans overlooking the gaps left by machines, there being little or no training on machine procedures, there being no accreditation body for new machines, and generally the situation which might be coming down the tracks, right towards us.

.

Privacy concerns. However, stakeholders from all corners of the industry must address a number of thorny challenges related to developing and deploying AI in healthcare … The large amount of “glue code” typically needed to hold together an AI solution, together with potential model and data dependencies, makes it very difficult to perform integration tests on the whole system and make sure that the solution is working properly at any given time. AI systems learn from the data... Professional realignment.

There are risks involving bias and inequality in health-care AI. Despite its potential to unlock new insights and streamline the way providers and patients interact with healthcare data, AI may bring not inconsiderable threats of privacy problems, ethics concerns, and medical errors.

Bias and inequality.