10 dangers of Digital Health
This is good list of dangers. I believe points 1 and 7 are most urgent:
- Regulating adaptive A.I. algorithms
An adaptive artificial intelligence (A.I.) is one that can adapt itself on-the-go based on new information it receives. In a clinical environment, it would, for example, be able to recommend blood tests more frequently in a population that has a high prevalence of diabetes. And if it sees that people in this population also have a tendency to develop cardiac issues, it will also recommend a cardiovascular evaluation. However, such algorithms rely on existing medical data which is fraught with inherent biases. By evolving on such data, adaptive A.I. will only reinforce those harmful biases such as discriminating based on one’s ethnicity and/or gender. To mitigate those risks, regulatory authorities must adopt new regulatory approaches to ensure that equitable adaptive A.I. algorithms are employed in healthcare facilities.
- A.I. not tested in a real-life clinical setting
We regularly hear news headlines of an A.I.’s incredible prowess in the healthcare setting. But in many cases, these results are obtained from laboratories using selected datasets or ideal settings which aren’t totally reflective of actual clinical environments. Google learnt it the hard way. Medical researchers from the company touted the ability of its A.I. tool to screen patients for diabetic retinopathy (DR) from images with 90% accuracy. DR is a major cause of vision loss worldwide, and detecting the condition earlier can prevent complications. But Google’s A.I. didn’t fare so well in practice as it did on paper. When used by nurses in a hospital in Thailand, they encountered several issues. Sometimes they had internet connection troubles; at other times the quality of the scan didn’t meet a certain threshold, so the A.I. simply didn’t give a result. And on some occasions, nurses even had to spend extra time editing some of the images the algorithm didn’t want to analyse.