Screenshot: @hoalycu
In case you'd heard any scary stories about AI advancing to the point of taking clinicians' jobs, here's your regular reminder that we're very, very far from such a scenario.
Driving the news: In a recent thread, Twitter user Lucy Hao shared several screenshots from an MIT article detailing problems that befell several AI tools designed to help identify COVID-19.
Why it matters: In even the most advanced of cases, health care-related algorithms are typically being used in an effort to guide clinical decision making — not replace a doctor's judgment.
- Even when it comes to those types of tools, developers must be careful to ensure their algorithms are trained with the appropriate data and account for any confounding variables.
- One common issue that's surfaced a lot of late involves AI tools trained on non-diverse populations and then deployed on diverse populations. Can you say error-prone?
(Small) details: When unaccounted for, unexpected variables rendered several COVID algorithms useless.
- In one example from the article, developers trained their tool on a dataset containing chest scans of children without COVID. The resulting AI tools "learned to identify kids, not COVID."
- In another, patients who were scanned while lying down were more likely to be seriously ill. So "the AI learned wrongly to predict serious COVID risk from a person's position."
Oops.
