2. Medical AI has a big data problem
Facing increasingly overworked doctors and labyrinthine insurance systems, hospitals are searching for a lifeline in AI systems that promise to ease hard diagnoses and treatment decisions, Axios' Kaveh Waddell reports.
Reality check: The data underpinning the very first systems is often spotty, volatile and completely lacking in critical context, leading to a poor early record in the field.
The big picture: Basic clinical decision support (CDS) systems have been around for decades, but a skepticism of technology leads many doctors to ignore or override them.
- Now, experts say a nascent generation of CDS — infused with AI in academic labs and startups — may reduce the estimated 40,000–80,000 deaths a year that result from medical errors.
The grand vision: Researchers hope AI programs can point doctors toward the best medications, lab tests or treatment plans based on minute patterns discovered in huge numbers of patients' past experiences.
Yes, but: Record keeping is so bad that doctors laugh when you ask about it.
- Electronic medical records, central to CDS predictions, are notoriously error-ridden.
- Other quirks of health data also make problems for CDS systems.
The bottom line: It's the oldest problem in data science: garbage in, garbage out.
What's next: The FDA, which doesn't review most CDS systems, is considering policy changes that could head off some data issues. Scientists are pushing the agency to impose strict benchmarks and audits to prevent mistakes.