Facing increasingly overworked doctors and labyrinthine insurance systems, hospitals are searching for a lifeline in AI systems that promises to ease hard diagnoses and treatment decisions. 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 big problem: Record keeping is so bad that doctors laugh when you ask about it. Other quirks of health data make more problems for CDS systems: It's the oldest problem in data science: garbage in, garbage out. What's next: The Food and Drug Administration, which currently 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. Go deeper: What your hospital knows about you
Patchy and irrelevant data is holding back the use of AI systems in medicine. While clinical decision support (CDS) systems have the potential to eliminate 40,000-80,000 deaths each year from medical errors, the mistake-ridden nature of electronic medical records, along with generic diagnoses, mean that the AI is often dismissed by doctors as inaccurate. The problem lies not just in the accuracy or reliability of a patient’s diagnosis, but also in how it can be used for treatment.