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.
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. Furthermore, CDS systems' reliance on particularly areas—such as hospital readmission rates rather than patient happiness—to determine a treatment's effectiveness can often miss the big picture, while data can become outdated in as little as four months due to the fast-paced changes in clinical methods.