UCSF Health’s AI Breakthrough: What’s Been Learned So Far

Even as artificial intelligence (AI) has become an increasing focus in many areas within U.S. healthcare, clinicians, informaticists, and others are all discovering just how difficult and time-consuming the development of AI algorithms is actually turning out to be. By one estimate, it can take an investment of three years and $5 million per model to achieve generalizability. One of the biggest roadblocks to AI’s advancement is the quality and quantity of data needed to properly train algorithms, as an algorithm that performs well on local clinical data often fails when exposed to diverse real-world data. Acquiring the volume of diverse, real-world data needed to retrain algorithms is not only complex, but time-consuming and expensive.