May 18, 2022 – Imagine walking into the Library of Congress, with its millions of books, and having the goal of reading them all. Impossible, right? Even if you could read every word of every work, you wouldn’t be able to remember or understand everything, even if you spent a lifetime trying.
Now let’s say you somehow had a super-powered brain capable of reading and understanding all that information. You would still have a problem: You wouldn’t know what wasn’t covered in those books – what questions they’d failed to answer, whose experiences they’d left out.
Similarly, today’s researchers have a staggering amount of data to sift through. All the world’s peer-reviewed studies contain more than 34 million citations. Millions more data sets explore how things like bloodwork, medical and family history, genetics, and social and economic traits impact patient outcomes.
Artificial intelligence lets us use more of this material than ever. Emerging models can quickly and accurately organize huge amounts of data, predicting potential patient outcomes and helping doctors make calls about treatments or preventive care. Advanced mathematics holds great promise. Some algorithms – instructions for solving problems – can diagnose breast cancer with more accuracy than pathologists. Other AI tools are already in use in medical settings, allowing doctors to more quickly look up a patient’s medical history or improve their ability to analyze radiology images.
But some experts in the field of artificial intelligence in medicine suggest that while the benefits seem obvious, lesser noticed biases can undermine these technologies. In fact, they warn that biases can lead to ineffective or even harmful decision-making in patient care. New Tools, Same Biases?
While many people associate “bias” with personal, ethnic, or racial prejudice, broadly defined, bias is a tendency to lean in a certain direction, either in favor of or against a particular thing.
In a statistical sense, bias occurs when data does not fully or accurately represent the population it is intended to model. This can happen from having poor data at the start, or it can occur when data from one population is applied to another by mistake. Both types of bias – statistical and racial/ethnic – exist within medical literature. Some populations have been studied more, while others are under-represented. This raises the question: If we build AI models from the existing information, are