Will Bad Data Undermine Good Tech?
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As we are having AI in our inventory to analyze vast health data being generated, do we still face similar challenges of bias?
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@diwakar 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 retain or comprehend 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 clinicians have a staggering amount of data to sift through. Pubmed alone contains more than 34 million citations. And that's just the peer-reviewed stuff. Millions more data sets explore how factors like bloodwork, medical and family history, genetics, and socioeconomic traits impact patient outcomes.
Artificial intelligence (AI) lets us use more of this material than ever. Emerging models can quickly and accurately synthesize enormous amounts of data, predicting potential patient outcomes and helping doctors make calls about treatments or preventive care.
Predictive algorithms hold great promise. Some can diagnose breast cancer with a higher rate of 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.
However, some experts in the field of artificial intelligence in medicine (AIM) suggest that while the benefits seem obvious, lesser noticed biases can undermine these technologies. In fact, they caution 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 errantly applied to another.
Both types of bias — statistical and racial/ethnic — exist within medical literature. Some populations have been studied more, while others are under-represented.