DO LESS HARM:

In theory, catching cancers earlier should make them easier to treat, saving lives. But that's not always what the data shows. A study published in late August combed the literature for randomized clinical trials that compared mortality (from any cause, not just cancer) in two groups: people who underwent cancer screening and people who did not. For most common types of cancer screening, they found no significant difference. The exception was sigmoidoscopy, a type of colon cancer screening that involves visualizing only the lower portion of the colon. [...]

There is no question that screening programs have caught cancers that would have killed people had they gone undetected. So why worry about overdiagnosis? Screening can also cause harm. Patients undergoing colonoscopies sometimes end up with a perforated bowel. Biopsies can lead to infection. Treatments like radiation and chemotherapy come with serious risks to people's health, and so does surgery to remove tumors.

So will AI-assisted screening lead to more overdiagnosis? I checked in with Adewole Adamson, a dermatologist and researcher at the Dell School of Medicine at the University of Texas at Austin. "Without reservation I would say 'Yes, it will,'" he says. "People think that the goal is to find more cancer. That's not our goal. Our goal is to find cancers that will ultimately kill people."  

And that's tricky. For the vast majority of cancers, there aren't good ways to separate nonlethal cases from lethal ones. So doctors often treat them all as if they might be deadly.

In a 2019 paper, Adamson explains how these cancer-detecting algorithms learn. The computer is presented with images that are labeled "cancer" or "not cancer." The algorithm then looks for patterns to help it discriminate. "The problem is that there is no single right answer to the question, "What constitutes cancer?" Adamson writes. "Diagnoses of early-stage cancer made using machine-learning algorithms will undoubtedly be more consistent and more replicable than those based on human interpretation. But they won't necessarily be closer to the truth--that is, algorithms may not be any better than humans at determining which tumors are destined to cause symptoms or death."

But there's also a chance AI might help address the problem of overdiagnosis. The Australian researchers I referenced above offer up this example: AI could use the information embedded in medical records to examine the trajectories of different patients' cancers over time. In this scenario, it might be possible to distinguish those who don't benefit from a diagnosis.

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