AI-assisted cancer screening could cut radiologist workloads in half

The study saw no increase in false-positive results over human screeners.

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A newly published study in the the Lancet Oncology journal has found that the use of AI in mammogram cancer screening can safely cut radiologist workloads nearly in half without risk of increasing false-positive results. In effect, the study found that the AI’s recommendations were on par with those of two radiologists working together.

“AI-supported mammography screening resulted in a similar cancer detection rate compared with standard double reading, with a substantially lower screen-reading workload, indicating that the use of AI in mammography screening is safe,” the study found.

The study was performed by a research team out of Lund University in Sweden and, accordingly, followed 80,033 Swedish women (average age of 54) for just over a year in 2021-2022 . Of the 39,996 patients that were randomly assigned AI-empowered breast cancer screenings, 28 percent or 244 tests returned screen-detected cancers. Of the other 40,024 patients that received conventional cancer screenings, just 25 percent, or 203 tests, returned screen-detected cancers.

Of those extra 41 cancers detected by the AI side, 19 turned out to be invasive. Both the AI-empowered and conventional screenings ran a 1.5 percent false positive rate. Most impressively, radiologists on the the AI side had to look at 36,886 fewer screen readings than their counterparts, a 44 percent reduction in their workload.

“These promising interim safety results should be used to inform new trials and program-based evaluations to address the pronounced radiologist shortage in many countries, but they are not enough on their own to confirm that AI is ready to be implemented in mammography screening," lead author, Dr Kristina Lång, warned in a release. “We still need to understand the implications on patients’ outcomes, especially whether combining radiologists’ expertise with AI can help detect interval cancers that are often missed by traditional screening, as well as the cost-effectiveness of the technology.”

Cancer detection has been an aspirational goal for computer vision researchers and AI companies for years now. I mean, who doesn’t want to be the company to build the tricorder that infallibly spots cancerous growths in their earliest stages? Machine vision systems designed for these screenings have improved steadily in recent years and in specific cases have shown to be as reliable as human clinicians, with the likes of IBM, Google, MIT and NVIDIA investing in similar cancer screening research in recent years.