From the November 2021 issue of HealthCare Business News magazine

At least that’s what researchers at Princess Margaret Cancer Centre in Toronto found in June when they compared treatments in clinical settings for the care of prostate cancer patients.

“From our study, one key result that will change how we develop protocols in the future, is the fact physicians selected machine learning treatments less often in the prospective evaluation setting when patient care was at stake compared with evaluation of treatments in the retrospective setting," Dr. Leigh Conroy, medical physicist at UHN's Princess Margaret Cancer Centre, told HCB News.


When comparing the two, physicians found 89% of treatments generated by ML to be clinically acceptable, and selected 72% over human-generated treatments. When compared among a group of patients that already underwent radiotherapy, the number of ML-generated ones chosen by radiation oncologists was 83% more than human ones. But when asked in a clinical capacity which should be used for patients who had yet to undergo treatment, oncologists were less likely to recommend ML-generated treatment, with the number dropping to 61%.

The researchers chalk this up to fears of deploying inadequately validated AI systems, as any AI-generated treatments judged to be superior and preferable to their human counterparts would be used in the actual treatments for the pre-treatment group.

British researchers label over 100,000 MR exams in under 30 minutes
Researchers in London announced in July that they hd devised a technique that enables more than 100,000 MR exams to be labeled in less than 30 minutes.

The group at the School of Biomedical Engineering & Imaging Sciences at King’s College London automated brain MR image labeling to teach machine learning image recognition models to identify and accurately assign important labels from radiology reports to corresponding MR exams.

They expect it to save them what would amount to years spent manually labeling this number of exams. "By overcoming this bottleneck, we have massively facilitated future deep learning image recognition tasks and this will almost certainly accelerate the arrival into the clinic of automated brain MR readers. The potential for patient benefit through, ultimately, timely diagnosis, is enormous."

Booth and his colleagues evaluated model performance on unseen radiology reports as well as on unseen images. The latter of these tasks has been a challenge, he says, due to the enormous team of radiologists required.

November 2021 Magazine

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