The results suggested that most patients should receive hormone therapy, but the AI suggested a more nuanced answer. “Patients who had AI biomarker negative do not see any benefit from adding 4 months of hormone therapy ... whereas patients who have biomarker positive have significant difference and improvement in distant metastasis at 10 years and 15 years. This means that we can save a significant proportion of patients from getting [androgen deprivation therapy], which is hormonal therapy and has very well-known side effects, because they simply they will not benefit,” said Dr. Mohamad, who is an assistant professor of radiation oncology at University of California, San Francisco.
That study relied on the ArteraAI prostate cancer test, which is available through a Clinical Laboratory Improvement Amendment–certified laboratory in Florida.
Another example of AI used to plan treatment is On-line Real-time Benchmarking Informatics Technology for Radiotherapy (ORBIT-RT), developed at the University of California, San Diego. It focuses on radiotherapy treatment plan quality control, and has two main components: creating clinically validated plan routines and a free radiotherapy plan quality control system.
No matter how impressive the technical advances may be, AI contributions won’t impact clinical practice if radiation oncologists, physicians, and patients don’t accept AI. Dr. Aneja’s group surveyed patients about which health field they would feel more comfortable with AI having an important role. Most said they were extremely uncomfortable when it came to cancer. “Now, does that mean that we can’t use AI in oncology? No, I think it just means that we have to be a little bit more nuanced in our approach and how we develop AI solutions for cancer patients,” Dr. Aneja said.
Physicians also show reluctance, according to Alejandro Berlin, MD, who is an affiliate scientist at Princess Margaret Cancer Centre in Toronto. He discussed some research looking at physician acceptance of machine learning. His group looked at physician acceptance of treatment plans for prostate cancer that were generated by physicians and in parallel by machine learning. In a theoretical phase, physicians generally agreed that the machine learning plans were better, but when it came to a phase of the study in which physicians chose which plan to implement in a real patient, the acceptance of machine learning-generated plans dropped by 20%.
This tendency to trust humans over machines is what Dr. Berlin called “automation bias,” and he called for a more collaborative approach to implement AI. “In some cases, [machine learning] is going to be good and sufficient. And in some cases, you will need the expertise of a human.”
Dr. Aneja, who also moderated the session, expressed a similar sentiment when summing up the day’s talks: “I do feel like it’s a disruptive technology ... but I think there will still be a need for us to have people who are trained in order to evaluate and make sure that these algorithms are working correctly and efficiently.”
Dr. Aneja, Dr. Mohamad, and Dr. Berlin have no relevant financial disclosures.
* This article was updated on Nov. 15, 2022.