Artificial Intelligence in Health Care
Several new studies have shown that computers can outperform doctors in cancer screenings and disease diagnoses. What does that mean for newly trained radiologists and pathologists?
A young Johns Hopkins University a fellow recently asked that question while chatting with Elliot Fishman, MD, about #artificial_intelligence (AI). The two men were on the opposite ends of the career spectrum: Fishman has been at Johns Hopkins Medicine since 1980 and a professor of radiology and oncology there since 1991; the fellow was preparing for his first job as a radiologist.
Fishman laughs when he tells the story, but he understands the concern. Over the past few years, many #AI proponents and medical professionals have branded radiology and pathology as dinosaur professions, doomed for extinction. In 2016, a New England Journal of Medicine article predicted that “#machine_learning will displace much of the work of radiologists and anatomical pathologists,” adding that “it will soon exceed human accuracy.” That same year, Geoffrey Hinton, Ph.D., a professor emeritus at the University of Toronto who also designs #machine_learning algorithms for Google (and who received the Association for #Computing Machinery’s A.M. Turing Award often called the Nobel Prize of computing, in 2019), declared, “We should stop training radiologists now."
The reason for the predictions? #AI’s tantalizing power to identify patterns and anomalies and to examine “pathologies that look certain ways,” says Fishman, who is among the enthusiasts: He’s studying the use of AI for early detection of pancreatic cancer.
“The hope is that if we could pick up early tumors that are missed, we would have better outcomes,” he says.
An array of studies have offered glimpses of #AI’s enormous potential. In a study published by #Nature_Medicine in May 2019, a Google algorithm outperformed six radiologists to determine if patients had lung cancer. The algorithm, which was developed using 42,000 patient scans from a #National_Institutes of Health clinical trial, detected 5% more cancers than its human counterparts and reduced false positives by 11%. False positives are a particular problem with lung cancer: A study in JAMA Internal Medicine of 2,100 patients found a false positive rate of 97.5%.
Furthermore, #AI performed comparably to breast screening radiologists in a study in the March 2019 Journal of the National Cancer Institute. At Stanford University, computer scientists developed an algorithm for diagnosing skin cancer, using a database of nearly 130,000 skin disease images. In diagnostic tests, the algorithm’s success rate was almost identical to that of 21 dermatologists, according to a study published in Nature in 2017. In another skin cancer study, #AI surpassed the performance of 58 international dermatologists. The algorithm not only missed fewer melanomas, but it was less likely to misdiagnose benign moles as malignant, the European Society for Medical Oncology found.