In Hungary, artificial intelligence is catching cases of breast cancer than radiologists overlook, enabling the cancer to be removed before it metastasizes and kills women. In the U.S., this technology could save lives, if it were approved by the Food and Drug Administration, but the FDA often blocks or delays potentially life-saving devices, such as home HIV tests or smartwatches that detect illnesses. Here is an excerpt from the news article “Using A.I. to Detect Breast Cancer That Doctors Miss”:
Inside a dark room at Bács-Kiskun County Hospital outside Budapest, Dr. Éva Ambrózay, a radiologist with more than two decades of experience, peered at a computer monitor showing a patient’s mammogram.
Two radiologists had previously said the X-ray did not show any signs that the patient had breast cancer. But Dr. Ambrózay was looking closely at several areas of the scan circled in red, which artificial intelligence software had flagged as potentially cancerous. “This is something,” she said. She soon ordered the woman to be called back for a biopsy, which is taking place within the next week.
Advancements in A.I. are beginning to deliver breakthroughs in breast cancer screening by detecting the signs that doctors miss. So far, the technology is showing an impressive ability to spot cancer at least as well as human radiologists, according to early results and radiologists, in what is one of the most tangible signs to date of how A.I. can improve public health.
Hungary, which has a robust breast cancer screening program, is one of the largest testing grounds for the technology on real patients. At five hospitals and clinics that perform more than 35,000 screenings a year, A.I. systems were rolled out starting in 2021 and now help to check for signs of cancer that a radiologist may have overlooked. Clinics and hospitals in the United States, Britain and the European Union are also beginning to test or provide data to help develop the systems.
A.I. usage is growing as the technology has become the center of a Silicon Valley boom, with the release of chatbots like ChatGPT showing how A.I. has a remarkable ability to communicate in humanlike prose — sometimes with worrying results. Built off a similar form used by chatbots that is modeled on the human brain, the breast cancer screening technology shows other ways that A.I. is seeping into everyday life.
Widespread use of the cancer detection technology still faces many hurdles, doctors and A.I. developers said. Additional clinical trials are needed before the systems can be more widely adopted as an automated second or third reader of breast cancer screens, beyond the limited number of places now using the technology. The tool must also show it can produce accurate results on women of all ages, ethnicities and body types. And the technology must prove it can recognize more complex forms of breast cancer and cut down on false-positives that are not cancerous, radiologists said.
The A.I. tools have also prompted a debate about whether they will replace human radiologists, with makers of the technology facing regulatory scrutiny and resistance from some doctors and health institutions. For now, those fears appear overblown, with many experts saying the technology will be effective and trusted by patients only if it is used in partnership with trained doctors.
And ultimately, A.I. could be lifesaving, said Dr. László Tabár, a leading mammography educator in Europe who said he was won over by the technology after reviewing its performance in breast cancer screening from several vendors.
“I’m dreaming about the day when women are going to a breast cancer center and they are asking, ‘Do you have A.I. or not?’” he said.
In 2016, Geoff Hinton, one of the world’s leading A.I. researchers, argued the technology would eclipse the skills of a radiologist within five years….Mr. Hinton and two of his students at the Uiversity of Toronto built an image recognition system that could accurately identify common objects like flowers, dogs and cars. The technology at the heart of their system — called a neural network — is modeled on how the human brain processes information from different sources. It is what is used to identify people and animals in images posted to apps like Google Photos, and allows Siri and Alexa to recognize the words people speak….
Many A.I. evangelists believed such technology could easily be applied to detect illness and disease, like breast cancer in a mammogram. In 2020, there were 2.3 million breast cancer diagnoses and 685,000 deaths from the disease, according to the World Health Organization.Mr. Kecskemethy grew up in Hungary spending time at one of Budapest’s largest hospitals. His mother was a radiologist, which gave him a firsthand look at the difficulties of finding a small malignancy within an image. Radiologists often spend hours every day in a dark room looking at hundreds of images and making life-altering decisions for patients.
“It’s so easy to miss tiny lesions,” said Dr. Edith Karpati, Mr. Kecskemethy’s mother, who is now a medical product director at Kheiron. “It’s not possible to stay focused.”
Mr. Kecskemethy, along with Kheiron’s co-founder, Tobias Rijken, an expert in machine learning, said A.I. should assist doctors. To train their A.I. systems, they collected more than five million historical mammograms of patients whose diagnoses were already known, provided by clinics in Hungary and Argentina, as well as academic institutions, such as Emory University. The company, which is in London, also pays 12 radiologists to label images using special software that teaches the A.I. to spot a cancerous growth by its shape, density, location and other factors.
From the millions of cases the system is fed, the technology creates a mathematical representation of normal mammograms and those with cancers. With the ability to look at each image in a more granular way than the human eye, it then compares that baseline to find abnormalities in each mammogram.
Last year, after a test on more than 275,000 breast cancer cases, Kheiron reported that its A.I. software matched the performance of human radiologists when acting as the second reader of mammography scans. It also cut down on radiologists’ workloads by at least 30 percent because it reduced the number of X-rays they needed to read. In other results from a Hungarian clinic last year, the technology increased the cancer detection rate by 13 percent because more malignancies were identified.
Dr. Tabár, whose techniques for reading a mammogram are commonly used by radiologists, tried the software in 2021 by retrieving several of the most challenging cases of his career in which radiologists missed the signs of a developing cancer. In every instance, the A.I. spotted it.
“I was shockingly surprised at how good it was,” Dr. Tabár said. He said that he did not have any financial connections to Kheiron when he first tested the technology and has since received an advisory fee for feedback to improve the systems. Systems he tested from other A.I. companies, including Lunit Insight from South Korea and Vara from Germany, have also delivered encouraging detection results, he said.
Kheiron’s technology was first used on patients in 2021 in a small clinic in Budapest called MaMMa Klinika. After a mammogram is completed, two radiologists review it for signs of cancer. Then the A.I. either agrees with the doctors or flags areas to check again.
Across five MaMMa Klinika sites in Hungary, 22 cases have been documented since 2021 in which the A.I. identified a cancer missed by radiologists, with about 40 more under review.
The fact that this technology is being used in Hungary to save lives does not mean it can immediately be used in the U.S. FDA employees commonly take years to approve life-saving diagnostic tools.The FDA didn’t approve a home test for HIV until 24 years after it first received an application. According to an FDA advisory committee, the test “holds the potential to prevent the transmission of more than 4,000 new HIV infections in its first year of use alone.” That means thousands of people likely got infected with AIDS as a result of the delay in approving it. As Roger Parloff of Fortune notes, the FDA’s delay in approving the home HIV test was a “scandal.” It likely caused the deaths of thousands of people, given the high mortality rate from AIDS in the past.
The FDA’s slowness in approving drugs and devices results in the deaths of hundreds of thousands of people who could have been saved by earlier approval of those drugs. For example, at least a hundred thousand people died waiting for the FDA to approve beta blockers. One of the FDA officials involved in delaying their approval was John Nestor.
Nestor was notorious for following rules in ways designed to frustrate and inconvenience other people. As the Journal of American Physicians and Surgeons notes:
Nestor had the unique habit of getting into the leftmost lane [on the highway] with his cruise control set at 55 mph, the posted speed limit. He would drive at this speed regardless of what came up behind him. Cars would zoom up close to his rear bumper; drivers would flash their lights and blast their horns,some swerving around him on the right while giving him the finger—none of this fazed Nestor in the least. As he explained it, 55 mph was the law, and he had a right to drive in whichever lane he chose: “Why should I inconvenience myself for someone who wants to speed?”
Nestor followed this rigid mindset in his work at the FDA. He was very good at using agency red tape, and minor risks or side effects of drugs, as an excuse to avoid approving life-saving drugs.
