AI News, Google AI Can Now Detect Breast Cancer Better Than Doctors ... artificial intelligence

A machine-versus-doctors fixation masks important questions about artificial intelligence

Wallet-sized cards containing a person’s genetic code don’t exist.  Yet they were envisioned in a 1996 Los Angeles Times article, which predicted that by 2020 the makeup of a person’s genome would drive their medical care.  That idea that today we’d be basking in the fruits of ultra-personalized medicine was put forth by scientists who were promoting the Human Genome Project —

He pointed to “incentives for both biologists and journalists to tell simple stories, including the idea of relatively simple genetic causation of common, debilitating disease.” Lately the allure of a simple story thwarts public understanding of another technology that’s grabbed the spotlight in the wake of the genetic data boom:  artificial intelligence (AI).  With AI, headlines often focus on the ability of machines to “beat” doctors at finding disease. Take coverage of a study published this month on a Google algorithm for reading mammograms: CNBC: Google’s DeepMind A.I.

At least anecdotally, Harvey said, some young doctors are eschewing the field of radiology in the UK, where there is a shortage.  Harvey drew chuckles during a speech at the Radiological Society of North American in December when he presented a slide showing that while about 400 AI companies has sprung up in the last five years, the number of radiologists who have lost their jobs stands at zero.

(Medium ran Harvey’s defiant explanation of why radiologists won’t easily be nudged aside by computers.) The human-versus-machine fixation distracts from questions of whether AI will benefit patients or save money.  We’ve often written about the pitfalls of reporting on drugs that have only been studied in mice.

Almost always, a computer’s “deep learning” ability is trained and tested on cleaned-up datasets that don’t necessarily predict how they’ll perform in actual patients.  Harvey said there’s a downside to headlines “overstating the capabilities of the technology before it’s been proven.” “I think patients who read this stuff can get confused.

In Undark, Jeremy Hsu reported on the lack of evidence for a triaging app, Babylon Health.  Harvey said journalists also need to point out “the reality of what it takes to get it into the market and into the hands of end users.” He cites lung cancer screening, for which some stories cover “how good the algorithm is at finding lung cancers and not much else.” For example, a story that appeared in the New York Post (headline: “Google’s new AI is better at detecting lung cancer than doctors”)  declared that “AI is proving itself to be an incredible tool for improving lives” without presenting any evidence.

Google Can Now Detect Breast Cancer Better Than Doctors

recent study, funded by Google, compared analyses of around 29,000 mammograms from the UK and U.S.; the study found that Google’s technology reduced false negatives by 9.7 percent, and false positives by 5.7 percent in the U.S., and reduced 2.7 percent of false negatives and 1.2 false positives in the UK.

Gizmodo warns the tech industry that Google might have ulterior motives, and “will gladly scoop your data as a shady means to benevolent ends.” The publication specifically points to the UK accusing the tech giant of illegally obtaining health records for over a million people for a DeepMind study on kidney injuries;

Op-Ed:Using artificial intelligence to diagnose cancer could mean unnecessary treatments

The new decade opened with some intriguing news: the journal Nature reported that artificial intelligence was better at identifying breast cancers on mammograms than radiologists.

In this case, the system was trained with images labeled as either “cancer” or “not cancer.” From them, it learned to deduce features from the images — such as shape, density and edges — that are associated with the cancer label.

Over the last 20 years there has been a growing recognition that screening mammography has led to substantial overdiagnosis — the detection of abnormalities that meet the pathological definition of cancer, yet are not destined to ever cause symptoms or death.

We suggested that each biopsy used in training AI systems be evaluated by a diverse panel of pathologists and labeled with three distinct categories: unanimous agreement of cancer, unanimous agreement of not cancer, and disagreement as to the presence of cancer.

This intermediate category of disagreement would not only help researchers understand the natural history of cancer, but could also be used by clinicians and patients to investigate less invasive treatment for “cancers” in the gray area.

Yet, while the notion of disagreement may be unsettling, disagreement also provides important information: Patients diagnosed with an early-stage cancer should be more optimistic about their prognoses if there were some disagreement about whether cancer was present, rather than all pathologists agreeing it was obviously cancer.

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