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AI can help screen for cancer—but there’s a catch

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AI can help screen for cancer—but there’s a catch


Why this might be isn’t totally clear. It could be because of design flaws in the study. The trials the authors included in their analysis might not have followed participants long enough to see a difference.  Another explanation is that the benefits of screening for some may be outweighed by the harms for others who don’t benefit. For example, if screening catches deadly cancers early, patients might gain precious time to successfully treat the disease.  But if a screening is catching many cancers that aren’t killing people, the balance tips. The problem is known as overdiagnosis. I like this description from a team of researchers in Australia: “Overdiagnosis is not a false-positive diagnosis (diagnosing a disease in an individual who does not meet diagnostic criteria) or a misdiagnosis (diagnosing the wrong condition in an individual who does have an underlying disease).” The diagnosis is correct, but it will provide little to no health benefit for the patient and may even result in harm.

There is no question that screening programs have caught cancers that would have killed people had they gone undetected. So why worry about overdiagnosis? Screening can also cause harm. Patients undergoing colonoscopies sometimes end up with a perforated bowel. Biopsies can lead to infection. Treatments like radiation and chemotherapy come with serious risks to people’s health, and so does surgery to remove tumors.

So will AI-assisted screening lead to more overdiagnosis? I checked in with Adewole Adamson, a dermatologist and researcher at the Dell School of Medicine at the University of Texas at Austin. “Without reservation I would say ‘Yes, it will,’” he says. “People think that the goal is to find more cancer. That’s not our goal. Our goal is to find cancers that will ultimately kill people.”  

And that’s tricky. For the vast majority of cancers, there aren’t good ways to separate nonlethal cases from lethal ones. So doctors often treat them all as if they might be deadly.
In a 2019 paper, Adamson explains how these cancer-detecting algorithms learn. The computer is presented with images that are labeled “cancer” or “not cancer.” The algorithm then looks for patterns to help it discriminate. “The problem is that there is no single right answer to the question, “What constitutes cancer?” Adamson writes. “Diagnoses of early-stage cancer made using machine-learning algorithms will undoubtedly be more consistent and more replicable than those based on human interpretation. But they won’t necessarily be closer to the truth—that is, algorithms may not be any better than humans at determining which tumors are destined to cause symptoms or death.”

But there’s also a chance AI might help address the problem of overdiagnosis. The Australian researchers I referenced above offer up this example: AI could use the information embedded in medical records to examine the trajectories of different patients’ cancers over time. In this scenario, it might be possible to distinguish those who don’t benefit from a diagnosis.

Adamson isn’t anti-AI. He sees value in simply adding a third category to the data that the algorithms learn from: “Maybe cancer.” This classification would encompass slides or images that provoke disagreement among experts. For those patients, “maybe you investigate treatments that are a bit more conservative.”

So it’s probably too early to make a ruling on AI’s role in cancer diagnoses, but we should probably read any future claims about AI cancer screening with a more skeptical eye. For his part, Adamson is tired of seeing headlines trumpet the power of AI to catch more cancers. “People get duped by those kinds of headlines into thinking that finding more cancer is better,” he says. “I want to rip my hair out, if I had any.”

Another thing

Last week I wrote about what you should know about this fall’s covid vaccines. This week, I have another story on the site about who is expected to benefit most from the vaccines, which were endorsed by the CDC on September 12.

Read more from Tech Review’s archive

When radiologists and AI work together, they can catch more breast cancer cases than either can on their own. Hana Kiros has the story

AI might also hold promise for skin cancer, Megan Lewis reports.

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The hunter-gatherer groups at the heart of a microbiome gold rush

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The hunter-gatherer groups at the heart of a microbiome gold rush


The first step to finding out is to catalogue what microbes we might have lost. To get as close to ancient microbiomes as possible, microbiologists have begun studying multiple Indigenous groups. Two have received the most attention: the Yanomami of the Amazon rainforest and the Hadza, in northern Tanzania. 

Researchers have made some startling discoveries already. A study by Sonnenburg and his colleagues, published in July, found that the gut microbiomes of the Hadza appear to include bugs that aren’t seen elsewhere—around 20% of the microbe genomes identified had not been recorded in a global catalogue of over 200,000 such genomes. The researchers found 8.4 million protein families in the guts of the 167 Hadza people they studied. Over half of them had not previously been identified in the human gut.

Plenty of other studies published in the last decade or so have helped build a picture of how the diets and lifestyles of hunter-gatherer societies influence the microbiome, and scientists have speculated on what this means for those living in more industrialized societies. But these revelations have come at a price.

A changing way of life

The Hadza people hunt wild animals and forage for fruit and honey. “We still live the ancient way of life, with arrows and old knives,” says Mangola, who works with the Olanakwe Community Fund to support education and economic projects for the Hadza. Hunters seek out food in the bush, which might include baboons, vervet monkeys, guinea fowl, kudu, porcupines, or dik-dik. Gatherers collect fruits, vegetables, and honey.

Mangola, who has met with multiple scientists over the years and participated in many research projects, has witnessed firsthand the impact of such research on his community. Much of it has been positive. But not all researchers act thoughtfully and ethically, he says, and some have exploited or harmed the community.

One enduring problem, says Mangola, is that scientists have tended to come and study the Hadza without properly explaining their research or their results. They arrive from Europe or the US, accompanied by guides, and collect feces, blood, hair, and other biological samples. Often, the people giving up these samples don’t know what they will be used for, says Mangola. Scientists get their results and publish them without returning to share them. “You tell the world [what you’ve discovered]—why can’t you come back to Tanzania to tell the Hadza?” asks Mangola. “It would bring meaning and excitement to the community,” he says.

Some scientists have talked about the Hadza as if they were living fossils, says Alyssa Crittenden, a nutritional anthropologist and biologist at the University of Nevada in Las Vegas, who has been studying and working with the Hadza for the last two decades.

The Hadza have been described as being “locked in time,” she adds, but characterizations like that don’t reflect reality. She has made many trips to Tanzania and seen for herself how life has changed. Tourists flock to the region. Roads have been built. Charities have helped the Hadza secure land rights. Mangola went abroad for his education: he has a law degree and a master’s from the Indigenous Peoples Law and Policy program at the University of Arizona.

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The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan

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The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan


Over the last couple of decades, scientists have come to realize just how important the microbes that crawl all over us are to our health. But some believe our microbiomes are in crisis—casualties of an increasingly sanitized way of life. Disturbances in the collections of microbes we host have been associated with a whole host of diseases, ranging from arthritis to Alzheimer’s.

Some might not be completely gone, though. Scientists believe many might still be hiding inside the intestines of people who don’t live in the polluted, processed environment that most of the rest of us share. They’ve been studying the feces of people like the Yanomami, an Indigenous group in the Amazon, who appear to still have some of the microbes that other people have lost. 

But there is a major catch: we don’t know whether those in hunter-gatherer societies really do have “healthier” microbiomes—and if they do, whether the benefits could be shared with others. At the same time, members of the communities being studied are concerned about the risk of what’s called biopiracy—taking natural resources from poorer countries for the benefit of wealthier ones. Read the full story.

—Jessica Hamzelou

Eric Schmidt has a 6-point plan for fighting election misinformation

—by Eric Schmidt, formerly the CEO of Google, and current cofounder of philanthropic initiative Schmidt Futures

The coming year will be one of seismic political shifts. Over 4 billion people will head to the polls in countries including the United States, Taiwan, India, and Indonesia, making 2024 the biggest election year in history.

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Navigating a shifting customer-engagement landscape with generative AI

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Navigating a shifting customer-engagement landscape with generative AI


A strategic imperative

Generative AI’s ability to harness customer data in a highly sophisticated manner means enterprises are accelerating plans to invest in and leverage the technology’s capabilities. In a study titled “The Future of Enterprise Data & AI,” Corinium Intelligence and WNS Triange surveyed 100 global C-suite leaders and decision-makers specializing in AI, analytics, and data. Seventy-six percent of the respondents said that their organizations are already using or planning to use generative AI.

According to McKinsey, while generative AI will affect most business functions, “four of them will likely account for 75% of the total annual value it can deliver.” Among these are marketing and sales and customer operations. Yet, despite the technology’s benefits, many leaders are unsure about the right approach to take and mindful of the risks associated with large investments.

Mapping out a generative AI pathway

One of the first challenges organizations need to overcome is senior leadership alignment. “You need the necessary strategy; you need the ability to have the necessary buy-in of people,” says Ayer. “You need to make sure that you’ve got the right use case and business case for each one of them.” In other words, a clearly defined roadmap and precise business objectives are as crucial as understanding whether a process is amenable to the use of generative AI.

The implementation of a generative AI strategy can take time. According to Ayer, business leaders should maintain a realistic perspective on the duration required for formulating a strategy, conduct necessary training across various teams and functions, and identify the areas of value addition. And for any generative AI deployment to work seamlessly, the right data ecosystems must be in place.

Ayer cites WNS Triange’s collaboration with an insurer to create a claims process by leveraging generative AI. Thanks to the new technology, the insurer can immediately assess the severity of a vehicle’s damage from an accident and make a claims recommendation based on the unstructured data provided by the client. “Because this can be immediately assessed by a surveyor and they can reach a recommendation quickly, this instantly improves the insurer’s ability to satisfy their policyholders and reduce the claims processing time,” Ayer explains.

All that, however, would not be possible without data on past claims history, repair costs, transaction data, and other necessary data sets to extract clear value from generative AI analysis. “Be very clear about data sufficiency. Don’t jump into a program where eventually you realize you don’t have the necessary data,” Ayer says.

The benefits of third-party experience

Enterprises are increasingly aware that they must embrace generative AI, but knowing where to begin is another thing. “You start off wanting to make sure you don’t repeat mistakes other people have made,” says Ayer. An external provider can help organizations avoid those mistakes and leverage best practices and frameworks for testing and defining explainability and benchmarks for return on investment (ROI).

Using pre-built solutions by external partners can expedite time to market and increase a generative AI program’s value. These solutions can harness pre-built industry-specific generative AI platforms to accelerate deployment. “Generative AI programs can be extremely complicated,” Ayer points out. “There are a lot of infrastructure requirements, touch points with customers, and internal regulations. Organizations will also have to consider using pre-built solutions to accelerate speed to value. Third-party service providers bring the expertise of having an integrated approach to all these elements.”

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