Technology
AI is permeating American culture, but radiologists hesitant to place patients' health in an algorithm's hands
How good would an algorithm have to be to take over your job?
It’s a new question for many workers amid the rise of ChatGPT and other AI programs that can hold conversations, write stories and even generate songs and images within seconds.
For doctors who review scans to spot cancer and other diseases, however, AI has loomed for about a decade as more algorithms promise to improve accuracy, speed up work and, in some cases, take over entire parts of the job. Predictions have ranged from doomsday scenarios in which AI fully replaces radiologists, to sunny futures in which it frees them to focus on the most rewarding aspects of their work.
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That tension reflects how AI is rolling out across health care. Beyond the technology itself, much depends upon the willingness of doctors to put their trust — and their patients’ health — in the hands of increasingly sophisticated algorithms that few understand.
Even within the field, opinions differ on how much radiologists should be embracing the technology.
“Some of the AI techniques are so good, frankly, I think we should be doing them now,” said Dr. Ronald Summers, a radiologist and AI researcher at the National Institutes of Health. “Why are we letting that information just sit on the table?”
Dr. Laurie Margolies demonstrates the Koios DS Smart Ultrasound software, Wednesday, May 8, 2024, at Mount Sinai hospital in New York. The breast imaging AI is used to get a second opinion on mammography ultrasounds. “I will tell patients, ‘I looked at it, and the computer looked at it, and we both agree,’” Margolies said. “Hearing me say that we both agree, I think that gives the patient an even greater level of confidence.” (AP Photo/Mary Altaffer)
Summers’ lab has developed computer-aided imaging programs that detect colon cancer, osteoporosis, diabetes and other conditions. None of those have been widely adopted, which he attributes to the “culture of medicine,” among other factors.
Radiologists have used computers to enhance images and flag suspicious areas since the 1990s. But the latest AI programs can go much further, interpreting the scans, offering a diagnosis and even drafting written reports about their findings. The algorithms are often trained on millions of X-rays and other images collected from hospitals and clinics.
Across medicine, the FDA has OK’d more than 700 AI algorithms to aid physicians. More than 75% of them are in radiology, yet just 2% of radiology practices use such technology, according to one recent estimate.
For all the promises from industry, radiologists see a number of reasons to be skeptical of AI programs: limited testing in real-world settings, lack of transparency about how they work and questions about the demographics of the patients used to train them.
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“If we don’t know on what cases the AI was tested, or whether those cases are similar to the kinds of patients we see in our practice, there’s just a question in everyone’s mind as to whether these are going to work for us,” said Dr. Curtis Langlotz, a radiologist who runs an AI research center at Stanford University.
To date, all the programs cleared by the FDA require a human to be in the loop.
In early 2020, the FDA held a two-day workshop to discuss algorithms that could operate without human oversight. Shortly afterwards, radiology professionals warned regulators in a letter that they “strongly believe it is premature for the FDA to consider approval or clearance” of such systems.
But European regulators in 2022 approved the first fully automatic software that reviews and writes reports for chest X-rays that look healthy and normal. The company behind the app, Oxipit, is submitting its U.S. application to the FDA.
The need for such technology in Europe is urgent, with some hospitals facing monthslong backlogs of scans due to a shortage of radiologists.
In the U.S., that kind of automated screening is likely years away. Not because the technology isn’t ready, according to AI executives, but because radiologists aren’t yet comfortable turning over even routine tasks to algorithms.
“We try to tell them they’re overtreating people and they’re wasting a ton of time and resources,” said Chad McClennan, CEO of Koios Medical, which sells an AI tool for ultrasounds of the thyroid, the vast majority of which are not cancerous. “We tell them, ‘Let the machine look at it, you sign the report and be done with it.’”
Radiologists tend to overestimate their own accuracy, McClennan says. Research by his company found physicians viewing the same breast scans disagreed with each other more than 30% of the time on whether to do a biopsy. The same radiologists even disagreed with their own initial assessments 20% of the time, when viewing the same images a month later.
About 20% of breast cancers are missed during routine mammograms, according to the National Cancer Institute.
And then there’s the potential for cost savings. On average, U.S. radiologists earn over $350,000 annually, according to the Department of Labor.
In the near term, experts say AI will work like autopilot systems on planes — performing important navigation functions, but always under the supervision of a human pilot.
That approach offers reassurances to both radiologists and patients, says Dr. Laurie Margolies, of Mount Sinai hospital system in New York. The system uses Koios breast imaging AI to get a second opinion on mammography ultrasounds.
“I will tell patients, ‘I looked at it, and the computer looked at it, and we both agree,’” Margolies said. “Hearing me say that we both agree, I think that gives the patient an even greater level of confidence.”
The first large, rigorous trials testing AI-assisted radiologists against those working alone give hints at the potential improvements.
Initial results from a Swedish study of 80,000 women showed a single radiologist working with AI detected 20% more cancers among mammograms than two radiologists working without the technology.
In Europe, mammograms are reviewed by two radiologists to improve accuracy. But Sweden, like other countries, faces a workforce shortage, with only about 70 breast radiologists in a country of 10 million people.
Using AI instead of a second reviewer decreased the human workload by 44%, according to the study.
Still, the study’s lead author says it’s essential that a radiologist make the final diagnosis in all cases.
If an automated algorithm misses a cancer, “that’s going to be very negative for trust in the caregiver,” said Dr. Kristina Lang of Lund University.
The question of who would be held liable in such cases is among the thorny legal issues that have yet to be resolved.
One result is that radiologists are likely to continue double-checking all AI determinations, lest they be held responsible for an error. That’s likely to wipe out many of the predicted benefits, including reduced workload and burnout.
Only an extremely accurate, reliable algorithm would allow radiologists to truly step away from the process, says Dr. Saurabh Jha of the University of Pennsylvania.
Until such systems emerge, Jha likens AI-assisted radiology to someone who offers to help you drive by looking over your shoulder and constantly pointing out everything on the road.
“That’s not helpful,” Jha says. “If you want to help me drive then you take over the driving so that I can sit back and relax.”
Would you pay $20 a month for access to AI hardware you already own? That appears to be one of Meta’s next bets. This week, it quietly announced that your glasses’ Conversation Focus feature will soon be limited to three hours of use per month, unless you pay for a $19.99 Meta One Premium subscription.
In a help article, the company insists that it won’t require a subscription to use your glasses, period; it’s merely erecting a “rate limit” for certain AI features. Even premium subscribers will only get 15 hours of Conversation Focus per month under that “rate limit,” it claims.
Problem is, Meta’s rate limit is ridiculous. The Conversation Focus feature, which amplifies the voice of the person you’re speaking to so you can hear better in noisy environments, is not something that should plausibly be rate-limited, because it doesn’t use Meta’s servers. It runs on-device, using the chips inside the glasses that you’ve already purchased. I turned off my internet, and it kept working.
Here’s how the company introduced it last year: “[C]onversation focus uses your AI glasses’ open-ear speakers, beamforming technology, and real-time spatial processing to dynamically amplify the voice of the person you’re talking to.”
Not only does it avoid Meta’s servers, but Conversation Focus doesn’t technically require an internet connection at all. I double-checked by turning off my phone’s Wi-Fi and cellular, turning on Airplane Mode, and I was still able to use Conversation Focus just fine by tapping a button on my phone.
Does Meta have some secret licensing deal with another company that costs it money every time a person uses Conversation Focus? Failing that, the rate limit sounds utterly bogus.
We’ve asked if Meta can explain the move, and whether the company plans to put other on-device features behind a subscription. Meta didn’t immediately respond to a request for comment.
TRANSFORMATION: AI changes what robots can accomplish
Humanoid robots, showcased at Chicago’s Automate Show, demonstrate advancements in AI and robotics. Jeff Burnstein, President of the Association for Advancing Automation, explains AI’s role in enabling diverse tasks in hospitals, factories, and warehouses. He emphasizes that robotics boosts competitiveness, leading to job creation.
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A busy warehouse loading dock can be a grind. Trucks pull up. Packages pour in. Workers have to move fast, lift heavy boxes and keep everything flowing before the next trailer arrives. That part of the warehouse has always been one of the hardest places to automate. Every box can be a different size. Freight can shift in transit. Labels may face the wrong way. And when one system finishes a task, the next system still has to know what to do with the package.
Now, Ambi Robotics and Pickle Robot Company say they have linked their robotic systems to help solve that handoff problem. The companies announced a commercial integration that connects Pickle Robot’s trailer-unloading robots with Ambi Robotics’ AmbiStack pallet-building system. In other words, one robot system unloads mixed freight from a trailer. Then a conveyor moves those cases downstream so another robotic system can scan and stack them for warehouse receiving.
If this works well in large facilities, it points to a future where robots can handle more of the work that happens between a truck and a warehouse floor.
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Ambi Robotics and Pickle Robot Company have integrated their warehouse robotics systems to automate the flow of freight from trailers to pallets. The companies say the setup can fit into existing warehouse operations. (Ambi Robotics and Pickle Robot Company )
How warehouse robots move packages from truck to pallet
The setup starts at the trailer. Pickle Robot’s system unloads boxes from trailers or containers. That matters because unloading mixed freight can be exhausting work. It also creates bottlenecks when warehouses do not have enough people on the dock. From there, the packages move by conveyor into AmbiStack. Ambi Robotics designed AmbiStack as a multipurpose stacking system. It reads package information and builds pallets for the next stage of the warehouse process.
The key here is the handoff. Many warehouses already use automation. However, those systems often work in separate lanes. One machine may handle unloading. Another may handle sorting or stacking. Yet the warehouse still needs people or custom engineering to connect the pieces. This collaboration tries to make that connection smoother. The companies say the system can work with existing warehouse infrastructure. That means operators may avoid tearing apart a facility to use it.
Why Physical AI is important for warehouse automation
Physical AI means AI that controls machines doing physical work. That is important here because warehouse robots have to deal with moving boxes, shifting freight, conveyor timing and pallet stability. That creates a very different challenge from software that writes a paragraph or answers a question. A warehouse robot has to react to what sits in front of it. A box can arrive dented. A label can face the wrong way. A pallet can become unstable if the next case goes in the wrong spot.
This Ambi Robotics and Pickle Robot integration shows how that can work inside a warehouse. Pickle Robot handles the trailer unloading. AmbiStack takes over downstream by scanning and stacking cases for receiving. Together, the systems show how specialized robots can connect across a warehouse workflow.
“Warehouse operators shouldn’t have to choose between best-in-class technologies and seamless integration,” said Jim Liefer, CEO of Ambi Robotics. “As Physical AI transforms supply chains, interoperability will become increasingly important.”
AJ Meyer, founder and CEO of Pickle Robot Company, put the customer demand more directly: “Customers want automation that improves real-world throughput while fitting into existing operations.”
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A new warehouse automation system connects robotic trailer unloading with AI-powered pallet building, reducing manual handoffs on busy loading docks. (Ambi Robotics and Pickle Robot Company )
Why loading docks can slow warehouse operations
Anyone who has waited on a delayed package knows the supply chain can break down fast. Sometimes the problem starts long before a delivery truck reaches your home. Inbound logistics covers the work that happens when goods arrive at a warehouse. That includes getting boxes off trailers and moving them into the right workflow. It sounds pretty straightforward until you see the reality.
Trailers can be packed unevenly. Boxes can arrive in odd shapes. Warehouse teams also deal with tight schedules and physical strain. That is why loading docks have become such a major focus for automation. If robots can unload freight and pass it into a pallet-building system without constant human intervention, warehouses could move goods faster through one of the most labor-heavy parts of the operation.
How warehouse robots could change jobs
The big question is obvious. What happens to workers? Robots can take over repetitive and physically demanding tasks. That may reduce injuries and help warehouses handle labor shortages. It may also change which jobs companies need most.
Instead of spending a full shift unloading trailers, some workers may monitor the unloading and stacking systems. Others may step in when a package jams, a label fails to scan or a pallet needs human attention.
Still, that shift can feel unsettling. Automation often comes with a promise of safety and efficiency. Workers want to know where they fit in next. That is very important. A robot may move a box, but people still handle judgment calls, customer issues and fast decisions when the workflow changes.
Why retailers want connected warehouse robots now
Retailers and logistics companies feel pressure from several directions. Consumers expect faster shipping. Warehouses face staffing challenges. Meanwhile, e-commerce keeps creating more package volume. That creates a hard math problem. Companies need to move more goods without slowing down at the dock.
This Ambi Robotics and Pickle Robot setup gives warehouse operators another option. Instead of buying one giant system from a single vendor, they can connect specialized robotic tools that handle different parts of the job. That could give operators more flexibility. It could also help them avoid major redesigns, which can be expensive and disruptive. In other words, the robots are getting smarter. They are also starting to work together in more useful ways.
What this means to you
Even if you never set foot in a warehouse, this kind of automation can affect your life. When warehouses move goods more efficiently, stores may restock faster. Online orders may move with fewer delays. Returns may get processed more quickly. There is another side, too. More automation can reshape job roles inside warehouses. That means workers may need new training as companies bring in more robotic systems.
You may also hear fewer excuses when packages run late. If robots help warehouses operate with fewer bottlenecks, retailers may raise expectations for speed even more. That sounds convenient, but it also means the race for faster delivery keeps putting pressure on every part of the supply chain.
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Ambi Robotics and Pickle Robot Company say their integrated systems could help warehouses move inbound freight faster while easing physically demanding work. (Ambi Robotics and Pickle Robot Company )
Kurt’s key takeaways
What grabs me here is the handoff. One robot unloads packages from a trailer. Another scans and stacks them for the next part of the warehouse process. That is the piece that could change how loading docks operate. Warehouses are full of little delays that add up fast. If a package sits in the wrong place or waits for a person to move it to the next step, the whole process can slow down. This integration shows how warehouse robots may start taking over more of that middle work between the truck and the warehouse floor. Still, the human side deserves attention. These systems could reduce backbreaking work, which is a good thing. At the same time, they may change what warehouse workers are asked to do. The companies that make that transition clear, fair and useful for workers will be the ones to watch.
If robots can unload the truck, build the pallet and keep the warehouse moving, what job inside the warehouse gets automated next? Let us know by writing to us at CyberGuy.com.
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Google’s NotebookLM is adding a new way to catch up on your notes: TikTok-style AI videos. The new feature is rolling out to Google AI Ultra and Pro subscribers, allowing NotebookLM to generate 60-second vertical AI clips based on the sources you upload to the app.
The example shared by Google details Australia’s unsuccessful war on emus, pairing paper cutout-style AI art of emus with narration. It adds to some of the other ways NotebookLM lets you interact with your research, including by generating AI podcasts, cinematic videos, and visual explainers.
To generate a 60-second clip, head to NotebookLM on the web or app, select a notebook, and then choose “Video” from the Studio column on the right side of the screen. From there, select “Short,” choose the topic you’d like NotebookLM to focus on (or enter your own), and then hit the “Generate” button.
The feature is rolling out in English only for now, with support for free users coming “soon.”
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