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Researchers develop artificial intelligence that can identify cancer cells
Scientists from Osaka University in Japan have developed artificial intelligence (AI) that can identify different types of cancers based on microscopy images of their cells. The AI was also able to determine whether the cancer cells were resistant to radiation, and further learned the differences between human and animal cancers. Since the accuracy and timeliness of traditional methods of identifying cancer cells are prone to delays and errors, an accurate and automated system for accomplishing this would be beneficial to cancer research and treatment overall. The results of the scientists’ research were published in the December 2018 issue of Cancer Research.
The type of AI developed for the cell identification is called a convolutional neural network (CNN); it’s loosely based on the connectivity patterns used by neurons in the brain and primarily used for classifying images. As described in their publication, the scientists used a training set of 10,000 images each of human cervical cancer cells (ME-180) and mouse squamous cancer cells (NR-S1), e.g., the thin, flat types of cells found on the surface of the skin and thin linings around various organs throughout the body. They also included images of radioresistant clones with the set, and ultimately obtained a 96% accuracy in a 2,000 image test.
Because the types of cells in a single cancerous tumor can vary widely, identifying the specific cells present is important for determining the best treatment. Thus, having a tool to provide this information quickly and accurately could have a significant impact. The Osaka team hopes to expand the types of cancers their AI can identify and ultimately establish a universal system that can identify all cancer cell types.
Using artificial intelligence in the battle against cancer is being explored throughout the world as the number of uses devised expands. In one notable instance, a team of scientists from The Institute of Cancer Research in London and the University of Edinburgh has developed an AI technique called REVOLVER (repeated evolution of cancer) which identifies DNA mutation patterns in cancers to predict the ways they will change in response to treatment. Similar to how bacteria become resistant to antibiotics, so too can cancers become resistant to the drugs used against them. By removing the unpredictability variable in cancer behavior, scientists would be able to stay ahead of the disease’s progress and tailor treatments accordingly.
The collaboration between AI and healthcare overall is growing, not just in cancer research – even Google is making contributions to the field. Fortunately, the agencies regulating developments are also attuned to the changes. Earlier this year at the AcademyHealth 2018 Health Datapalooza, the U.S. Food and Drug Administration (FDA) Commissioner Scott Gottlieb, MD signaled the agency’s positive position towards the field. “AI holds enormous promise for the future of medicine, and we’re actively developing a new regulatory framework to promote innovation in this space and support the use of AI-based technologies,” he stated at the event. He also referred to the agency’s plans to streamline their regulations and tools to be sufficiently flexible to handle the rapid pace of advancements and “focus on the ways in which real-world data flows.”
Tesla Cybercab stands to gain from new rules that the Trump Administration is aiming to enforce on autonomous vehicles. On Thursday, NHTSA, under the Trump Administration’s U.S. Department of Transportation, commenced rulemaking on the Federal Motor Vehicle Safety Standards (FMVSS).
This effort aims to eliminate the mandate for manual brake pedals in vehicles that are designed to be driven exclusively by automated driving systems. This would impact the Tesla Cybercab, which the company has stated would operate without a steering wheel or pedals.
Tesla Cybercab launch is imminent after latest sighting at Giga Texas
The Trump Administration is looking to revise FMVSS No. 135, which requires standard braking systems on light-duty vehicles.
Currently, the regulation requires light-duty cars to use traditional manual braking systems that allow operators to slow the vehicle. With the advent of self-driving in the U.S., these regulations need updating, and these are the changes that could come to FMVSS No. 135:
- Removes requirements for hand- or foot-operated brake controls for vehicles designed never to be operated by a human. Existing rules still apply to AVs that retain manual controls.
- All subject vehicles must still meet the same stopping distance performance criteria via alternative testing procedures.
- While this update ensures AVs can physically stop when commanded, NHTSA is separately developing safety performance requirements for AVs in real-world driving scenarios.
- NHTSA will continue to use its broad defect enforcement authority to investigate unsafe ADS behavior and oversee recalls.
As autonomy becomes a greater part of passenger travel, these types of rule adjustments will be more than reasonable. It will give manufacturers the ability to self-certify their vehicles and avoid any red tape that could ultimately delay the deployment of these vehicles.
Administrators are also incredibly excited about the opportunity to play a role in the advancement of self-driving vehicles.
“We are at the cusp of the greatest technological revolution in vehicle technology since the innovation of the Model T,” NHTSA Administrator Jonathan Morrison said. “If we want America to lead the way, we have to reimagine our regulatory framework. That’s why under Secretary Sean Duffy’s AV Framework, NHTSA is tearing down pointless barriers to innovative designs while strengthening the fundamental safety requirements that matter and holding AV developers accountable for safe performance.”
The Cybercab entered mass production at Gigafactory Texas in April. Tesla ultimately plans to push the vehicle into its Robotaxi fleet, potentially when frameworks like these are established.
Tesla plans to boost production at its Gigafactory Berlin plant in Germany following a sharp rebound in sales and demand in Europe after a softer 2025.
The plans put Tesla in a better position to compete with strengthening companies in Europe and potentially other markets; demand indicators show Tesla is much better off than in 2025.
Last year was a tough year for Tesla in terms of overall demand in Europe. The company produced over 200,000 vehicles at the German plant last year, a soft figure compared to the 375,000 vehicles Tesla lists as its current capacity at the factory.
🚨 Tesla said this morning it will ramp up production at Gigafactory Berlin to a volume of 7,500 vehicles per week.
This is a 20 percent boost in production. Tesla will hire 1,000 new employees to help with the increase.$TSLA pic.twitter.com/kravKfRO5n
— TESLARATI (@Teslarati) June 25, 2026
Tesla’s overall European sales dropped significantly last year due to a variety of factors. However, sales are rebounding, and demand is strong once again, and only getting stronger. Tesla is now planning to bump production of Model Y vehicles at Giga Berlin upward by about 20 percent. It will also bring 1,000 new jobs to the plant.
Tesla confirmed the details of its planned production expansion in Germany this morning. It is a strategy to keep up with strengthening demand.
In Q1, Tesla saw a record 61,000 vehicles produced at Giga Berlin. European registrations rebounded sharply, with Model Y seeing 117 percent increases in March 2026 compared to last year. Germany alone saw stark increases, with a quadrupling in registrations to 9,252 units.
This trend continued in other key European markets, including France, Denmark and Sweden. Tesla registrations were up over 46 percent in some of these markets, and Model Y continued its trend as a top BEV in the market.
Demand has been recovering strongly in 2026, giving Tesla a reason to expand production efforts at the factory. These increases signal management’s confidence in sustained or growing European pull for Berlin-built vehicles.
Tesla is being sued by the family of the woman who was killed in a Texas crash involving a Model 3. The driver, who is also being sued, claimed the vehicle was operating on Autopilot mode, but Tesla executives have come out challenging that claim, stating that the driver of the vehicle overrode the system.
The lawsuit was filed by 76-year-old Martha Avila’s daughter and her husband, who allege a “design defect” involving a Tesla and a failure to warn. The suit alleges negligence against Tesla and the driver, Michael Butler.
Butler “stated he was operating with an automated driving assistance system engaged at the time of the crash,” the Harris County Sheriff’s Office said in a statement. He showed no signs of intoxication and was cooperative, the Sheriff’s Office said, according to NBC News.
Just after reports of the crash and numerous headlines that immediately blamed Tesla’s Autopilot suite, both Tesla CEO Elon Musk and Head of AI Ashok Elluswamy challenged that. Musk said the crash made “no sense” given that Tesla Autopilot and Full Self-Driving do not travel at the speeds the door cameras captured the car traveling at, which Tesla says was 73 MPH.
Tesla finally clarifies fatal Texas crash, confirms driver manually overrode acceleration
Elluswamy also revealed that Tesla data showed Butler overrode the system by pressing the accelerator to 100%, and that the pedal was compressed fully even after the car had crashed. Tesla has not released this data to the public, likely because it is communicating with agencies like the NHTSA on an investigation.
The suit uses a Washington Post analysis of government data that “identified at least 17 fatal incidents linked to Tesla Autopilot.”
This is far from the first time an accident has been blamed on Autopilot. A fatal crash in Texas was blamed on Autopilot several years ago, but when Tesla released data to the NTSB, which was investigating the crash, Autopilot was not available where the crash occurred, and Autosteer was never enabled, meaning the car was manually controlled at the time of the accident.
“Application of the accelerator pedal was found to be as high as 98.8 percent,” the NTSB said in their findings. The highest recorded speed in the five seconds leading up to the impact was 67 miles per hour. The area where the crash occurred is residential, and Texas State laws… pic.twitter.com/XGD97NHVZ2
— TESLARATI (@Teslarati) March 18, 2026
More information on the accident will be released as Tesla works with agencies to find the cause of the crash. From personal experience, it is hard to imagine Tesla Autopilot or FSD operating in this manner. It drives sometimes too cautiously in residential areas in parking lots, at least in my experience. Speeding happens, but at this rate in this type of area, it is hard to believe.
We look forward to more details being released with time.
Tesla Cybercab stands to gain from new Trump autonomy rules
Tesla plans production boost at Giga Berlin following rebound in Europe
