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AI dominates China’s elite doctors in cancer diagnosis competition

[Credit: China Daily/Twitter]

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A custom-built AI designed to diagnose brain tumors and predict hematoma expansion dominated some of China’s best doctors in a competition last Saturday in Beijing. The AI, dubbed BioMind, ultimately scored 2:0 against its human competitors, comprised of 15 senior doctors from China’s premier hospitals.

BioMind was developed by a collaboration between a team from the Artificial Intelligence Research Center for Neurological Disorders at the Beijing Tiantan Hospital and researchers from the Capital Medical University. BioMind’s developers opted to feed the AI with data sets featuring tens of thousands of images depicting nervous-system-related diseases, which were retrieved from Tiantan Hospital’s archives stretching over the past decade.

Wang Yongjun, executive vice-president of Tiantan Hospital, stated that this training ultimately enabled the AI to become proficient in diagnosing neurological diseases such as meningioma and glioma with an accuracy rate of over 90%. According to Wang, such rates are comparable to the accuracy of a senior doctor, according to a report from state-owned Xinhua News.

During its the competition on Saturday, BioMind was able to correctly diagnose brain tumors with an accuracy rate of 87% out of a total of 225 cases. The AI was also able to complete its task in 15 minutes. In comparison, the team of 15 elite doctors was able to achieve an accuracy rate of 66% when diagnosing brain tumors, finishing the task in 30 minutes. Apart from this, BioMind was able to make correct predictions in 83% of brain hematoma expansion cases, while its human competition displayed a more conservative 63% accuracy.

Despite the AI’s strong performance against China’s elite doctors on Saturday, however, Cheng Jingliang, a professor of radiology at the First Affiliated Hospital of Zhengzhou University, stated that artificial intelligence systems for the medical field are still well into their infancy. According to Cheng, AI is already being used in hospitals to help doctors read images such as lung scans, but when it comes to giving full diagnoses to patients, artificial intelligence still lags far behind that of senior medical professionals.

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In a statement to China Daily, Paul Parizel from the Antwerp University Hospital in Belgium, who served as a member of the jury during last Saturday’s AI vs. human doctors competition, believes that systems such as BioMind would prove to be incredibly valuable when integrated to existing medical practices.

“It will be like a GPS guiding a car. It will make proposals to a doctor and help the doctor diagnose. But it will be the doctor who ultimately decides, as there are a number of factors that a machine cannot take into consideration, such as a patient’s state of health and family situation,” he said.

The United States initially led the artificial intelligence race, but over the years, China has steadily gained ground in the AI industry. Thanks to a population that is compliant to the application of new technologies, as well as a government that actively pushes AI researchers to push further, China is on track to overtake the United States in the near future. Last January alone, the Chinese government announced plans to build a $2.1 billion technopark in Beijing that is expected to house companies actively involved in AI research and development. The United States does not have a comparable initiative to date. This was confirmed by Jack Clark of Elon Musk-backed OpenAI, who previously stated that the country lacks a central national strategy on artificial intelligence.

“It is confusing that we have this technology of such obvious power and merit and we are not hearing full-throated support, including financial support,” Clark said.

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Simon is an experienced automotive reporter with a passion for electric cars and clean energy. Fascinated by the world envisioned by Elon Musk, he hopes to make it to Mars (at least as a tourist) someday. For stories or tips--or even to just say a simple hello--send a message to his email, simon@teslarati.com or his handle on X, @ResidentSponge.

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Tesla Model 3 gets perfect 5-star Euro NCAP safety rating

Tesla prides itself on producing some of the safest vehicles on the road today.

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Credit: Tesla Singapore/X

Tesla prides itself on producing some of the safest vehicles on the road today. Based on recent findings from the Euro NCAP, the 2025 Model 3 sedan continues this tradition, with the vehicle earning a 5-star overall safety rating from the agency.

Standout Safety Features

As could be seen on the Euro NCAP’s official website, the 2025 Model 3 achieved an overall score of 90% for Adult Occupants, 93% for Child Occupants, 89% for Vulnerable Road Users, and 87% for Safety Assist. This rating, as per the Euro NCAP, applies to the Model 3 Rear Wheel Drive, Long Range Rear Wheel Drive, Long Range All Wheel Drive, and Performance All Wheel Drive.

The Euro NCAP highlighted a number of the Model 3’s safety features, such as its Active Hood, which automatically lifts during collisions to mitigate injury risks to vulnerable road users, and Automatic Emergency Braking System, which now detects motorcycles through an upgraded algorithm. The Euro NCAP also mentioned the Model 3’s feature that prevents initial door opening if someone is approaching the vehicle’s blind spot.

Standout Safety Features

In a post on its official Tesla Europe & Middle East account, Tesla noted that the company is also introducing new features that make the Model 3 even safer than it is today. These include functions like head-on collision avoidance and crossing traffic AEB, as well as Child Left Alone Detection, among other safety features.

“We also introduced new features to improve Safety Assist functionality even further – like head-on collision avoidance & crossing traffic AEB – to detect & respond to potential hazards faster, helping avoid accidents in the first place. 

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“Lastly, we released Child Left Alone Detection – if an unattended child is detected, the vehicle will turn on HVAC & alert caregivers via phone app & the vehicle itself (flashing lights/audible alert). Because we’re using novel in-cabin radar sensing, your Tesla is able to distinguish between adult vs child – reduced annoyance to adults, yet critical safety feature for kids,” Tesla wrote in its post on X.

Below is the Euro NCAP’s safety report on the 2025 Tesla Model 3 sedan.

Euroncap 2025 Tesla Model 3 Datasheet by Simon Alvarez on Scribd

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USDOT Secretary visits Tesla Giga Texas, hints at national autonomous vehicle standards

The Transportation Secretary also toured the factory’s production lines and spoke with CEO Elon Musk.

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Credit: Elon Musk/X

United States Department of Transportation (USDOT) Secretary Sean Duffy recently visited Tesla’s Gigafactory Texas complex, where he toured the factory’s production lines and spoke with CEO Elon Musk. In a video posted following his Giga Texas visit, Duffy noted that he believes there should be a national standard for autonomous vehicles in the United States.

Duffy’s Giga Texas Visit

As could be seen in videos of his Giga Texas visit, the Transportation Secretary seemed to appreciate the work Tesla has been doing to put the United States in the forefront of innovation. “Tesla is one of the many companies helping our country reach new heights. USDOT will be right there all the way to make sure Americans stay safe,” Duffy wrote in a post on X. 

He also praised Tesla for its autonomous vehicle program, highlighting that “We need American companies to keep innovating so we can outcompete the rest of the world.”

National Standard

While speaking with Tesla CEO Elon Musk, the Transportation Secretary stated that other autonomous ride-hailing companies have been lobbying for a national standard for self-driving cars. Musk shared the sentiment, stating that “It’d be wonderful for the United States to have a national set of rules for autonomous driving as opposed to 50 independent sets of rules on a state-by-state rules basis.”

Duffy agreed with the CEO’s point, stating that, “You can’t have 50 different rules for 50 different states. You need one standard.” He also noted that the Transportation Department has asked autonomous vehicle companies to submit data. By doing so, the USDOT could develop a standard for the entire United States, allowing self-driving cars to operate in a manner that is natural and safe.

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Tesla posts Optimus’ most impressive video demonstration yet

The humanoid robot was able to complete all the tasks through a single neural network.

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Credit: Tesla Optimus/X

When Elon Musk spoke with CNBC’s David Faber in an interview at Giga Texas, he reiterated the idea that Optimus will be one of Tesla’s biggest products. Seemingly to highlight the CEO’s point, the official Tesla Optimus account on social media platform X shared what could very well be the most impressive demonstration of the humanoid robot’s capabilities to date.

Optimus’ Newest Demonstration

In its recent video demonstration, the Tesla Optimus team featured the humanoid robot performing a variety of tasks. These include household chores such as throwing the trash, using a broom and a vacuum cleaner, tearing a paper towel, stirring a pot of food, opening a cabinet, and closing a curtain, among others. The video also featured Optimus picking up a Model X fore link and placing it on a dolly.

What was most notable in the Tesla Optimus team’s demonstration was the fact that the humanoid robot was able to complete all the tasks through a single neural network. The robot’s actions were also learned directly from Optimus being fed data from first-person videos of humans performing similar tasks. This system should pave the way for Optimus to learn and refine new skills quickly and reliably.

Tesla VP for Optimus Shares Insight

In a follow-up post on X, Tesla Vice President of Optimus (Tesla Bot) Milan Kovac stated that one of the team’s goals is to have Optimus learn straight from internet videos of humans performing tasks, including footage captured in third person or by random cameras.

“We recently had a significant breakthrough along that journey, and can now transfer a big chunk of the learning directly from human videos to the bots (1st person views for now). This allows us to bootstrap new tasks much faster compared to teleoperated bot data alone (heavier operationally).

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“Many new skills are emerging through this process, are called for via natural language (voice/text), and are run by a single neural network on the bot (multi-tasking). Next: expand to 3rd person video transfer (aka random internet), and push reliability via self-play (RL) in the real-, and/or synthetic- (sim / world models) world,” Kovac wrote in his post on X.

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