Mobile phone health rules need update, warns US watchdog

Radio emission standards may go up or down


The US Government Accountability Office (GAO) is urging legislators to update the health protection requirements required of mobile phone manufacturers, but indicated some emissions limits might be increased rather than reduced.

In a report, the agency recommended a rethink after finding that the current rules for phone radio emissions were last revised by the Federal Communications Commission (FCC) in 1996, and that there has been a huge amount of new research into the issue since then. While the GAO says there's no evidence of mobiles causing cancer, it nevertheless recommends a new look, given technological advances.

In 1996 the FCC decided that the maximum safe exposure to radio waves from mobiles was 1.6 watts per kilogram, averaged over one gram of tissue. The measurements are taken with the phone between 1.5 and 2.5cm from the head and the FCC samples about one per cent of US phones a year to ensure compliance.

The GOA report noted that in 2006 the IEEE suggest a safe limit of two watts per kilogram averaged over 10 grams of tissue, which according to the IEEE "represents a scientific consensus on RF energy exposure limits." Other experts consulted pointed out that the safe limits were set at a fiftieth of what would be considered directly harmful to humans.

However, consumer groups were less happy with the situation, with several calling for a decrease in any amount of radio emissions so close to the brain. Labeling of the specific radio strengths of handsets, as has been proposed in San Francisco and elsewhere, was also recommended.

The current testing strategy also needs to be revised, the report suggests. At present there's no formal structure for testing phones held to the body in belt clips or attached via a headset. Any new legislation would need to sort out metrics for these situations, since they mean direct bodily contact with the device.

"While the GAO report indicates there is not evidence to suggest using a cell phone causes cancer, it's important that safety standards are current and account for changing trends in cell phone use and technology," said Representative Anna Eshoo, one of three Democratic legislators pushing for a review.

"As the number of users of wireless technology grows exponentially, the FCC should reevaluate acceptable radiation emission levels to determine if they need to be adjusted."

Complaints about mobile phones and health have been going on pretty much since their invention, and while has been much research the scientific consensus has been moving towards the view that there is no direct threat at present, although the jury will be out for some time to come.

In 2011 the World Health Organization did classify mobile phones and "possibly carcinogenic to humans" and listed them as Group B carcinogenic agents due to their potential to induce brain cancer. It should also be pointed out that other substances listed in the category include alcohol, coal burnt indoors, and getting malaria. ®

Broader topics


Other stories you might like

  • Can AI transformer models help design drugs and treat incurable diseases?
    From protein prediction to drug generation, neural networks are revolutionizing medication

    Special report AI can study chemical molecules in ways scientists can't comprehend, automatically predicting complex protein structures and designing new drugs, despite having no real understanding of science.

    The power to design new drugs at scale is no longer limited to Big Pharma. Startups armed with the right algorithms, data, and compute can invent tens of thousands of molecules in just a few hours. New machine learning architectures, including transformers, are automating parts of the design process, helping scientists develop new drugs for difficult diseases like Alzheimer's, cancer, or rare genetic conditions.

    In 2017, researchers at Google came up with a method to build increasingly bigger and more powerful neural networks. Today, transformer-based models are behind some of the largest AI systems and typically learn patterns from vast amounts of text. They're versatile and can process different forms of language from code to ancient scripts scribbled thousands of years ago.

    Continue reading
  • Algorithm can predict pancreatic cancer from CT scans well before diagnosis
    Software picks up subtle clues human doctors miss

    AI algorithms can predict whether a patient will develop pancreatic cancer years before an official diagnosis, or so this research suggests.

    Tens of thousands of people in the US are diagnosed with pancreatic ductal adenocarcinoma – the most common type of pancreatic cancer – every year. Less than 10 percent of patients live more than five years after diagnosis.

    Detecting the disease earlier could boost survival rates by up to 50 percent, it is believed. But doctors don't right now have any methods that screen patients for early signs of pancreatic cancer. Now, a team of researchers led by Cedars-Sinai Medical Center, a top non-profit hospital based in Los Angeles, California, believe AI could be up to the task.

    Continue reading
  • 'Virtually no difference' between AI and humans in diagnosing prediabetes
    Is that... a good or bad thing?

    Deep-learning algorithms have shown themselves equal to humans in detecting patients at high-risk of developing Type-2 diabetes by analyzing CT scans of their pancreases, according to a research paper published on Tuesday.

    Type-2 diabetes is estimated to affect 11.3 percent of the US population, or at least 37 million people. Type-2 diabetes can lead to issues with circulatory, nervous, and immune systems, increasing the risk of heart disease and strokes.

    Those with the initial form, prediabetes, can repair their body's insulin resistance, so they don't develop the full-blown condition, if they change their diets and exercise habits. American health officials reckon 38 percent of the US adult population, some 96 million people, have prediabetes.

    Continue reading

Biting the hand that feeds IT © 1998–2022