The Fox News Health Newsletter brings you trending and important stories about health care, drug developments, mental health issues, real people's triumphs over medical struggles, and more.

'The Five' co-hosts discus Elon Musk's prediction that jobs will become like a'hobby' as AI progresses. 'FUTURE OF MEDICINE': Elon Musk is urging people to submit their medical scans to Grok for analysis, but doctors advise using caution when relying on artificial intelligence for health care insights. AI ART FOR SALE: Ai-Da, the world's first ultra-realistic robot artist, has produced a striking portrait of computing pioneer Alan Turing that will go under the hammer this month. SAN DIEGO, CA - JULY 14: Actor Robert Downey Jr. arrives at the "Iron Man 3" panel with Marvel Studios during Comic-Con International 2012 at San Diego Convention Center on July 14, 2012 in San Diego, California. IRON MAN'S FIGHT: Robert Downey Jr. might be devoid of iron, but he's sure got some steel.

'The Five' co-hosts discus Elon Musk's prediction that jobs will become like a'hobby' as AI progresses. Elon Musk is urging people to submit their medical scans to Grok for analysis, but doctors advise using caution when relying on artificial intelligence for health care insights. The Tesla and SpaceX CEO posted on X on Tuesday, encouraging his followers to submit X-rays, PET scans, MRIs or other medical images to the AI chatbot. "This is still early stage, but it is already quite accurate and will become extremely good," Musk wrote. WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

Scientists from Skoltech, Philips Research, and Goethe University Frankfurt have trained a neural network to detect anomalies in medical images to assist physicians in sifting through countless scans in search of pathologies. Reported in IEEE Access, the new method is adapted to the nature of medical imaging and is more successful in spotting abnormalities than general-purpose solutions. Image anomaly detection is a task that comes up in data analysis in many industries. Medical scans, however, pose a particular challenge. It is way easier for algorithms to find, say, a car with a flat tire or a broken windshield in a series of car pictures than to tell which of the X-rays show early signs of pathology in the lungs, like the onset of COVID-19 pneumonia.

Scientists from Skoltech, Philips Research, and Goethe University Frankfurt have trained a neural network to detect anomalies in medical images to assist physicians in sifting through countless scans in search of pathologies. Reported in IEEE Access, the new method is adapted to the nature of medical imaging and is more successful in spotting abnormalities than general-purpose solutions. Image anomaly detection is a task that comes up in data analysis in many industries. Medical scans, however, pose a particular challenge. It is way easier for algorithms to find, say, a car with a flat tire or a broken windshield in a series of car pictures than to tell which of the X-rays show early signs of pathology in the lungs, like the onset of COVID-19 pneumonia.

Millions of dollars are being spent to develop artificial intelligence software that reads x-rays and other medical scans in hopes it can spot things doctors look for but sometimes miss, such as lung cancers. A new study reports that these algorithms can also see something doctors don't look for on such scans: a patient's race. The study authors and other medical AI experts say the results make it more crucial than ever to check that health algorithms perform fairly on people with different racial identities. Complicating that task: The authors themselves aren't sure what cues the algorithms they created use to predict a person's race. Evidence that algorithms can read race from a person's medical scans emerged from tests on five types of imagery used in radiology research, including chest and hand x-rays and mammograms.

The COVID-19 pandemic has forced the closure of non-essential radiology services, with over 850,000 MRI and CT scans still outstanding in the UK, meaning patients waiting over two months for a consultation and upwards of 30 days to receive results. Independent market researcher IDTechEx has published a recent report exploring how image recognition in Artificial Intelligence (AI) represents a potential solution to the problem. The report highlights that AI technology can help to compensate for the lack of qualified staff by extending the responsibility of diagnosis beyond just experts and enables trained radiologists of all levels to approach the analysis process with the same tools and skillset so that more resources can be utilised simultaneously. In addition, AI accelerates the detection of abnormalities from medical scans by greatly diminishing reading times – sometimes by over 90% – which allows doctors to examine cases faster and see patients sooner. The key issue is that as of November 2020, AI may not be widely implemented enough throughout hospitals to make a difference.

But when it comes to how machine learning (ML) might benefit humanity, there's almost no field more promising than healthcare. Hardly a month passes when we don't hear about a new disease that machine learning models have learned to tag faster and more accurately than trained clinicians. ML is being used to help doctors spot tumors in medical scans, speed up data entry, and respond automatically to hospital patients' needs. These ML-powered breakthroughs come at a crucial time, as the shortage of doctors and specialists in the US and worldwide continues to grow. As our demand for doctors surpasses supply, we may well find ourselves depending on technology to help fill in the gaps.

All deep learning systems are vulnerable to adversarial attacks; while cause for concern, it also sparks research that may lead to better, more accountable, artificial intelligence. All deep learning systems are vulnerable to adversarial attacks, researchers warn. Tiny alterations to the input can cause these neural networks to classify pictures or other data totally incorrectly. While cause for concern, this also sparks research that may lead to better, more accountable, artificial intelligence. Artificial intelligence (AI) based on neural networks has made spectacular progress in recent years.

Medical artificial intelligence breaks a little too easily. Although AI promises to improve healthcare by quickly analysing medical scans, there is increasing evidence that it trips up on seemingly innocuous changes. Sam Finlayson at Harvard Medical School and his colleagues fooled three AIs designed for scanning medical images into misclassifying them by simply altering a few pixels. In one example, the team ever so slightly altered a picture of a mole that was first classified as benign with 99 per cent confidence. The AI then classified the altered image as malignant with 100 per cent confidence, despite the two images being indistinguishable to the human eye.