It will not imminently put medical experts out of work
FOUR years ago a woman in her early 30s was hit by a car in London. She needed emergency surgery to reduce the pressure on her brain. Her surgeon, Chris Mansi, remembers the operation going well. But she died, and Mr Mansi wanted to know why. He discovered that the problem had been a four-hour delay in getting her from the accident and emergency unit of the hospital where she was first brought, to the operating theatre in his own hospital. That, in turn, was the result of a delay in identifying, from medical scans of her head, that she had a large blood clot in her brain and was in need of immediate treatment. It is to try to avoid repetitions of this sort of delay that Mr Mansi has helped set up a firm called Viz.ai. The firm’s purpose is to use machine learning, a form of artificial intelligence (AI), to tell those patients who need urgent attention from those who may safely wait, by analysing scans of their brains made on admission.
That idea is one among myriad projects now under way with the aim of using machine learning to transform how doctors deal with patients. Though diverse in detail, these projects have a common aim. This is to get the right patient to the right doctor at the right time.
In Viz.ai’s case that is now happening. In February the firm received approval from regulators in the United States to sell its software for the detection, from brain scans, of strokes caused by a blockage in a large blood vessel. The technology is being introduced into hospitals in America’s “stroke belt”—the south-eastern part, in which strokes are unusually common. Erlanger Health System, in Tennessee, will turn on its Viz.ai system next week.
The potential benefits are great. As Tom Devlin, a stroke neurologist at Erlanger, observes, “We know we lose 2m brain cells every minute the clot is there.” Yet the two therapies that can transform outcomes—clot-busting drugs and an operation called a thrombectomy—are rarely used because, by the time a stroke is diagnosed and a surgical team assembled, too much of a patient’s brain has died. Viz.ai’s technology should improve outcomes by identifying urgent cases, alerting on-call specialists and sending them the scans directly.
The AIs have it
Another area ripe for AI’s assistance is oncology. In February 2017 Andre Esteva of Stanford University and his colleagues used a set of almost 130,000 images to train some artificial-intelligence software to classify skin lesions. So trained, and tested against the opinions of 21 qualified dermatologists, the software could identify both the most common type of skin cancer (keratinocyte carcinoma), and the deadliest type (malignant melanoma), as successfully as the professionals. That was impressive. But now, as described last month in a paper in the Annals of Oncology, there is an AI skin-cancer-detection system that can do better than most dermatologists. Holger Haenssle of the University of Heidelberg, in Germany, pitted an AI system against 58 dermatologists. The humans were able to identify 86.6% of skin cancers. The computer found 95%. It also misdiagnosed fewer benign moles as malignancies.
There has been progress in the detection of breast cancer, too. Last month Kheiron Medical Technologies, a firm in London, received news that a study it had commissioned had concluded that its software exceeded the officially required performance standard for radiologists screening for the disease. The firm says it will submit this study for publication when it has received European approval to use the AI—which it expects to happen soon.
This development looks important. Breast screening has saved many lives, but it leaves much to be desired. Overdiagnosis and overtreatment are common. Conversely, tumours are sometimes missed. In many countries such problems have led to scans being checked routinely by a second radiologist, which improves accuracy but adds to workloads. At a minimum Kheiron’s system looks useful for a second opinion. As it improves, it may be able to grade women according to their risks of breast cancer and decide the best time for their next mammogram.
Efforts to use AI to improve diagnosis are under way in other parts of medicine, too. In eye disease, DeepMind, a London-based subsidiary of Alphabet, Google’s parent company, has an AI that screens retinal scans for conditions such as glaucoma, diabetic retinopathy and age-related macular degeneration. The firm is also working on mammography.
Heart disease is yet another field of interest. Researchers at Oxford University have been developing AIs intended to interpret echocardiograms, which are ultrasonic scans of the heart. Cardiologists looking at these scans are searching for signs of heart disease, but can miss them 20% of the time. That means patients will be sent home and may then go on to have a heart attack. The AI, however, can detect changes invisible to the eye and improve the accuracy of diagnosis. Ultromics, a firm in Oxford, is trying to commercialise the technology and it could be rolled out later this year in Britain.
There are also efforts to detect cardiac arrhythmias, particularly atrial fibrillation, which increase the risk of heart failure and strokes. Researchers at Stanford University, led by Andrew Ng, have shown that AI software can identify arrhythmias from an electrocardiogram (ECG) better than an expert. The group has joined forces with a firm that makes portable ECG devices and is helping Apple with a study looking at whether arrhythmias can be detected in the heart-rate data picked up by its smart watches. Meanwhile, in Paris, a firm called Cardiologs is also trying to design an AI intended to read ECGs.
Eric Topol, a cardiologist and digital-medicine researcher at the Scripps Research Institute, in San Diego, says that doctors and algorithms are comparable in accuracy in some areas, but computers have the advantage of speed. This combination of traits, he reckons, will lead to higher accuracy and productivity in health care.
Artificial intelligence might also make medicine more specific, by being able to draw distinctions that elude human observers. It may be able to grade cancers or instances of cardiac disease according to their risks—thus, for example, distinguishing those prostate cancers that will kill quickly, and therefore need treatment, from those that will not, and can probably be left untreated.
What medical AI will not do—at least not for a long time—is make human experts redundant in the fields it invades. Machine-learning systems work on a narrow range of tasks and will need close supervision for years to come. They are “black boxes”, in that doctors do not know exactly how they reach their decisions. And they are inclined to become biased if insufficient care is paid to what they are learning from. They will, though, take much of the drudgery and error out of diagnosis. And they will also help make sure that patients, whether being screened for cancer or taken from the scene of a car accident, are treated in time to be saved.
Source: The Economist
Samsung to invest $115 billion in its foundry business by 2030
Samsung is earmarking $9.5 billion a year for Samsung LSI and Samsung Foundry.
Samsung Electronics is one of the largest semiconductor players around, and the manufacturer is investing $115 billion (133 trillion won) over the next 12 years to take on Qualcomm and Intel. Samsung says its goal is to become the world leader in semiconductors and logic chips, and the company will invest $9.5 billion a year from now through 2030.
Samsung will invest $63.4 billion (73 trillion won) toward domestic R&D — where it is looking to add 15,000 jobs to “bolster its technological prowess” — and spend $52 billion (60 trillion won) toward production facilities that will make the logic chips. Samsung has long been the dominant player in the memory business, but with that market shrinking the South Korean manufacturer will be looking to diversify.
While the $115 billion seems like a staggering amount at first, it’s in line with what Samsung has been spending in recent years. Just last year alone Samsung invested over $15 billion in R&D, and Intel also spent over $10 billion toward developing new products.
LG V50 ThinQ 5G launch in South Korea delayed
The delay is due to LG wanting to further optimize the Qualcomm Snapdragon 855 chipset and Qualcomm X50 5G modem inside of the V50. LG also said it’s working with Qualcomm and South Korean carriers to improve 5G service and phone interoperability.
LG V50 ThinQ 5G price & release date: What we know so far (it’s not much)
LG didn’t say when the V50 will be available in South Korea. Android Authority reached out to LG for comment on a new release date and whether the delayed launch in South Korea will affect the U.S. launch, but did not receive a response by press time.
The delay comes at a bad time for LG, which saw rival Samsung launch its first 5G smartphone April 5 in South Korea. LG likely had hoped to use the Galaxy S10 5G’s launch momentum for its own 5G smartphone, but now we don’t know when the V50 will debut.
That said, LG might have dodged a very big bullet by delaying the V50’s launch. Business Koreareported last week that Galaxy S10 5G owners have struggled with poor 5G connectivity and an inability to switch to 4G LTE. Samsung pushed out an update that supposedly addressed the issues, but the update didn’t help much.
Samsung snubs Apple on 5G modem supply, leaving few good options for the 2020 iPhones
Thanks to the patent war with Qualcomm reaching a crescendo mode, last year Apple’s iPhones shipped exclusively with “Intel inside” as far as cellular connectivity is concerned. That, however, is not an ideal solution for Apple, as Intel’s modems are behind the curve when it comes to features, so it has been shopping around for other options.
Apple could go with Samsung, Huawei or MediaTek’s 5G modems, but each of those choices comes with severe drawbacks. Samsung will likely charge an arm and a leg for its 5G brainchild, America’s homeland security institutions would balk at Huawei’s involvement due to geopolitical considerations, while MediaTek simply isn’t up to par yet.
SAMSUNG’S 5G MODEM OPTION IS OUT FOR APPLE, BUT WHOSE IS IN?
Surprise, surprise, even those unpalatable options have now become harder to pick from, as Korean media is reporting today that Samsung has declined Apple’s advances for its Exynos 5100 5G modem. Not only does the company need its production for the Galaxy S10 5G that will be shipping tomorrow in Korea but it could very well need it for the Note 10, too.
Samsung, it turns out, is simply unable to churn out 5G modems in the quality and quantity that Apple would demand, or so it claims. According to one “electronics industry official” there:
Apple inquired about the supply of 5G modem chip from Samsung Electronics System LSI division. However, we know that Samsung Electronics System LSI answered that the supply volume of its smartphone 5G modem chip is insufficient.
There you have it – unless Apple resolves the bad blood between the companies, Qualcomm is likely to sit its 5G push out, so the last remaining option is for Apple to go it alone, either by acquiring Intel’s wireless modem assets or starting from scratch (highly unlikely). All of these options mean either a lot of extra expenses for Apple in order to deliver a 5G iPhone in 2020, or falling behind the competition by launching one that is a cycle or two behind.
Last summer, insiders claimed that they have seen internal Intel communication regarding a memo that Apple sent Chipzilla. In it, Apple warns that it might no longer need Intel’s wireless modem designs, including the 5G ones, starting with the 2020 iPhone crop. Intel reportedly halted research in this area and might disband the whole 5G modem undertaking, as Apple was its largest and perhaps sole customer.
5G gets going and Apple’s 2020 iPhones can’t go FOMO
South Korea just launched its nationwide 5G network, with the Galaxy S10 5G being its poster child. Upon the phone’s release there tomorrow, Korea will have all of its largest networks offering 5G plans. In fact, Korea Telecom announced three 5G price tiers. Among those, there is a “Super Plan” that offers truly unlimited 5G data without speed caps, and this one will go for the equivalent of $70, a pretty good price no matter how you slice it. In fact, the Super 5G Plan is somewhat cheaper than the current unlimited 4G LTE plans in Korea, so the 5G future seems bright, and we are expecting more and more 5G handsets to enter the fray this year, especially towards the tail end of 2019.
A true nationwide shift to 5G networks is not happening this year in the US anyway, so iPhone users won’t be missing all that much until then. Next year, however, most of the flagship phones of the spring season will probably have some sort of 5G connectivity support, be it with a Qualcomm, Samsung or Huawei modem, and Apple could feel the pinch in that regard. If in the fall of 2020 Apple hasn’t solved its 5G modem supply options, however, there might be image and perception consequences. As virtually all of Apple’s 5G avenues have dried up and will incur extra expenses, patching thing up with Qualcomm would be a smart solution so we’ll keep our eyes on the patent lawsuit as it moves through the court system.