Other than repeating phrases like AI, Deep Learning, and Blockchain, it’s really thin on how a public ledger is going to achieve any of this.
Therefore, GliaLab is proud to announce that we are building GliaBase, a global diagnostic healthcare platform utilizing AI and blockchain technology to provide users with universal and safe remote access to next-gen diagnostic tools and advice. Essentially, it is a platform powered by deep learning algorithms where users have access to top-tier diagnostic tools and healthcare information related to different diseases.
Yeeeaaaaah. How? There’s a lot of back-patting, some “imagine a world...” and just a dash of “Patients will... doctors will...” and zero specifics. Weasel words like “essentially” in front of a pat and low-content description of the actual platform also raises alarm bells for me.
This reeks.
Went to their site, and it seems the magic ingredient is that this is another ICO, complete with flashy splash page, and all of the usual red flags. They’re selling 30,000,000 GLIA (Gliatokens) which they assure you is a utility token. For something they haven’t built yet. The SEC is not going to be amused.
I do agree that there are a lot of buzz words involved, but if you actually talk to them, they have a decent team in place and they are one of a few legit startups that is doing a token sale. They are actually the only team I talked to thus far that told me to buy their tokens only if I want to use their product. Other people usually pitch me "XX returns on my investment". I might be participating in their token sale once they release more tech specs, so I talked to their CTO to learn more, and they do have a diagnostic demo with one algorithm related to breast cancer diagnosis. I am not a tech guy, so I was not able to judge the quality of the system, but their approach looks very interesting.
Bullshit. This is an ICO off the back of people’s desire to fight cancer. No FDA approval, no actual system, no substance, just GLIA “utility” tokens. If they want to defend their approach with something substantial, I’ll retract the “bullshit,” but this looks like the usual ICO scam. I took the liberty of filling out complaints with the SEC, FDA, and FTC yesterday and hopefully they can get some real answers.
> "Healthcare providers will be using our platform to gain access to next-gen diagnostic algorithms"
And what is the nature of these "next-gen diagnostic algorithms"? Are they the research results of medical experts published to medical journals using limited data sets on limited populations that promise to magically cure each and every malady of any patient population, or rather, is a more realistic example of a "next-gen diagnostic algorithm" something not useless, but less grandiose, such as algorithm that tries to address the likelihood of a narrow problem like sepsis occurring in the ICU?
When there are competing algorithms/research papers around the same topic, how do you decide which algorithm is best? And how "actionable" is the algorithm's prediction?
All this reminds me a little of IBM Watson marketing and MD Anderson Cancer Center, where the hype machine generated high expectations, and the results did not match the hype.
Still, DeepMind said a commercial product using AI is a ways off. Streams, the only product DeepMind has actually deployed, uses no AI. While DeepMind originally set out to use machine learning to improve an existing NHS algorithm to detect AKI, it said it never carried out that research. When DeepMind visited the Royal Free, it found the existing algorithm— which wasn’t half bad— was the least of the problem. Of far more concern were antiquated technology and Byzantine workflows that meant it took too long for doctors and nurses to act on blood test results. The real problems in medicine “are much more gritty and practical,” Suleyman said.
P.S. The buzzwords in the article include, among others: blockchain, artificial intelligence, token sale, revolutionize, deep learning algorithms... Maybe the only missing buzzword term is "quantum computing"?
And the St. Francis quote? A decent quote, yet if you have ever read any works on Francis such as the Little Flowers, it does not sound quite like him, and googling on the origin confirms doubts about the quote source.
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[ 4.5 ms ] story [ 19.0 ms ] threadTherefore, GliaLab is proud to announce that we are building GliaBase, a global diagnostic healthcare platform utilizing AI and blockchain technology to provide users with universal and safe remote access to next-gen diagnostic tools and advice. Essentially, it is a platform powered by deep learning algorithms where users have access to top-tier diagnostic tools and healthcare information related to different diseases.
Yeeeaaaaah. How? There’s a lot of back-patting, some “imagine a world...” and just a dash of “Patients will... doctors will...” and zero specifics. Weasel words like “essentially” in front of a pat and low-content description of the actual platform also raises alarm bells for me.
This reeks.
Went to their site, and it seems the magic ingredient is that this is another ICO, complete with flashy splash page, and all of the usual red flags. They’re selling 30,000,000 GLIA (Gliatokens) which they assure you is a utility token. For something they haven’t built yet. The SEC is not going to be amused.
Let me know when this changes.
And what is the nature of these "next-gen diagnostic algorithms"? Are they the research results of medical experts published to medical journals using limited data sets on limited populations that promise to magically cure each and every malady of any patient population, or rather, is a more realistic example of a "next-gen diagnostic algorithm" something not useless, but less grandiose, such as algorithm that tries to address the likelihood of a narrow problem like sepsis occurring in the ICU?
When there are competing algorithms/research papers around the same topic, how do you decide which algorithm is best? And how "actionable" is the algorithm's prediction?
All this reminds me a little of IBM Watson marketing and MD Anderson Cancer Center, where the hype machine generated high expectations, and the results did not match the hype.
"IBM pitched its Watson supercomputer as a revolution in cancer care. It’s nowhere close": https://www.statnews.com/2017/09/05/watson-ibm-cancer/
"MD Anderson Cancer Center’s IBM Watson project fails, and so did the journalism related to it": https://www.healthnewsreview.org/2017/02/md-anderson-cancer-...
Google's DeepMind Health also seems worth mentioning in this context: https://www.bloomberg.com/news/articles/2017-11-28/alphabet-...
Still, DeepMind said a commercial product using AI is a ways off. Streams, the only product DeepMind has actually deployed, uses no AI. While DeepMind originally set out to use machine learning to improve an existing NHS algorithm to detect AKI, it said it never carried out that research. When DeepMind visited the Royal Free, it found the existing algorithm— which wasn’t half bad— was the least of the problem. Of far more concern were antiquated technology and Byzantine workflows that meant it took too long for doctors and nurses to act on blood test results. The real problems in medicine “are much more gritty and practical,” Suleyman said.
P.S. The buzzwords in the article include, among others: blockchain, artificial intelligence, token sale, revolutionize, deep learning algorithms... Maybe the only missing buzzword term is "quantum computing"?
And the St. Francis quote? A decent quote, yet if you have ever read any works on Francis such as the Little Flowers, it does not sound quite like him, and googling on the origin confirms doubts about the quote source.