> "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 algor

> "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. "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/ 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-centers-ibm-watson-project-fails-journalism-related/ 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-s-deepmind-is-trying-to-transform-health-care-but-should-an-ai-company-have-your-health-records 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.