Interesting article. A common problem in healthcare is too much data and taking time to process relevant information and not leave information out. A difficulty in implementing AI/machine learning in healthcare is that everything still has to be verified by a provider and ends up taking significant amounts of time (as with Watson at MSK). Both Watson and this machine learning initiative synthesize data and do not make decisions but physicians still reviews all relevant data. However it will take time and development to ‘teach’ the machine learning in healthcare.
This concept is very interesting and potentially very useful. Wondering though if people would be concerned for the “big brother” like model that this confers (monitoring all types, taps, swipes and scrolls). Additionally, people would have to opt-in to this type of monitoring, which I see it difficult to convince adopters – particularly in patients with schizophrenia. That said anything that can help prevent suicide or identify high risk people is worth pursuing.
First, the purported price tag of $2.7 billion for drug development is based on research that is not sound. With that number, they factored in the opportunity cost of investing funds into R&D, essentially using it to double their figure. Additionally, the study was funded by pharmaceutical companies, creating clear conflicts of interest. However, that aside, much can be done to improve drug development and Roche’s efforts are a step in the right direction. I think machine learning will be particularly useful in drug discovery, more so than clinical testing and go to market strategy but is an interesting concept to explore regardless.
very interesting piece. As many other comments have addressed, I wonder if this technology will quickly become outdated as development in facial recognition, iris recognition, or even spot DNA sequencing are made. However, if short term developments can be made and it can help with TSA lines and setting up banking, I think it is a great idea and excellent application of machine learning.
Great piece, very interesting use of machine learning. A concern that you addressed and I’m not sure how machine learning can help is with the traffic burden in India, which is a major barrier to their success as a company. Perhaps one way they can get around this (as a I know they do in London) is using bicycle delivery during heavy traffic times with a smaller radius of delivery. This focus might allow it to differentiate itself from its competitors that you mentioned that have a greater share of the data.