I agree with a lot of the points that have been made in the comments so far. You specifically mention that machines are not here to replace doctors. From many patients’ perspective, though, I think there will be a huge reluctance to start accepting “robots” as doctors in any context. Healthcare is one of the few industries where people still value the human element of a physician, and I think there is a fear that there is a slippery slope when it comes to making recommendations based on machine learning algorithms. I agree with Michael’s comment that this is not a field where there is room for error; a wrong diagnosis or treatment recommendations can have severe consequences. This opens up these companies to a whole host of liabilities and, more importantly, can run the risk of harming a patient if there is any mistake in the data or analysis.
I think this is a fascinating and exciting concept for the future of the medical industry. Technology in the healthcare industry is always interesting to me; the anecdote at the beginning of this article is inspiring and uplifting. What would have happened if something had gone wrong because there was a mistake with the 3D model? In this industry, I think technological advancement is hugely important but will inevitably move more slowly than it should. Despite the huge benefits 3D printing can bring for improving health outcomes, a few mistakes have the potential to seriously jeopardize the progress being made.
I thought this was a really interesting example of how a company can think innovatively to solve a problem from multiple angles. You raise a good point in the end about how important it is for them to reach critical mass in order to reduce their costs. In the short-term, might it be worth subsidizing the cost to achieve scale faster? If this has the potential to be a long-term shift in the way we recycle, maybe a short-term investment is worth it.
Crowd-sourcing innovation is a really interesting concept here; one thing that I think companies have to keep in mind is the selection bias of the people who are interacting with the company. Putting out a “contest” for chip flavors is a cool idea; however, the people who are spending time to respond to this challenge are not necessarily representative of the target customer that Pepsi is going after. For example, maybe this is only attracting customers who are already super loyal, while Pepsi is trying to broaden its customer base. In this case, offering a niche product might be completely ineffective in broadening its market.
I think machine learning for investors is a really interesting concept. One “concern” or question I have is whether or not we can find ourselves in a precarious situation where certain investors are at massively unfair advantages due to their machine learning capabilities. While investors are not receiving any non-public information about the companies they are investing in, in reality the individual investors are operating with a relative lack of information because of their inability to obtain or process such vast amounts of data. Will machine learning put individual investors at too much of a disadvantage?
I agree with Sterling’s comment that there is huge inertia when it comes to technology advancements in a field like healthcare. The personal nature of the healthcare industry raises the stakes and threshold for technological advances (e.g. in other industries, a flaw in technology might not be a matter of life and death). I thought that one of the most interesting points the author made in this article was the benefit of additive manufacturing as a tool for testing. Medtronic can give themselves a huge competitive advantage if they find themselves in a position where they can prototype products quicker than their competitors. By utilizing additive manufacturing, Medtronic can iterate and enhance their R&D process, without taking on the immediate risk of direct patient contact in the near-term.