Michael Girouard

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Very interesting article about Pfizer trying to harness the power of the masses in drug discovery and innovation. My biggest concern is on the proper compensation for these innovators, particularly if some of this innovation results in blockbuster drug discovery. I do appreciate the design thinking mentality that Pfizer is taking, but I do find it interesting that they chose to use aging as the topic, as their primary customer (older adults) are less likely to be aware of or have the capabilities to engage with this technology. This method of drug discovery seems very much in line with current trends of megapharmaceutical companies like Pfizer acquiring small startups, effectively outsourcing innovation, as opposed to funding their own R&D as they did in the 80s and 90s.

On November 15, 2018, Michael Girouard commented on Solidifying the impact of open data innovation in the government – NYC311 :

This is a really interesting way for the government to engage with data and with younger generations who might be more interested in innovation and data science. However, I worry that the vast majority of citizens will still be unable to engage with this data due to a lack of data analytic capabilities. Or worse, they may analyze the data in faulty ways and arrive at fraught conclusions. As with all data, it is critically important to understand how the data is collected and its limitations to be able to correctly interpret any findings. Do citizens even have access to the computing power to analyze such vast quantities of data? These are a few concerns I have with this open innovation in this context, particularly in light of the type of data the government might release. Lastly, I wonder if the computing systems are similar enough across branches of government to be able to aggregate the data in a meaningful way. Great work!

On November 15, 2018, Michael Girouard commented on Printing the Future of Athletic Shoes at Adidas :

It was interesting to see a company really put in practice the rapid trial and error philosophy that we’ve learned about in FIELD and TOM (spaghetti challenge, Shad challenge). I wonder if and how this has given them a competitive edge against other players in the space, or if most companies adopt this design thinking. Given the potential of this technique for Adidas, I think they should consider not only partnering with Carbon, but acquiring it altogether. In terms of how they will convey value to the customers, I think the first question to answer is: “Who are the customers?” Are they professional sports players, high end retail customers, or retail customers more generally. In line with their previous strategy, I think focusing on function and professional endorsements will help them create the most demand.

On November 15, 2018, Michael Girouard commented on Dear Adidas, you’ve mastered limited releases…what’s next? :

I agree that this is definitely the new wave of shoemaking, especially in the luxury retail or professional sports community. However, I wonder how the production capabilities of 3D printing can be scaled to appeal to a wider customer base and how they might reduce the costs associated with this new technology. I also wonder at which price point the market will accept. Will they be able to create demand to sustain the $300 (or even higher) price? In your reading, did you get any sense that Adidas is capitalizing on sustainability and waste-reduction trends that this technology seems to enable? Finally, one point in your article that I disagree with is the concern about in store vs online retail, as it seems that footwear is still primarily an in-store, experiential purchase despite the dominance of online retail.

On November 14, 2018, Michael Girouard commented on Machine Learning and Radiologists: Friends or Foes? :

Really nice article and very well-written. There is a lot of hype about computers and machine learning in healthcare, particularly in radiology has you have noted. People do talk in the extremes, saying that radiologists will be rendered useless by machine learning in the future. However, radiologists do much more than just interpret images. They determine proper scan parameters given the clinical question at hand, and they administer proper contrast agents (which can have serious side effects) taking into account the patient’s clinical history. Furthermore, human anatomy is quite variable from person to person, study to study, moment to moment (e.g. intestinal movement), so I imagine a machine learning algorithm would require exponentially more images and interpretations to learn from given this dynamic nature. I loved the FIFO comment and agree that prioritization is key in this field. At this point, our only prioritization is really clinical presentation, but it seems like there is room for improvement with AI.

On November 14, 2018, Michael Girouard commented on Sharing Data to Advance Cancer Care at Memorial Sloan Kettering Cancer Center :

Very interesting article. It reminds me a lot of the debate over Henrietta Lacks, a patient whose cancer cells were cloned and on which years and years of research was performed without explicit consent from her or compensation. Patient deidentification is key for this type of work. However, a patient’s genetic information is the ultimate identifier, which complicates these questions especially as direct to consumer sequencing becomes more widely available. Finally, in the spirit of collaboration and wanting to share data, I actually wonder if these hospitals are disincentivized to share their data or organize it in a digestible and transferable form because they are able to retain proprietary rights to the data, from which they can extract scientific findings and compensation.

On November 14, 2018, Michael Girouard commented on Love in a Hopeless Place: Machine Learning at OkCupid :

Fascinating read! I had heard that OkCupid used data but had never realized the extent. However, I wonder if they are fulling using their data capabilities at this point. How are they following up with couples that meet? How do they learn from good dates and bad dates, and how to they know when matches have actually worked? To me, that is the crux of their machine learning capabilities. I do have some reservations though. For example, there are likely important social implications of matching people who are similar together. By which criteria do they measure similarity (interests, geography, race, socioeconomic status)? Finally, how do they maintain their competitive edge with the network effects they have created in such a growing, fragmented market with many new entrants?

Great article! This definitely resonated with me, as I remember countless times feeling frustrated that my neonatal and pediatric patients were unable to tell me what they were feeling or experiencing and relying solely on whatever objective measures we had on hand from our physical exam and the machines/labs/scans we had. There is so much untapped potential in the data that these hospitals generate (e.g. temporal associations between signs/symptoms and clinical outcomes that could serve as warning signs). I do worry about our reliance on papers and clinical trials to be the only driving force of change in the field, as typically it takes 5-10 years for these recommendations to become standard of care. Finally, I think you nailed it when you brought up the gravity of the “Toronto” mistake in healthcare, clearly much graver than an incorrect Jeopardy answer.

On November 14, 2018, Michael Girouard commented on Sephora and Artificial Intelligence: What does the future of beauty look like? :

Very interesting application of artificial intelligence. The premise of the program does remind me, however, of the Dove “Evolution” ad, where a woman was digitally transformed into an almost unrecognizable, super-model version of herself via makeup and digital editing. Though this was certainly the extreme case, I worry that the digital application of these products might not be as accurate or dramatic as the actual application of the products, thus disappointing customers and harming the brand. Finally, the Color IQ scale also give me pause, as the rating of skin colors along a scale could lead to horrible PR and social backlash depending on how it is carried out.

On November 14, 2018, Michael Girouard commented on The AI Doctor will see you now – Machine learning transforming healthcare :

Really nice article. Since my first year of medical school, I’ve always felt that technology should play a role in addressing or overcoming human shortcomings in medicine (e.g. electronic stethoscopes that are able to analyze heart sounds more accurately than the human ear). The AI capabilities that you describe coming down the pipeline are directly in line with current trends in medical education. Medical students are now relying less and less on accumulating a body of knowledge (though a hefty background is necessary) and relying more on “learning how to learn” given the explosion of data entering the medical field. I do worry about the “laughing stock” mistakes a la Watson that you reference, as these could potentially be life threatening in the medical context. However, this technology, if used judiciously, could help with physician shortages in the rural US and globally by providing physicians with initial, first-pass diagnoses.