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• Case | HBS Case Collection | September 2018 (Revised December 2019)

  Zebra Medical Vision

  Shane Greenstein and Sarah Gulick

  An Israeli startup founded in 2014, Zebra Medical Vision developed algorithms that produced diagnoses from X-rays, mammograms, and CT-scans. The algorithms used deep learning and digitized radiology scans to create software that could assist doctors in making diagnoses. By July 2018, Zebra had developed seven algorithms to analyze scans for emphysema, liver density, compression fractures, bone density, brain bleeds, breast cancer, and a calcium score used to detect calcified plaque in coronary arteries. For each scan analyzed, Zebra charged hospitals $1. By 2018 Zebra found itself in a race with its competitors to perfect these algorithms, create software tools, distribute the tools to physician partners, and create a market. Zebra already had several partners in the U.S. and Europe who gave feedback on its development. Management had to answer the following questions: What should they do next at Zebra? Should they work on the accuracy of the already developed algorithms or continue to develop many new tools? If they chose to develop new products, which applications should they address? And how should they go to market?

  Keywords: radiology; Machine learning; X-ray; CT scan; medical technology; probability; FDA 510(k); diagnosis; Business Startups; Health Care and Treatment; Technology; Software; Competitive Strategy; Product Development; Commercialization; Decision Choices and Conditions; Health Industry; Medical Devices and Supplies Industry; Technology Industry; Israel;

  Citation:

  Greenstein, Shane, and Sarah Gulick. "Zebra Medical Vision." Harvard Business School Case 619-014, September 2018. (Revised December 2019.)  View Details

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• Working Paper | 2019

  The Impact of the General Data Protection Regulation on Internet Interconnection

  Ran Zhuo, Bradley Huffaker, KC Claffy and Shane Greenstein

  The Internet comprises thousands of independently operated networks, where bilaterally negotiated interconnection agreements determine the flow of data between networks. The European Union’s General Data Protection Regulation (GDPR) imposes strict restrictions on processing and sharing of personal data of EU residents. Both contemporary news reports and simple bilateral bargaining theory predict reduction in data usage at the application layer would negatively impact incentives for negotiating interconnection agreements at the internet layer due to reduced bargaining power of European networks and increased bargaining frictions. Considerable empirical evidence at the application layer confirms this prediction. Using a large sample of interconnection agreements between networks around the world in 2015–2019, we empirically investigate the impact of the GDPR on interconnection behavior of network operators in the European Economic Area (EEA) compared to network operators in non-EEA OECD countries. All evidence estimates precisely zero effects across multiple measures: the number of observed agreements per network, the inferred agreement types, and the number of observed IP-address-level interconnection points per agreement. We also find economically small effects of the GDPR on the entry and the observed number of customers of networks. We conclude that the short-run costs for GDPR are concentrated at the application layer.

  Citation:

  Zhuo, Ran, Bradley Huffaker, KC Claffy, and Shane Greenstein. "The Impact of the General Data Protection Regulation on Internet Interconnection." NBER Working Paper Series, No. 26481, November 2019.  View Details

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• Case | HBS Case Collection | November 2019

  DeepMap: Charting the Road Ahead For Autonomous Vehicles

  Shane Greenstein and Nicole Tempest Keller

  Founded in 2016, DeepMap developed high definition (HD) mapping software and localization services for Level 4+ autonomous vehicles. Traditional navigational maps were accurate to a few meters, which was sufficient for drivers, but not for machine-driven vehicles which required centimeter level accuracy. Autonomous vehicles required a new form of map that was highly precise, produced a 3D representation of the surrounding area, enabled vehicles to locate themselves within the map, provided information for how to navigate safely using the correct rules of the road, and was updated continuously as road conditions changed. DeepMap was not selling a static mapping database, but rather licensing its software under a software-as-a-service model. As a startup with limited resources navigating a nascent market, DeepMap faced uncertainty across several dimensions: the timing of overall AV market adoption, which countries would adopt fastest, which AV segments would move most rapidly, which sensor technologies would become standard, and what impact regulation would have. They also faced the challenge of serving customers on a global scale. As DeepMap looked ahead, it had to decide how and where to focus and allocate its funding in order to achieve its short and long-term objectives. 

  Keywords: mapping software; autonomous vehicles; Business Startups; Software; Technological Innovation; Technology Adoption; Service Delivery; Global Range; Resource Allocation; Strategic Planning; Technology Industry; Auto Industry;

  Citation:

  Greenstein, Shane, and Nicole Tempest Keller. "DeepMap: Charting the Road Ahead For Autonomous Vehicles." Harvard Business School Case 620-047, November 2019.  View Details

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