With respect to "Zebra Medical Vision" please answer the below questions:

1. What is the value proposition at Zebra Medical ? What are the biggest risks to realizing that proposition in the near future?
2. With seventy other firms in the ML space for imaging, what are Zebra's competitive advantages and disadvantages?
3. Where should Zebra orient its development over the next three to five years? (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?)

@ HARVARD BUSINESS SCHOOL 9-519-014 REV: DECEMBER 5. 21119 SHANE GREENSTEIN SARAH GULICK Zebra Medical Vision In July 2018, Eyal Gura, Elad Benjamin, and Eyal Toledarlo, co-founders of Zebra Medical Vision (Zebra), were celebrating the news that their first artificial intelligence (Al) algorithm, used to determine risk factors of coronary heart disease, had just been granted FDA 510(k) clearance This meant that they would be able to market their technology in the United States, the world's largest healthcare economy. They were meeting in their ofce in Shefayim, Israel, upstairs from a cafe in what was once a farming lvzilzilzlutz,b down the road from Herzliya, Israel's start-up capital. Founded in 2014, Zebra had completed three rounds of financing, and had enough funding left to continue for three years. Zebra had developed algorithms that produced diagnoses from X-rays, mammograms, and (IT-scans. The algorithms used deep learning and digitized radiology scans to create software that could assist doctors in making diagnoses. (See Exhibit 1 for a sample Zebra scan.) 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, a price point they hoped would encourage care providers to use their technology frequently.\" Gura explained that "after four years of walking alone in the desert," 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 US. and Europe who gave feedback on its development. Gura, Toledano, and Benjamin had to decide: 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