We’ve just wrapped up our 2019 SIIM Annual Meeting in Denver during which AI was at the center of the discussions.
I would like to reflect on two panel discussions I have interacted with on Wednesday 26th and Thursday 27th June in the Exhibition Hall Theater. The first one was about the economics of AI and the second about its current state in practice. I also had many interesting exchanges with key stakeholders of the AI and Radiology ecosystem ranging from Academia to Corporates.
The panel included a diversified group of academic faculties, entrepreneurs and industry stakeholders including startups (Infervision, Ai.doc, Qure.ai …) and established companies (Nuance, Blackford Analysis, Intelerad, Theracon, GE, Philips …). The audience was composed of physicians (mainly radiologists) as well as HealthIT and Imaging Informatics Professionals.
During the panel discussion, I jumped on the microphone and told them about my experience as an investor. During the last years, I had multiple opportunities to invest in startups that were developing deep learning-based software for medical imaging, but so far, I had not made any investment…
Despite reliable teams, promising technologies, a large market and solid domain-expert advisory boards, I was not confident enough to board on these ventures. The missing piece was the lack of a sound business case and defensibility in the long run.
The majority if not most of the startups operating in this field are focused on solving very narrow clinical problems based on limited and biased training datasets and are heavily focused on image pixels rather than healthcare’s big picture; this will refrain them from developing scalable & clinically useful products and building profitable and successful companies.
Companies that are building algorithms to detect one or few radiological abnormalities on one medical imaging modality are building features rather than products. The delineation between a feature and a product can be hard to define in the digital world.