Signify Research’s latest imaging AI VC funding report revealed an unexpected surge in 2021, along with major funding shifts that might explain why many of us didn’t see it coming. Here’s some of Signify’s big takeaways and here’s where to get the full report.
AI’s Path to $3.47B – Imaging AI startups have raised $3.47B in venture funding since 2015, helped by a record-high $815M in 2021 after several years of falling investments (vs. 2020’s $592M, 2019’s $450M, 2018’s $790M).
Big Get Bigger – That $3.47B funding total came from over 200 companies and 290 deals, although the 25 highest-funded companies were responsible for 80% of all capital raised. VCs increased their focus on established AI companies in 2021, resulting in record-high late-stage funding (~$723.5M), record-low Pre-Seed/Seed funding (~$7M), and a major increase in average deal size (~$33M vs. ~$12M in 2020).
Made in China – If you’re surprised that 2021 was a record AI funding year, that’s probably because it targeted Chinese companies (~$260M vs. US’ ~$150M), continuing a recent trend (China’s AI VC share was 45% in 2020, 26% in 2019). We’re also seeing major funding go to South Korea and Australia’s top startups, adding to APAC AI vendors’ funding leadership.
Health VC Context – Although imaging AI’s $815M 2021 funding total seems big for a category that’s figuring out its path towards full adoption, the amount VC firms are investing in other areas of healthcare makes it seem pretty reasonable. Our two previous Digital Health Wire issues featured seven digital health startup funding rounds with a total value of $267M (and that’s from just one week).
Signify correctly points out that imaging AI funding remains strong despite a list of headwinds (COVID, regulatory hurdles, lacking reimbursements), while showing more signs of AI market maturation (larger funding rounds to fewer players) and suggesting that consolidation is on the way. Those factors will likely continue in 2022. However, more innovation is surely on the way too and quite a few regional AI powerhouses still haven’t expanded globally, suggesting that the next steps in AI’s evolution won’t be as straightforward as some might think.