There’s plenty of bold forecasts about imaging AI’s long term potential, but short term projections of when AI startups will reach profitability are rarely disclosed and almost never bold. That’s why RadNet’s quarterly investor calls are proving to be such a valuable bellwether for the business of AI, and its latest briefing was no exception.
RadNet entered the AI arena with its 2020 acquisition of DeepHealth (~$20M) and solidified its AI presence in early 2022 by acquiring Aidence and Quantib (~$85M), but its AI business generated just $4.4M in revenue and booked a $24.9M in pre-tax loss in 2022.
Those numbers are likely typical for similar-sized AI companies. However, RadNet’s path towards AI revenue growth and breakeven operations might outpace most of its peers.
- Looking into 2023, RadNet forecasts that its AI revenue will quadruple to between $16M and $18M, while its Adjusted EBITDA loss falls to between -$9M and -$11M.
- By 2024, RadNet expects its AI division to generate at least $25M to $30M in revenue, allowing it to achieve AI profitability for the first time.
So how exactly is RadNet going to achieve 6x AI revenue growth and reach profitability within just two years? Patients are going to pay for it.
RadNet expects its new direct-to-patient Enhanced Breast Cancer Detection (EBCD) service to generate between $11M and $13M in 2023 revenue, representing up to 72% of RadNet’s overall AI revenue and driving much of its AI profitability improvements. And EBCD’s nationwide rollout won’t be complete until Q3.
RadNet’s 2024 AI revenue and profit improvements will again rely on “substantial” EBCD growth, with some help from its Aidence and Quantib operations. Those improvements would offset delayed AI efficiency benefits that RadNet has “yet to really realize” due in part to slow radiologist adoption.
The fact that RadNet expects to become one of imaging’s largest and most profitable AI companies within the next two years might not be surprising. However, RadNet’s reliance on patient payments to drive that growth is astounding, and it’s something to keep an eye on as AI vendors and radiology groups work on their own AI monetization strategies.