Paris-based SquareMind has just pocketed a cool $18 million in a pre-Series A funding round to roll out its AI-powered robotic system, dubbed Swan. The goal? To automate the tedious, and frankly error-prone, process of full-body skin exams for early melanoma detection. The round was notably led by Sonder Capital, a venture fund co-founded by medical robotics heavyweight Fred Moll of Intuitive Surgical fame.
The Swan system is, in essence, a very polite and incredibly meticulous robot arm that captures high-resolution, dermoscopic images of a patient’s entire skin surface. The contactless process, guided by audio and visual prompts, takes just minutes and creates a comprehensive digital baseline of every last mole and freckle. This data is then fed into an AI platform that helps dermatologists track any new or changing lesions over subsequent visits—a critical factor, considering around 80% of melanomas are new lesions.
The company claims Swan is the world’s first robot to achieve this level of standardized, full-body imaging at a dermoscopic level, effectively acting as an “augmented dermatoscope” for the whole body. The new capital will fuel SquareMind’s commercial launch in the U.S. and Europe, where long wait times for dermatologists are a growing problem.
Why is this important?
Let’s be honest, the current standard of care—a doctor eyeballing your skin and maybe taking a few Polaroids—feels decidedly analog in an increasingly digital world. SquareMind is tackling a problem of both scale and precision. Dermatologists are overworked, and human memory is fallible. By creating a time-series, high-resolution digital twin of a patient’s skin, the Swan robot provides objective, trackable data.
This isn’t about replacing doctors; it’s about arming them with superior tools. Studies have shown that while AI alone performs comparably to dermatologists in detecting melanoma, AI-assisted dermatologists achieve significantly higher sensitivity and specificity. By automating the laborious documentation process, SquareMind’s system allows clinicians to focus on what they do best: making critical diagnostic decisions. It’s a textbook case of robotics and AI enhancing, not supplanting, human expertise to potentially save lives through earlier, more accurate detection.