RadNet, a US-based radiology company listed on NASDAQ (RDNT), has acquired medtech startup See-Mode Technologies.

The announcement was made on June 5, 2025.

See-Mode, based in Singapore, was founded in 2018 by Sadaf Monajemi and Milad Mohammadzadeh. The company develops AI-driven solutions to improve diagnostic accuracy and efficiency in radiology.

Initially focused on stroke prevention, See-Mode now serves radiology groups in Australia, Canada, and the US.

Cocoon Capital, an early investor, supported See-Mode through its growth.

🔗 Source: Cocoon Capital

🧠 Food for thought

1️⃣ Singapore’s growing role in the global AI healthcare acquisition landscape

See-Mode’s acquisition highlights Southeast Asia’s emergence as a source of AI healthcare innovation amidst significant M&A activity in the sector.

The AI healthcare market, valued at $600 million in 2014, is now projected to reach $150 billion by 2026, representing a compound annual growth rate of approximately 40% 1.

This acquisition follows a broader industry trend where established healthcare providers are increasingly acquiring AI startups to enhance their diagnostic capabilities and workflow efficiency, rather than developing these technologies in-house 2.

Since 2010, over 635 AI startups have been acquired across industries, with healthcare becoming a competitive sector for acquisition targets as traditional medical companies seek to modernize their technology infrastructure 3.

See-Mode’s journey from a pre-revenue Singapore startup to acquisition by a NASDAQ-listed company demonstrates how the geography of healthcare innovation is expanding beyond traditional hubs in the United States and Europe.

2️⃣ Radiology emerges as the primary battleground for AI healthcare applications

See-Mode’s focus on AI-enhanced ultrasound imaging aligns with radiology’s position as the dominant field for AI medical applications, where the technology is transforming diagnostic accuracy and workflow efficiency.

Over 76% of AI-enabled medical devices authorized in the U.S. are in radiology, making it the most mature segment of AI healthcare applications 4.