What You Should Know:
– Aignostics, a leading AI company specializing in pathology data analysis, raised $34M in Series B funding, led by ATHOS with participation from Mayo Clinic and growth financing from HTGF, alongisde existing investors Wellington Partners, Boehringer Ingelheim Venture Fund, CARMA Fund, and VC Fonds Technologie managed by IBB Ventures.
– Aignostics combines proprietary access to multimodal clinical data with cutting-edge AI technology and rigorous scientific methods to develop best-in-class solutions for precision medicine. The investment will fuel the development of new products for biopharmaceutical clients, expand the company’s presence in the United States, and support the creation of advanced foundation models for pathology in collaboration with Mayo Clinic.
AI-Driven Solutions for Precision Medicine
As precision medicine becomes increasingly complex, biopharmaceutical companies are turning to AI to enhance their research and development efforts. Aignostics’ AI-powered platform analyzes complex multimodal pathology data, providing valuable insights for drug discovery, translational research, clinical trials, and companion diagnostics (CDx) development.
Expanding Capabilities and Partnerships
This new funding will enable Aignostics to:
- Develop Scalable Products: Launch “plug-and-play” AI solutions for various indications and tasks, including tumor microenvironment and biomarker profiling.
- Expand US Presence: Increase its footprint in the U.S. market and support its growing network of partners.
- Collaborate with Mayo Clinic: Develop advanced foundation models and biopharma product offerings in partnership with Mayo Clinic.
“2024 has been a pivotal year for us that has included a major strategic collaboration with Bayer and the launch of our first foundation model, RudolfV,” said Viktor Matyas, CEO and Co-Founder of Aignostics. “With RudolfV, we’ve gained the ability to quickly develop cost-efficient algorithms that generalize to the real-world. Now with this new round of funding, we’re turning our most popular algorithms into products that will help usher in an era of truly generalizable AI for computational pathology.”