Imagene AI, a pioneer in multi-modal foundation models for precision medicine, announced the successful completion of a $23 million Series B financing round led by Oracle Chairman and CTO Larry Ellison. This latest funding brings the company's total capital raised to $45 million, with existing investor Aguras Pathology Investments also participating in the round.
Founded in 2020 by Dean Bitan, Jonathan Zalach, and Shahar Porat, the company employs 30 people, with most based in Israel and the remainder in the United States. This marks Ellison's second investment in the company, reflecting his continued commitment to the platform's vision.
Multi-Modal Foundation Model Architecture
At the core of Imagene AI's platform is a dynamic intelligence infrastructure that continuously evolves with biological and clinical data streams. The system processes histopathology images, molecular profiles, and clinical context in real-time through proprietary foundation models, large language models, and biology-tuned analysis engines.
"We're developing the core intelligence that allows precision medicine to function as an adaptive, insight-driven system - from real-time trial design to rapid patient identification and more informed clinical decisions," said Dean Bitan, CEO and Co-founder of Imagene AI. "Our goal is to make every trial more responsive, every insight more actionable, and every patient journey more personalized."
The company's digital pathology foundation model has been trained on more than 1.5 million biopsy images and has demonstrated benchmark performance in key research tasks, particularly when working with limited data—a common challenge in real-world clinical scenarios.
Clinical Applications and Platform Capabilities
Imagene AI has launched the OI Suite platform to a broader community of users, including non-AI experts, delivering capabilities for biomarker discovery, indication expansion, and patient identification across multiple therapeutic areas. One of the company's first commercial products is a system that analyzes biopsy images to identify cancer mutations, ensuring patients receive the most effective treatment.
"Our system is like what OpenAI did for language models, but in our world of medicine and biology, where information is far less accessible," Bitan explained. "We've collected extensive clinical and medical data and fed it into our 'brain,' which can be asked all kinds of questions."
The platform can predict disease recurrence patterns and serves as a living system that constantly learns from new information. The company's foundation model ingests diverse data types to provide infrastructure for pharmaceutical companies to conduct more successful experiments and clinical trials.
Strategic Partnerships and Market Expansion
Imagene AI is expanding its strategic collaborations, including a partnership with Tempus, the largest testing provider in the United States. This collaboration will distribute the company's predictive assays and work with hospitals in Israel to bring rapid treatment guidance to more clinicians and patients.
"We plan to expand our offering to more locations and to grow our activities in the pharmaceutical world, because we want to see more patients receiving personalized treatments, and we aim to help companies develop better drugs," Bitan stated.
The company's "brain" is already embedded within several pharmaceutical companies, helping optimize clinical trials and advance personalized medicine applications.
Technology Vision and Future Impact
"Imagene AI's ability to unite imaging, omics, and clinical data in a single foundation model is exactly the kind of breakthrough we believe will drive the next generation of drugs and diagnostics," said Larry Ellison. "Their approach opens the door to a more adaptive, biologically fluent era of precision medicine."
The continuous loop architecture ensures insights remain current and biologically grounded, empowering clinicians and researchers with personalized, up-to-date guidance at every stage of clinical trials. This approach aims to make every clinical trial faster and every treatment more successful through enhanced predictive capabilities and real-time data integration.
