Uses computational models and generative AI to create customizable virtual patient populations and conduct in silico trials, enabling the assessment of medical device safety and efficacy without human testing. This approach reduces design failures, accelerates R&D timelines, and optimizes clinical trials by simulating diverse anatomical, physiological, and pathological conditions at scale.
Funding
$4.4M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Traditional medical device testing relies on human trials, which are costly, time-consuming, and may expose patients to unnecessary risks. Identifying design flaws early in the development process is challenging, leading to increased R&D expenses and potential clinical failures. Furthermore, access to diverse patient populations for testing can be limited, hindering the comprehensive evaluation of device safety and efficacy.
Solution
Adsilico offers a computational modeling and generative AI platform that creates customizable virtual patient populations for in silico trials, enabling medical device manufacturers to assess device safety and efficacy without human testing. The platform simulates diverse anatomical, physiological, and pathological conditions at scale, allowing for early identification of design flaws and optimization of clinical trial protocols. By using virtual patients, Adsilico reduces the need for extensive human trials, accelerates R&D timelines, and minimizes the risk of patient harm. The technology facilitates the exploration of device performance across a wide range of patient characteristics, including under-represented and ethically challenging populations.
Target Audience
The primary target audience includes medical device manufacturers, research institutions, and regulatory agencies involved in the development, testing, and approval of medical devices.
Features
- Generative AI technology for creating high-fidelity, simulation-ready virtual patient models
- Customizable virtual models of human anatomy, physiology, and pathology
- Scalable generation of virtual patient populations, from dozens to thousands
- Simulation of diverse pathological conditions and anatomical structures
- Identification of poor designs early in the development process
- Optimization of clinical trials through exposure to diverse patient populations