Apheris AI GmbH, a startup with a platform for analyzing healthcare data, today disclosed that it has raised $8.25 million in funding.

OTB Ventures and eCAPITAL led the Series A round. The investment brings Berlin-based Apheris’ total outside funding to $20.8 million.

In the healthcare sector, researchers from different organizations often require the ability to share clinical data with one another. But sending data over the network to a different organization can present cybersecurity risks. Healthcare institutions address those risks using an approach called federated computing, which forms the basis of Apheris’ platform.

With federated computing, a company doesn’t have to transfer its clinical datasets outside the corporate network to make them accessible for third-party researchers. The technology lets researchers remotely run analyses on the part of the company’s internal infrastructure that hosts the data. The processing results are subsequently sent back to the researchers over the network without moving any of the information that was analyzed.

Apheris’ platform makes it easier for healthcare institutions to implement federal computing. It also enables researchers to run artificial intelligence models on the clinical datasets shared with them.

Federated computing projects ca be facilitated with a lightweight program, or agent, called the Compute Gateway. It runs on the system that hosts the clinical dataset a company plans to make available for external researchers. Once installed, the Compute Gateway allows researchers to request access to the dataset on a self-service basis. 

Apheris users structure their information access requests as a so-called Compute Spec. This is a file that specifies what dataset researchers wish to access, the AI model that they plan to run on the dataset and the hardware resources required for the task. Employees at the organization that owns the dataset can use a centralized dashboard to review and approve such requests.

Under the hood, the platform is based on an open-source Nvidia Corp. framework called FLARE. When a researcher runs an AI model on a system that hosts a dataset, FLARE ensures the system’s operator can’t reverse-engineer the model to obtain its training dataset. The framework likewise blocks attempts to access the neural network’s weights.  

According to Apheris, its platform protects not only AI models but also the clinical information they process.

Data usually has to be decrypted before it can be analyzed. While in plaintext form, information is considerably easier for hackers to steal, which increases the risk of breaches. Apheris implements a technique called homomorphic encryption that makes it possible to run queries on a dataset without decrypting it. 

The feature is complemented by a second security feature called differential privacy. When the clinical dataset used in a research project contains personal information such as patient records, scientists often anonymize it before sharing it with third parties. Differential privacy makes it more impossible for hackers to extract personal information from anonymized datasets. 

Apheris says its software has been adopted by Johnson & Johnson, Roche and other major pharmaceutical companies. The software maker’s installed base also includes several hospitals. 

It will use its newly raised capital to hire more life sciences and go-to-market professionals. Additionally, the company intends to enhance its platform’s AI features. The plan is to host foundation AI models that can speed up some of the tasks involved in studying proteins. 

Photo: Pixabay