The Apheris Resource Library

Exploring the EU AI Act, a pivotal regulation addressing AI's multifaceted nature, this blog outlines risk levels, high-risk system requirements, and preparation for compliance. Apheris's solution for various governance, privacy, and security obligations is highlighted, emphasizing a shared commitment to responsible AI.

Reaching universal agreement on how to regulate AI is impossible. But a carefully designed cyclical process can lead to efficiencies in an enterprise's journey towards trustworthy, accountable, and sustainable AI. In this blog, I navigate the regulation space through the eyes of a data custodian.

There is so much useful data around us. Yet most data is not accessible for machine learning or other large scale analytics. In this article, we will explore why this is and what needs to be in place to enlighten this dark data.

Learn how to leverage federated and sensitive data at scale and how you can collaborate with your partners to advance and evolve the practice of life sciences.

In this case study you will learn:

How two pharma companies use protein-ligand interaction data and AI models to predict target binding of novel compounds
How a pharma company fueld their drug discovery activities by leveraging molecule data from their suppliers
How an AI-focussed health tech company developed AI models to improve decision-making processes for cancer treatments

We need to make biomedical data easily accessible and securely usable, to find new medicines and diagnostics, and to establish data-based insights that evolve the practice of medicine.

In this case study you will learn:


How a MedTech company built up the capability for reliable and strategic data access to thousands of annotated MRI scans from several hospitals
How a data science team in a pharma company collaborated with hospitals and institutions to securely leverage cancer patient data for AI
How a genomics company analyzed genomics data from laboratories at scale

Learn how multiple companies across a manufacturing value chain have securely collaborated on data while protecting the IP of any party, data, or model.

In this case study you will learn:

How a coating supplier increased output quality to consistently meet the OEM's targets
How an Industrial Solution Provider developed AI-features for its immersion heaters and implemented them at his customers' environment
How several automotive suppliers for high-performance plastics parts collaboratively developed AI models to predict material behavior