Solving the Global Diagnostics Scaling Challenge

On average, everyone has a 50% lifetime-risk of cancer. The demand for high-quality cancer diagnostics is already exceeding the global capacity of experts. Cases are expected to nearly double by 2030, creating a scaling challenge. CENTAURON.NET is the first incentive-driven, decentralized organization that is collectively building a shared data layer as a global brain for cancer imaging diagnostics. This global network of experts and machines will create diagnostic algorithms as human-machine hybrids. CENTAURON.NET is making artificial intelligence-based cancer diagnostics globally available with unprecedented efficacy, accuracy and robustness.

The Unmet Need

Pathology is the microscopic analysis of patient tissue samples, currently still done by a complex visual decision process using manual interpretation. Large parts of the world have no or limited access to this diagnostic knowledge. Pharma industry has huge difficulties in scaling its new biomarker-driven therapeutics. By 2030 the need for cancer diagnostics will double. This will result in massive growth to the current US$27 billion cancer pathology diagnostics market. While medical experts remain in the driver seat, artificial intelligence (AI) will automate all repetitive, simple tasks in these markets.

The Data Challenge

AI can only assist in diagnostics if algorithms can be trained on huge data repositories of labeled and sorted data. Such repositories currently do not exist. This forms a barrier to standardization and digitization of cancer medicine. Instead, all knowledge is currently analog. It is distributed in expert’s brains around the globe. The idea, attempted by some, to centralize this knowledge will fail due to medical complexity and also because it goes against the community idea of scientific advances. Only decentralization can provide robust and valid algorithms and set new medical standards in the coming era of digital medicine.

The Solution

CENTAURON.NET can scale diagnostics globally without limits to the benefit of patients and to all who contribute resources. It is a hybrid human-machine network of pathologists, AI specialists, research institutions and pharma, working on a common data-layer. To accommodate for data privacy and governance rules and allow the creation of private, partially-shared and global algorithms. The network will comprise public and private nodes. We invented this new form of decentralized organization as we think that it is best suited for harvesting the opportunities but avoiding the threats of digital medicine.


cAPPS (Cancer Classifying Apps) & Services

Network Users

Institutions and Labs Using CENTAURON.NET

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