Ethereum can be used for cancer research. Here’s how it works
Important takeaways
- A medical team pointed out in a paper this year that blockchains were useful for cancer researchers to share information with each other for their AI systems.
- According to the team, blockchains allow AI model parameters to be shared simultaneously across all collaborators without the help of a centralized coordinator.
- The team specifically mentioned using smart contracts on Ethereum for that purpose.
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Ethereum’s smart contracts allowed three different teams of researchers to update their AI models simultaneously without going through a centralized authority. The AI models themselves are used to predict the emergence of cancer cells in the body.
Decentralized data exchange
The Ethereum blockchain is used in the global fight against cancer.
A research paper published in Nature Medicine in April, called Swarm learning for decentralized artificial intelligence in cancer histopathology and written by 27 different contributors, indicates in one of the footnotes that the team started using the Ethereum network for their experiments with cancer.
According to the paper, artificial intelligence (AI) can help predict the emergence of cancer cells in patients by extracting information about the shape and size of cells that are not visible to the human eye. However, the large datasets needed to run such AI systems face “practical, ethical and legal obstacles” from a data collection point of view, especially if the data is shared across countries.
One of the ways to solve this problem is to use federated learning (FL), which does not require researchers to share their data, only their locally trained AI model weights (or parameters). The problem is that such systems depend on a centralized coordinator who essentially combines all the model weights together – and who then has full control over the research project and its commercial exploitation.
Instead, the team pointed to the growing use of swarm learning (SL), a system that leverages blockchain technology to avoid handing over power to a centralized entity. In other words, SL enables teams to share their AI model weights while keeping all contributors at the same level, making collaboration between a larger number of parties easier, and in turn feeding AI models with more data, making them stronger.
Specifically, the research team states that it used smart contracts on Ethereum for three separate computers to synchronize their AI model weights at set times. In fact, all three partners had updated AI models simultaneously without needing the help of a coordinator who would manually merge model parameters. “In this setup,” the paper says, “the blockchain maintains the global state information about the model.” The research paper found that AI systems born from the setup outperformed locally trained AI models and performed on par with other models trained with pooled datasets (and that the technique was more data efficient). As a medical professional AriGoldNFT explained when they pointed out the article on Twitter, “a hospital in New York can communicate with one in Los Angeles through nodes.”
This is important news for crypto in general and smart contract platforms in particular. So far, blockchains have proven extremely useful in finance, but critics and enthusiasts alike have decried the technology’s lack of use in other sectors. Ethereum creator Vitalik Buterin tired in August, the crypto had to “transform into something useful” within the next ten years. It would be difficult to find a more worthy use case than for the medical field.
Disclaimer: At the time of writing, the author of this piece owned BTC, ETH and several other crypto assets.
