Whether you’re interested in using Artificial Intelligence (AI) and Machine Learning (ML) to drive better health outcomes, reduce your operational costs, or improve fraud detection, one way you can better unlock these capabilities is through leveraging blockchain.
In my last blog, “Improving Patient Care through AI and Blockchain – Part 1,” I discussed several opportunities for blockchain to help advance AI in healthcare, from sourcing more training data from across a consortium, to tracking provenance of data, improving the quality of AI with auditing, and protecting the integrity of AI using blockchain. In this second blog, take a look at four more reasons to consider blockchain for advancing AI in healthcare.
- Shared models
In cases where constraints exist that preclude the sharing of raw training data from across a consortium of healthcare organizations, for legal or other reasons, it may be possible to incrementally train shared models, enabled by the blockchain. In this approach the AI / ML models themselves can be shared across the network of healthcare organizations in the consortium, rather than the raw training data, and these shared models can be incrementally trained by each organization using its training data, and within its firewall. Blockchain can then be used to share the models as well as metadata about training data, results, validations, audit trails, and so forth.
- Incentivizing collaboration using cryptocurrencies and tokens
Cryptocurrencies and tokens on blockchain can be used to incent and catalyze collaboration to advance AI / ML in healthcare. From sharing of training data, to collaboration on shared models, results, validations, and so forth, healthcare organizations can be rewarded with cryptocurrencies or tokens proportional to their participation and contribution. Depending on how the blockchain is setup these cryptocurrencies or tokens could be redeemed by participating healthcare organizations for meaningful rewards, or monetized. This can be useful in any AI / ML blockchain initiative both as an accelerant, and could also be critical to overcome potential impediments and reservations to collaboration that can arise where the size / value of contributions from organizations across the consortium are asymmetrical.
- Validating inference results and building trust fasterBefore AI / ML models can be used for patient care they must be validated to ensure safety and efficacy. A single organization validating a model alone will take more time to achieve an acceptable level of trust than would be the case for a consortium of healthcare organizations concurrently collaborating to validate a shared model. Blockchain can be used to coordinate and collaborate around such validation to increase synergy, minimize redundant efforts, accelerate validation, and establish trust in a new model faster.
- Automation through smart contracts and DAOsExecutable code for processing transactions associated with AI / ML, whether procurement of training data or otherwise, can be implemented on blockchains in the form of smart contracts. DAOs (Decentralized Autonomous Organizations) such as non-profits can also be built using smart contracts to automate whole enterprises that can facilitate advancing AI / ML in healthcare at scale.
Keep the conversation going
If you’re interested in using AI, ML, or blockchain for healthcare, you know that new opportunities are constantly surfacing and with it come a whole host of new questions. Follow me on LinkedIn and Twitter to get updates on these topics as well as cloud computing, security, privacy, and compliance. If you would like to explore a partnership as you work to implement AI and/or blockchain for your healthcare organization, we’d love to hear from you.
For more resources and tips on blockchain for healthcare, take a look at part 1 of this series here.