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Clinical risk prediction models: the canary in the coalmine for artificial intelligence in healthcare?
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  • Published on:
    Excellent articulation of a real problem, but unsure if the proposed solution would work.
    • Marcus J Baw, Clinical Informatician, Developer, GP and Emergency Physician. Freelance/RCPCH/Baw Medical Ltd

    The authors are correct that there is a definite problem of lack of availability of clinical risk prediction models (CRPMs) and other clinical digital tools at the 'coalface'. However there may be many other potential solutions to solving this, using more orthodox methods than a novel blockchain-based deployment marketplace.

    It is clear that academic, clinical, managerial , and industry incentives are misaligned and this is why CRPMs don't readily see deployment to places where clinical end-users can easily obtain and use them. But a blockchain-based solution is hard to envisage, when more ordinary deployment methods have not seemingly been tried with sufficient enthusiasm. The article suggests that blockchain 'might' be part of the solution but it would be a more convincing argument were this backed up by an open source proof of concept or a demonstration of such as system in action. It seems unlikely to me that EHR vendors will willingly integrate external features into their systems that are totally reliant on an unproven, fluid 'marketplace' of smart contract execution, with no guarantee of uptime, future cost, or long term reliability or even existence.

    Additionally, widening the discussion of these deployment incentives to include AI-based clinical risk models blurs the picture because these two types of CRPM are very different. The level of clinical trust of such experimental AI models is low, and does not favourably...

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    Conflict of Interest:
    None declared.