Policy ForumBiomedical Technology Regulation

Algorithms on regulatory lockdown in medicine

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Science  06 Dec 2019:
Vol. 366, Issue 6470, pp. 1202-1204
DOI: 10.1126/science.aay9547

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  • New quantum risk management for unlocked Quantum Artificial Intelligence (QAI) based medical devices
    • Yuichi Hirata, Associate Professor, Hokkaido University, Central Institute of Isotope Science, Graduate School of Biomedical Science and Engineering

    For Artificial intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD), the U.S. Food and Drug Administration (FDA) defined the “locked” algorithm as an algorithm that provides the same result each time the same input is applied to it and does not change with use (1, 2).

    Although implementations of such “locked” algorithms may be possible for deterministic Classical Artificial Intelligence (CAI), it is not possible to implement completely “locked” algorithms in Quantum Artificial Intelligence (QAI) because of its stochastic natures due to quantum mechanics (3, 4).

    Since ordinary continuous risk-monitoring approach and tests explained in (1) are difficult for such unlocked QAI with quantum and stochastic natures, new quantum risk management for QAI based medical devices is necessary to ensure safety of the medical devices.

    The new quantum risk management shall monitor specific risks of the QAI such as risks caused by disruptions of quantum-superposition states used in calculations of quantum computers in the QAI to make unpredictable errors in its output data (4), and risks due to quantum mechanical uncertainties caused by fundamental limitations of quantum mechanical measurement (3).

    In other words, maintenance of such quantum-superposition states and consideration of such quantum mechanical uncertainties shall be part of essential requirements for the QAI based medical devices to satisfy.

    In addition, it is quite...

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    Competing Interests: None declared.

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