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    Memristive devices with electrical hysteresis show promise for non-Von Neumann computing and cancer diagnostics. This perspective examines their potential for integrated sensing and computing, particularly for multi-cancer marker detection.

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    Area of Science:

    • Materials Science
    • Computer Engineering
    • Biomedical Engineering

    Background:

    • Two-terminal switching devices exhibiting electrical hysteresis, often termed memristive devices, have been extensively researched over the past two decades.
    • These devices' unique characteristics enable applications in data storage, in-memory computing, and integrated sensing and computing functionalities.

    Purpose of the Study:

    • To critically review existing research on memristive devices for non-Von Neumann computing architectures.
    • To explore the integration of sensing and computing within memory units for novel computing paradigms.
    • To demonstrate the practical application of these devices in cancer medicine, specifically for enhanced diagnostic efficiency through multi-cancer marker analysis.

    Main Methods:

    • Comprehensive literature review of memristive device research.
    • Critical discussion on the potential of memristive devices for non-Von Neumann computing.
    • Exploration of the application of memristive devices in multi-cancer marker detection for diagnostics.

    Main Results:

    • Memristive devices offer a pathway towards developing advanced computing architectures that merge sensing and computation.
    • The inherent properties of these devices are suitable for creating integrated memory and processing units.
    • Simultaneous detection of multiple cancer markers using memristive devices can significantly improve diagnostic accuracy and efficiency.

    Conclusions:

    • Memristive devices are pivotal for the advancement of non-Von Neumann computing, enabling seamless integration of sensing and computation.
    • The application of memristive devices in cancer diagnostics, particularly for multi-marker analysis, represents a significant leap forward in medical technology.
    • Further research in this interdisciplinary field holds substantial promise for both computing innovation and improved healthcare outcomes.