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Data-driven approach to quantify trust in medical devices using Bayesian networks.

Mini Thomas1, Omar Boursalie2, Reza Samavi2,3

  • 1Department of Computing and Software, McMaster University, Hamilton, ON L8S 4L8, Canada.

Experimental Biology and Medicine (Maywood, N.J.)
|January 28, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a data-driven method to assess trust in wearable medical devices using Bayesian networks. The approach quantifies trust by extracting probabilities from device data, enabling reliable trustworthiness evaluation.

Keywords:
Bayesian parameter estimationnon-functional requirementsrequirements engineeringtrust quantificationtrustworthy AIwearable devices

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Area of Science:

  • Artificial Intelligence
  • Medical Informatics
  • Reliability Engineering

Background:

  • Bayesian networks are valuable for quantifying uncertainty in subjective concepts like trust.
  • Assessing trust in wearable medical devices is crucial for user adoption and data integrity.
  • Existing methods may lack data-driven approaches for direct parameter estimation from device metrics.

Purpose of the Study:

  • To propose a novel data-driven approach for estimating Bayesian network parameters for trust quantification in wearable medical devices.
  • To develop a method that extracts trust factor probabilities directly from device data (e.g., sensor quality).
  • To establish a relative trust score for comparing different devices under identical conditions.

Main Methods:

  • Developed a data-driven approach to estimate Bayesian parameters using probabilities extracted from wearable device data.
  • Integrated expert knowledge to define the strength of relationships between trust factors.
  • Applied propagation rules from requirements engineering to calculate trust scores based on individual factor contributions.

Main Results:

  • Successfully developed and evaluated Bayesian networks for trust quantification of similar wearable devices from two manufacturers.
  • Demonstrated the learnability of the proposed approach under identical test conditions and noise levels.
  • Showcased the generalizability of the method for assessing trustworthiness across different devices.

Conclusions:

  • The proposed data-driven Bayesian network approach effectively quantifies trust in wearable medical devices.
  • The method allows for direct estimation of trust factors from device performance metrics.
  • The approach provides a reliable and generalizable framework for evaluating device trustworthiness.