Issues and Limitations on the Road to Fair and Inclusive AI Solutions for Biomedical Challenges.

Journal: Sensors (Basel, Switzerland)
Published Date: Jan 2, 2025

Abstract

OBJECTIVE: In this paper, we explore the correlation between performance reporting and the development of inclusive AI solutions for biomedical problems. Our study examines the critical aspects of bias and noise in the context of medical decision support, aiming to provide actionable solutions. Contributions: A key contribution of our work is the recognition that measurement processes introduce noise and bias arising from human data interpretation and selection. We introduce the concept of "noise-bias cascade" to explain their interconnected nature. While current AI models handle noise well, bias remains a significant obstacle in achieving practical performance in these models. Our analysis spans the entire AI development lifecycle, from data collection to model deployment.

Authors

  • Oliver Faust
    Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom. Electronic address: o.faust@shu.ac.uk.
  • Massimo Salvi
  • Prabal Datta Barua
    Cogninet Australia, Sydney, NSW 2010 Australia.
  • Subrata Chakraborty
    Center for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Australia.
  • Filippo Molinari
    Department of Electronics and Telecommunications, Politecnico di Torino, Italy.
  • U Rajendra Acharya
    School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Darling Heights, Australia.

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