Deep Learning and Medical Image Analysis: Epistemology and Ethical Issues

Francesca Lizzi, Alessandra Retico, Maria Fantacci

2023

Abstract

Machine and deep learning methods applied to medicine seem to be a promising way to improve the performance in solving many issues from the diagnosis of a disease to the prediction of personalized therapies by analyzing many and diverse types of data. However, developing an algorithm with the aim of applying it in clinical practice is a complex task which should take into account the context in which the software is developed and should be used. In the first report of the World Health Organization (WHO) about the ethics and governance of Artificial Intelligence (AI) for health published in 2021, it has been stated that AI may improve healthcare and medicine all over the world only if ethics and human rights are a main part of its development. Involving ethics in technology development means to take into account several issues that should be discussed also inside the scientific community: the epistemological changes, population stratification issues, the opacity of deep learning algorithms, data complexity and accessibility, health processes and so on. In this work, some of the mentioned issues will be discussed in order to open a discussion on whether and how it is possible to address them.

Paper Citation

in Harvard Style

Lizzi F., Retico A. and Fantacci M. (2023). Deep Learning and Medical Image Analysis: Epistemology and Ethical Issues. In Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE, ISBN 978-989-758-642-2, SciTePress, pages 172-179. DOI: 10.5220/0011983000003497

in Bibtex Style

@conference{improve23,
author={Francesca Lizzi and Alessandra Retico and Maria Fantacci},
title={Deep Learning and Medical Image Analysis: Epistemology and Ethical Issues},
booktitle={Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,},
year={2023},
pages={172-179},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011983000003497},
isbn={978-989-758-642-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,
TI - Deep Learning and Medical Image Analysis: Epistemology and Ethical Issues
SN - 978-989-758-642-2
AU - Lizzi F.
AU - Retico A.
AU - Fantacci M.
PY - 2023
SP - 172
EP - 179
DO - 10.5220/0011983000003497
PB - SciTePress