A Systematic Map of Interpretability in Medicine

Hajar Hakkoum, Ibtissam Abnane, Ali Idri, Ali Idri

2022

Abstract

Machine learning (ML) has been rapidly growing, mainly owing to the availability of historical datasets and advanced computational power. This growth is still facing a set of challenges, such as the interpretability of ML models. In particular, in the medical field, interpretability is a real bottleneck to the use of ML by physicians. This review was carried out according to the well-known systematic map process to analyse the literature on interpretability techniques when applied in the medical field with regard to different aspects. A total of 179 articles (1994-2020) were selected from six digital libraries. The results showed that the number of studies dealing with interpretability increased over the years with a dominance of solution proposals and experiment-based empirical type. Additionally, artificial neural networks were the most widely used ML black-box techniques investigated for interpretability.

Paper Citation

in Harvard Style

Hakkoum H., Abnane I. and Idri A. (2022). A Systematic Map of Interpretability in Medicine. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF; ISBN 978-989-758-552-4, SciTePress, pages 719-726. DOI: 10.5220/0010968700003123

in Bibtex Style

@conference{healthinf22,
author={Hajar Hakkoum and Ibtissam Abnane and Ali Idri},
title={A Systematic Map of Interpretability in Medicine},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF},
year={2022},
pages={719-726},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010968700003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF
TI - A Systematic Map of Interpretability in Medicine
SN - 978-989-758-552-4
AU - Hakkoum H.
AU - Abnane I.
AU - Idri A.
PY - 2022
SP - 719
EP - 726
DO - 10.5220/0010968700003123
PB - SciTePress