Medical informed machine learning: A scoping review and future research directions

Florian Leiser; Sascha Rank; Manuel Schmidt-Kraepelin; Scott Thiebes; Ali Sunyaev

doi:10.1016/j.artmed.2023.102676

Medical informed machine learning: A scoping review and future research directions

Artif Intell Med. 2023 Nov:145:102676. doi: 10.1016/j.artmed.2023.102676. Epub 2023 Oct 19.

Authors

Florian Leiser¹, Sascha Rank¹, Manuel Schmidt-Kraepelin¹, Scott Thiebes¹, Ali Sunyaev²

Affiliations

¹ Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany.
² Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany. Electronic address: sunyaev@kit.edu.

PMID: 37925206
DOI: 10.1016/j.artmed.2023.102676

Abstract

Combining domain knowledge (DK) and machine learning is a recent research stream to overcome multiple issues like limited explainability, lack of data, and insufficient robustness. Most approaches applying informed machine learning (IML), however, are customized to solve one specific problem. This study analyzes the status of IML in medicine by conducting a scoping literature review based on an existing taxonomy. We identified 177 papers and analyzed them regarding the used DK, the implemented machine learning model, and the motives for performing IML. We find an immense role of expert knowledge and image data in medical IML. We then provide an overview and analysis of recent approaches and supply five directions for future research. This review can help develop future medical IML approaches by easily referencing existing solutions and shaping future research directions.

Keywords: Domain knowledge; Informed machine learning; Machine learning; Medical informatics; Scoping literature review.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

Education, Medical*
Machine Learning*