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Review

A Review on Human–AI Interaction in Machine Learning and Insights for Medical Applications

School of Computing and Information Systems, The University of Melbourne, Melbourne 3010, Australia
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Author to whom correspondence should be addressed.
Academic Editor: Paul B. Tchounwou
Int. J. Environ. Res. Public Health 2021, 18(4), 2121; https://doi.org/10.3390/ijerph18042121
Received: 30 December 2020 / Revised: 8 February 2021 / Accepted: 12 February 2021 / Published: 22 February 2021
(This article belongs to the Special Issue Human and AI Collaborative Decision Making in Healthcare)
Objective: To provide a human–Artificial Intelligence (AI) interaction review for Machine Learning (ML) applications to inform how to best combine both human domain expertise and computational power of ML methods. The review focuses on the medical field, as the medical ML application literature highlights a special necessity of medical experts collaborating with ML approaches. Methods: A scoping literature review is performed on Scopus and Google Scholar using the terms “human in the loop”, “human in the loop machine learning”, and “interactive machine learning”. Peer-reviewed papers published from 2015 to 2020 are included in our review. Results: We design four questions to investigate and describe human–AI interaction in ML applications. These questions are “Why should humans be in the loop?”, “Where does human–AI interaction occur in the ML processes?”, “Who are the humans in the loop?”, and “How do humans interact with ML in Human-In-the-Loop ML (HILML)?”. To answer the first question, we describe three main reasons regarding the importance of human involvement in ML applications. To address the second question, human–AI interaction is investigated in three main algorithmic stages: 1. data producing and pre-processing; 2. ML modelling; and 3. ML evaluation and refinement. The importance of the expertise level of the humans in human–AI interaction is described to answer the third question. The number of human interactions in HILML is grouped into three categories to address the fourth question. We conclude the paper by offering a discussion on open opportunities for future research in HILML. View Full-Text
Keywords: human-in-the-loop machine learning; human–AI interaction; interactive machine learning; medical applications human-in-the-loop machine learning; human–AI interaction; interactive machine learning; medical applications
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MDPI and ACS Style

Maadi, M.; Akbarzadeh Khorshidi, H.; Aickelin, U. A Review on Human–AI Interaction in Machine Learning and Insights for Medical Applications. Int. J. Environ. Res. Public Health 2021, 18, 2121. https://doi.org/10.3390/ijerph18042121

AMA Style

Maadi M, Akbarzadeh Khorshidi H, Aickelin U. A Review on Human–AI Interaction in Machine Learning and Insights for Medical Applications. International Journal of Environmental Research and Public Health. 2021; 18(4):2121. https://doi.org/10.3390/ijerph18042121

Chicago/Turabian Style

Maadi, Mansoureh; Akbarzadeh Khorshidi, Hadi; Aickelin, Uwe. 2021. "A Review on Human–AI Interaction in Machine Learning and Insights for Medical Applications" Int. J. Environ. Res. Public Health 18, no. 4: 2121. https://doi.org/10.3390/ijerph18042121

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