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Improving the Applicability of AI for Psychiatric Applications through Human-in-the-loop Methodologies

Schizophrenia bulletin, 2022-09, Vol.48 (5), p.949-957 [Peer Reviewed Journal]

The Author(s) 2022. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. 2022 ;The Author(s) 2022. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. ;info:eu-repo/semantics/openAccess ;ISSN: 0586-7614 ;ISSN: 1745-1701 ;EISSN: 1745-1701 ;DOI: 10.1093/schbul/sbac038 ;PMID: 35639561

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  • Title:
    Improving the Applicability of AI for Psychiatric Applications through Human-in-the-loop Methodologies
  • Author: Chandler, Chelsea ; Foltz, Peter W ; Elvevåg, Brita
  • Subjects: Theme: Translating Natural Language Processing (NLP) into mainstream schizophrenia assessment
  • Is Part Of: Schizophrenia bulletin, 2022-09, Vol.48 (5), p.949-957
  • Description: Abstract Objectives Machine learning (ML) and natural language processing have great potential to improve efficiency and accuracy in diagnosis, treatment recommendations, predictive interventions, and scarce resource allocation within psychiatry. Researchers often conceptualize such an approach as operating in isolation without much need for human involvement, yet it remains crucial to harness human-in-the-loop practices when developing and implementing such techniques as their absence may be catastrophic. We advocate for building ML-based technologies that collaborate with experts within psychiatry in all stages of implementation and use to increase model performance while simultaneously increasing the practicality, robustness, and reliability of the process. Methods We showcase pitfalls of the traditional ML framework and explain how it can be improved with human-in-the-loop techniques. Specifically, we applied active learning strategies to the automatic scoring of a story recall task and compared the results to a traditional approach. Results Human-in-the-loop methodologies supplied a greater understanding of where the model was least confident or had knowledge gaps during training. As compared to the traditional framework, less than half of the training data were needed to reach a given accuracy. Conclusions Human-in-the-loop ML is an approach to data collection and model creation that harnesses active learning to select the most critical data needed to increase a model’s accuracy and generalizability more efficiently than classic random sampling would otherwise allow. Such techniques may additionally operate as safeguards from spurious predictions and can aid in decreasing disparities that artificial intelligence systems otherwise propagate.
  • Publisher: US: Oxford University Press
  • Language: English
  • Identifier: ISSN: 0586-7614
    ISSN: 1745-1701
    EISSN: 1745-1701
    DOI: 10.1093/schbul/sbac038
    PMID: 35639561
  • Source: Geneva Foundation Free Medical Journals
    Oxford Open (Open Access)
    NORA Norwegian Open Research Archives
    PubMed Central
    Alma/SFX Local Collection
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