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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Jul 22, 2023
Open Peer Review Period: Jul 22, 2023 - Sep 16, 2023
Date Accepted: Jan 30, 2024
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health

Washington P

A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health

J Med Internet Res 2024;26:e51138

DOI: 10.2196/51138

PMID: 38602750

PMCID: 11046386

Crowdsourcing and Human-In-The-Loop Workflows in Precision Health: Perspective

  • Peter Washington

ABSTRACT

The age of deep learning has brought high-performing diagnostic models for a variety of healthcare conditions. Deep neural networks can, in principle, approximate any function. However, this power can be considered both a gift and a curse, as the propensity towards overfitting is magnified when the input data are heterogeneous and high dimensional coupled with an output class which is highly nonlinear. This issue can especially plague diagnostic systems which predict behavioral and psychiatric conditions that are diagnosed with subjective criteria. An emerging solution to this issue is crowdsourcing, where crowd workers are paid to annotate complex behavioral features in return for monetary compensation or a gamified experience. These labels can then be used to derive a diagnosis, either directly or by using the labels as inputs to a diagnostic machine learning model. Here, I describe existing work in this field. I then discuss ongoing challenges and opportunities with crowd-powered diagnostic systems. With the correct considerations, the addition of crowdsourcing into machine learning workflows for prediction of complex and nuanced health conditions can rapidly accelerate screening, diagnostics, and ultimately access to care.


 Citation

Please cite as:

Washington P

A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health

J Med Internet Res 2024;26:e51138

DOI: 10.2196/51138

PMID: 38602750

PMCID: 11046386

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