A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health
Modern machine learning approaches have led to performant diagnostic models for a variety of health conditions. Several machine learning approaches, such as decision trees and deep neural networks, can, in principle, approximate any function. However, this power can be considered to be bo...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2024-04-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2024/1/e51138 |
| _version_ | 1827287599910223872 |
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| author | Peter Washington |
| author_facet | Peter Washington |
| author_sort | Peter Washington |
| collection | DOAJ |
| description |
Modern machine learning approaches have led to performant diagnostic models for a variety of health conditions. Several machine learning approaches, such as decision trees and deep neural networks, can, in principle, approximate any function. However, this power can be considered to be both a gift and a curse, as the propensity toward overfitting is magnified when the input data are heterogeneous and high dimensional and the output class is highly nonlinear. This issue can especially plague diagnostic systems that 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. This viewpoint describes existing work in this emerging field and discusses ongoing challenges and opportunities with crowd-powered diagnostic systems, a nascent field of study. With the correct considerations, the addition of crowdsourcing to human-in-the-loop machine learning workflows for the prediction of complex and nuanced health conditions can accelerate screening, diagnostics, and ultimately access to care. |
| first_indexed | 2024-04-24T11:10:56Z |
| format | Article |
| id | doaj.art-8dbeb4fee5bd4e3baf5645d5c189394b |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-04-24T11:10:56Z |
| publishDate | 2024-04-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-8dbeb4fee5bd4e3baf5645d5c189394b2024-04-11T15:00:50ZengJMIR PublicationsJournal of Medical Internet Research1438-88712024-04-0126e5113810.2196/51138A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision HealthPeter Washingtonhttps://orcid.org/0000-0003-3276-4411 Modern machine learning approaches have led to performant diagnostic models for a variety of health conditions. Several machine learning approaches, such as decision trees and deep neural networks, can, in principle, approximate any function. However, this power can be considered to be both a gift and a curse, as the propensity toward overfitting is magnified when the input data are heterogeneous and high dimensional and the output class is highly nonlinear. This issue can especially plague diagnostic systems that 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. This viewpoint describes existing work in this emerging field and discusses ongoing challenges and opportunities with crowd-powered diagnostic systems, a nascent field of study. With the correct considerations, the addition of crowdsourcing to human-in-the-loop machine learning workflows for the prediction of complex and nuanced health conditions can accelerate screening, diagnostics, and ultimately access to care.https://www.jmir.org/2024/1/e51138 |
| spellingShingle | Peter Washington A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health Journal of Medical Internet Research |
| title | A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health |
| title_full | A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health |
| title_fullStr | A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health |
| title_full_unstemmed | A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health |
| title_short | A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health |
| title_sort | perspective on crowdsourcing and human in the loop workflows in precision health |
| url | https://www.jmir.org/2024/1/e51138 |
| work_keys_str_mv | AT peterwashington aperspectiveoncrowdsourcingandhumanintheloopworkflowsinprecisionhealth AT peterwashington perspectiveoncrowdsourcingandhumanintheloopworkflowsinprecisionhealth |