e.g. mhealth
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Crowdsourced approaches to data set labeling are growing in popularity, and beneficial effects of crowdsourcing have been demonstrated in health care–related tasks including biomedical imaging analysis [10-14].
Using crowdsourcing for biomedical image labeling is challenged by the complexity of the tasks and the need to ensure label quality control. The user interface design for collecting crowd opinions and the metrics used for assessing opinion quality is key to successful results.
J Med Internet Res 2024;26:e51397
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One study reported that it took 6 months to obtain expert labels comprising 340 sentences from radiology reports written by 2 radiologists, whereas the authors obtained crowdsourced annotations of 717 sentences in under 2 days at a cost of less than $600. A classification algorithm trained using these crowdsourced annotations outperformed an algorithm trained using the expert-labeled data as a result of the increased volume of available training examples [32].
JMIR Med Inform 2023;11:e38412
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Hosio et al [43] developed a crowdsourced online system named Back Pain Workshop. They collected 2 knowledge bases, 1 from clinical professionals and 1 from nonprofessionals. Professionals found the system beneficial for self-reflection and educating new patients, while nonprofessionals acknowledged the reliable decision support that also respected the nonprofessional opinion [43].
JMIR Hum Factors 2022;9(3):e38265
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The study is a crowdsourced initiative [20] that will involve remote enrollment. It will use a cross-platform phone app to deliver surveys; allow for the input of COVID-19–related data; and allow participants to connect to third-party sources of wearable data, such as Fitbit LLC. By prospectively collecting regular mental well-being and COVID-19 survey data alongside historic and ongoing health-related wearable device data, we hope to address the following objectives.
JMIR Res Protoc 2021;10(12):e32587
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