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Levels of Autonomous Radiology
Cross-center training of the AI model for different demographics and distinct cohort features would help the model learn from multiple sources and mitigate the problem of generalizability.
Advancing from level 3, the AI systems at Level 4: High Automation would make decisions without the assistance of a radiologist. Human intervention would only be required in complex cases where the AI requests it. Such systems would require extensive clinical validations before they could be reliably used.
Interact J Med Res 2022;11(2):e38655
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Improving Skin Color Diversity in Cancer Detection: Deep Learning Approach
Based on this, set B will facilitate our evaluation of the generalizability of the classification model developed using the generated images, as it has variant skin tone distribution compared to the training data.
Study data sets for malignant and benign class distribution [21]. Set A (n=1094): training and validation set; set B (n=607): testing set.
Skin tone distribution of the study data sets. Set A (n=1094): training and validation set; set B (n=607): testing set.
JMIR Dermatol 2022;5(3):e39143
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generalizabilityAssessing Generalizability of Deep Learning Models Trained on Standardized and Nonstandardized Images
iproc 2021;7(1):e35391
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Indeed, one key feature of ROBOKOP is its generalizability across biomedical domains as a general question-answering system, with capabilities to support a variety of machine questions. For instance, we are using ROBOKOP to explore associations between medications and clinical outcomes, including adverse events, using data derived from electronic health records.
JMIR Med Inform 2021;9(7):e26714
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In this study, we sought to implement MED.Safe, a software package of medication discrepancy detection algorithms, and benchmark the results to our earlier work at the development site to determine its portability and generalizability. We analyzed the system outputs at an external site, highlighting where and in what context the system performed well, and suggested customizations to further improve its performance.
JMIR Med Inform 2020;8(12):e22031
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This hardly can be controlled for in a survey including only male responders, but points to possible limitations regarding the generalizability of survey RR findings to women. In this regard, however, the role of gender has been questioned, at least when it comes to cancer issues [38]. Furthermore, it should be noted that only municipality-level register data on responders’ sociodemographic characteristics were used. For example, the income of individual responders was not available.
J Med Internet Res 2020;22(9):e19517
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Moreover, the psychiatric inclusion and exclusion criteria are only one aspect of the generalizability of the sample. Self-guided i CBT studies may have more selective patient samples by virtue of other criteria that they use explicitly (eg, access to the internet) or implicitly (eg, willingness to participate in an i CBT study).
While we updated the review by Karyotaki et al [17], we drew on published reviews by Lorenzo-Luaces et al [30], instead of updating this review as well.
J Med Internet Res 2018;20(11):e10113
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