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README.md
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2. Technology Bias: Advanced imaging technologies might not be equally available in all settings, leading to a dataset that disproportionately represents patients from better-equipped facilities. This can skew the dataset towards conditions that are more likely to be diagnosed in such settings.
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3. Interpreter Bias: For the `"case_text"` and the `"caption"`, variability in the expertise and experience of radiologists or clinicians interpreting the images can lead to differences in diagnosis or findings reported in the dataset.
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### Limitations
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- Data Quality:
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1. For textual data, certain patient records are missing key descriptive terms. Meanwhile, cases where imaging studies were not conducted lack both the images and their respective descriptive captions.
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2. Technology Bias: Advanced imaging technologies might not be equally available in all settings, leading to a dataset that disproportionately represents patients from better-equipped facilities. This can skew the dataset towards conditions that are more likely to be diagnosed in such settings.
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3. Interpreter Bias: For the `"case_text"` and the `"caption"`, variability in the expertise and experience of radiologists or clinicians interpreting the images can lead to differences in diagnosis or findings reported in the dataset.
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### Risks
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1. Privacy and Confidentiality Risks: Patient data, including case records and images, are highly sensitive. There's a risk of identifying individuals even if the data is properly anonymized.
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2. Data Integrity and Quality Risks: Inaccuracies, missing data, and inconsistencies within the dataset can compromise the validity of research findings or clinical decisions based on the data. This could lead to ineffective or harmful interventions.
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### Limitations
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- Data Quality:
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1. For textual data, certain patient records are missing key descriptive terms. Meanwhile, cases where imaging studies were not conducted lack both the images and their respective descriptive captions.
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