Update README.md

README.md

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 The multimodal dataset of tuberculosis patients, meticulously curated from the larger MultiCaRe Dataset, stands to have a significant social impact, particularly in the field of public health and medical research. Tuberculosis (TB) remains a major global health issue, especially in low- and middle-income countries, and the integration of CT imaging with clinical case reports in this dataset provides a rich resource for advanced diagnostic and treatment research. By facilitating the development of more precise algorithms for CT image segmentation and classification, as well as enhancing natural language processing (NLP) techniques for extracting medical terms from clinical notes, this dataset has the potential to improve the accuracy and efficiency of TB diagnosis.
 
 ## Bias, Risks, and Limitations
+### Bias
+1. Selection Bias: The original MultiCaRe Dataset was generated from 75,382 open access PubMed Central articles spanning the period from 1990 to 2023. Therefore, the random sampling of the cases from difference demographic groups cannot be guaranteed. The data may have bias as the collection process was not representative of the broader population. For example, the dataset may predominantly includes cases from a specific geographic location, age group, or socioeconomic status, and the findings may not apply to other groups.
+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.
+
 ### Limitations
 - Data Quality: 
 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.