Update README.md

README.md

@@ -97,10 +97,13 @@ The data will follow the structure below:
 ## Social Impact
 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.
 
-## Limitations
+## Bias, Risks, and Limitations
+### Limitations
+- Data Quality: 
+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.
+Regarding images, a primary concern is also the incomplete nature of the dataset, as images do not accompany all patient records. Additionally, the image resolution varies, which can impede detailed examination. The inconsistency in image sizes and variations in the positioning of patient photographs may also pose challenges for consistent image analysis.
 
 ## Citation
-**BibTeX:**
 ```bibtex
 @dataset{NievasOffidani2023MultiCaRe,
   author       = {Nievas Offidani, M. and Delrieux, C.},