Medical MT5-xl-multitask is a version of Medical MT5 finetuned for sequence labelling. It can correctly label a wide range of Medical labels in unstructured text, such as Disease, Disability, ClinicalEntity, Chemical... Medical MT5-xl-multitask has been finetuned for English, Spanish, French and Italian, although it may work with a wide range of languages.

Medical MT5-xl-multitask was training using the Sequence-Labeling-LLMs library: https://github.com/ikergarcia1996/Sequence-Labeling-LLMs/
This library uses constrained decoding to ensure that the output contains the same words as the input and a valid HTML annotation. We recommend using Medical MT5-xl-multitask together with this library. Although you can also directly use it with 🤗 huggingface. In order to label a sentence, you need to append the labels you wan to use, for example, if you want to label dieseases you should format your input as follows: <Disease> Torsade de pointes ventricular tachycardia during low dose intermittent dobutamine treatment in a patient with dilated cardiomyopathy and congestive heart failure .

Our research in developing Medical mT5, a multilingual text-to-text model for the medical domain, has ethical implications that we acknowledge. Firstly, the broader impact of this work lies in its potential to improve medical communication and understanding across languages, which can enhance healthcare access and quality for diverse linguistic communities. However, it also raises ethical considerations related to privacy and data security. To create our multilingual corpus, we have taken measures to anonymize and protect sensitive patient information, adhering to data protection regulations in each language's jurisdiction or deriving our data from sources that explicitly address this issue in line with privacy and safety regulations and guidelines. Furthermore, we are committed to transparency and fairness in our model's development and evaluation. We have worked to ensure that our benchmarks are representative and unbiased, and we will continue to monitor and address any potential biases in the future. Finally, we emphasize our commitment to open source by making our data, code, and models publicly available, with the aim of promoting collaboration within the research community.