diff --git a/README.md b/README.md
index dc0a140..ed332bf 100644
--- a/README.md
+++ b/README.md
@@ -49,6 +49,23 @@ Install PyTorch using pip:
    pip install matplotlib jupyter pandas seaborn pytorch-lightning scikit-learn scikit-image tensorboard tqdm openpyxl
    ```
    
+### How to use
+
+In order to replicate the results presented in the paper, please follow these steps:
+
+1. Download the [CheXpert dataset](https://stanfordmlgroup.github.io/competitions/chexpert/), copy the file `train.csv` to the `datafiles` folder
+2. Download the [CheXpert demographics data](https://stanfordaimi.azurewebsites.net/datasets/192ada7c-4d43-466e-b8bb-b81992bb80cf), copy the file `CHEXPERT DEMO.xlsx` to the `datafiles` folder
+3. Run the notebook `chexpert.sample.ipynb` to generate the study data
+4. Run the script `chexpert.disease.py` to train a disease detection model
+5. Run the script `chexpert.sex.py` to train a sex classification model
+6. Run the script `chexpert.race.py` to train a race classification model
+7. Run the notebook `chexpert.predictions.ipynb` to evaluate all three prediction models
+8. Run the notebook `chexpert.explorer.ipynb` for the unsupervised exploration of feature representations
+
+Additionally, there are scripts `chexpert.sex.split.py` and `chexpert.race.split.py` to run SPLIT on the disease detection model. The default setting in all scripts is to train a DenseNet-121 using the training data from all patients. The results for models trained on subgroups only can be produced by changing the path to the datafiles (e.g., using `full_sample_train_white.csv` and `full_sample_val_white.csv` instead of `full_sample_train.csv` and `full_sample_val.csv`).
+
+Note, the Python scripts also contain code for running the experiments using a ResNet-34 backbone which requires less GPU memory.
+   
 ## Funding sources
 This work is supported through funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 757173, [Project MIRA](https://www.project-mira.eu), ERC-2017-STG) and by the [UKRI London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare](https://www.aicentre.co.uk/).