diff --git a/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py b/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
index 45d736efb..0ef67f2c3 100644
--- a/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
+++ b/hi-ml-multimodal/src/health_multimodal/image/model/pretrained.py
@@ -83,4 +83,39 @@ def get_biovil_t_image_encoder() -> ImageModel:
         joint_feature_size=JOINT_FEATURE_SIZE,
         pretrained_model_path=biovilt_checkpoint_path,
     )
+
+
+def get_biovil_t_linear_image_classifier(biovilt_checkpoint_path: str) -> ImageModel:
+    """
+    Download weights from Hugging Face and instantiate the image model.
+
+    The model is initialized with a linear classifier on top of the
+    BiomedVLP-BioViL-T image encoder.
+
+    Binary classification tasks in order:
+    ['Enlarged Cardiomediastinum', 'Cardiomegaly', 'Edema', 'Consolidation',
+     'Pneumonia',  'Pneumothorax', 'Pleural Effusion', 'No Finding']
+
+    :param biovilt_checkpoint_path: Path to the checkpoint file.
+
+    Example:
+    >>> checkpoint_path = "..."
+    >>> image_model = get_biovil_t_linear_image_classifier(checkpoint_path)
+    >>> image_model(torch.Tensor(batch_size, 3, 448, 448)).class_logits.shape
+    torch.Size([batch_size, num_classes, num_tasks])
+    """
+
+    num_classes = 2
+    num_tasks = 8
+
+    model_type = ImageEncoderType.RESNET50_MULTI_IMAGE
+    image_model = ImageModel(
+        img_encoder_type=model_type,
+        joint_feature_size=JOINT_FEATURE_SIZE,
+        pretrained_model_path=biovilt_checkpoint_path,
+        num_classes=num_classes,
+        num_tasks=num_tasks,
+        classifier_hidden_dim=None,
+    )
+
     return image_model
diff --git a/hi-ml-multimodal/test_multimodal/image/model/test_pretrained.py b/hi-ml-multimodal/test_multimodal/image/model/test_pretrained.py
new file mode 100644
index 000000000..515886cdb
--- /dev/null
+++ b/hi-ml-multimodal/test_multimodal/image/model/test_pretrained.py
@@ -0,0 +1,42 @@
+import pytest
+import torch
+
+from health_multimodal.image.model.model import ImageModel
+from health_multimodal.image.model.pretrained import get_biovil_t_linear_image_classifier
+
+
+@pytest.fixture
+def dummy_input_tensor() -> torch.Tensor:
+    batch_size = 2
+    return torch.randn(batch_size, 3, 448, 448)
+
+
+@pytest.fixture
+def biovil_t_linear_image_classifier() -> ImageModel:
+    # Set the path to None to initialize the model weights with random values
+    biovil_t_checkpoint_path = None
+    return get_biovil_t_linear_image_classifier(biovil_t_checkpoint_path)
+
+
+def test_get_biovil_t_linear_image_classifier_shape(
+    biovil_t_linear_image_classifier: ImageModel, dummy_input_tensor: torch.Tensor
+) -> None:
+    num_classes = 2
+    num_tasks = 8
+
+    output = biovil_t_linear_image_classifier(dummy_input_tensor)
+    expected_shape = torch.Size([dummy_input_tensor.shape[0], num_classes, num_tasks])
+
+    assert (
+        output.class_logits.shape == expected_shape
+    ), f"Unexpected output shape. Expected {expected_shape} but got {output.class_logits.shape}"
+
+
+def test_get_biovil_t_linear_image_classifier_inference(
+    biovil_t_linear_image_classifier: ImageModel, dummy_input_tensor: torch.Tensor
+) -> None:
+    with torch.no_grad():
+        try:
+            _ = biovil_t_linear_image_classifier(dummy_input_tensor)
+        except Exception as e:
+            pytest.fail(f"Model inference failed: {e}")