diff --git a/docs/source/transforms.rst b/docs/source/transforms.rst
index 4bb18cf6b48..4d95d314669 100644
--- a/docs/source/transforms.rst
+++ b/docs/source/transforms.rst
@@ -91,7 +91,8 @@ the tensor dtype. Tensor images with a float dtype are expected to have
 values in ``[0, 1]``. Tensor images with an integer dtype are expected to
 have values in ``[0, MAX_DTYPE]`` where ``MAX_DTYPE`` is the largest value
 that can be represented in that dtype. Typically, images of dtype
-``torch.uint8`` are expected to have values in ``[0, 255]``.
+``torch.uint8`` are expected to have values in ``[0, 255]``. Note that dtypes
+like ``torch.uint16`` or ``torch.uint32`` aren't fully supported.
 
 Use :class:`~torchvision.transforms.v2.ToDtype` to convert both the dtype and
 range of the inputs.
diff --git a/test/test_transforms_v2.py b/test/test_transforms_v2.py
index f9218c3e840..32acc5d6930 100644
--- a/test/test_transforms_v2.py
+++ b/test/test_transforms_v2.py
@@ -5478,6 +5478,18 @@ def test_functional_error(self):
             F.pil_to_tensor(object())
 
 
+@pytest.mark.parametrize("f", [F.to_tensor, F.pil_to_tensor])
+def test_I16_to_tensor(f):
+    # See https://github.com/pytorch/vision/issues/8359
+    I16_pil_img = PIL.Image.fromarray(np.random.randint(0, 2**16, (10, 10), dtype=np.uint16))
+    assert I16_pil_img.mode == "I;16"
+
+    cm = pytest.warns(UserWarning, match="deprecated") if f is F.to_tensor else contextlib.nullcontext()
+    with cm:
+        out = f(I16_pil_img)
+    assert out.dtype == torch.uint16
+
+
 class TestLambda:
     @pytest.mark.parametrize("input", [object(), torch.empty(()), np.empty(()), "string", 1, 0.0])
     @pytest.mark.parametrize("types", [(), (torch.Tensor, np.ndarray)])
diff --git a/torchvision/transforms/functional.py b/torchvision/transforms/functional.py
index 8efe2a8878a..09edfeea35e 100644
--- a/torchvision/transforms/functional.py
+++ b/torchvision/transforms/functional.py
@@ -164,7 +164,7 @@ def to_tensor(pic: Union[PILImage, np.ndarray]) -> Tensor:
         return torch.from_numpy(nppic).to(dtype=default_float_dtype)
 
     # handle PIL Image
-    mode_to_nptype = {"I": np.int32, "I;16" if sys.byteorder == "little" else "I;16B": np.int16, "F": np.float32}
+    mode_to_nptype = {"I": np.int32, "I;16" if sys.byteorder == "little" else "I;16B": np.uint16, "F": np.float32}
     img = torch.from_numpy(np.array(pic, mode_to_nptype.get(pic.mode, np.uint8), copy=True))
 
     if pic.mode == "1":