diff --git a/docs/source/transforms.rst b/docs/source/transforms.rst
index 529815ead9a..bd2b9cf540d 100644
--- a/docs/source/transforms.rst
+++ b/docs/source/transforms.rst
@@ -102,6 +102,13 @@ that can be represented in that dtype. Typically, images of dtype
 Use :class:`~torchvision.transforms.v2.ToDtype` to convert both the dtype and
 range of the inputs.
 
+.. note::
+
+    ``torch.uint16``, ``torch.uint32``, and ``torch.uint64`` dtypes are not
+    officially supported by the torchvision transforms. While some operations
+    may work, most transforms expect ``torch.uint8`` or ``torch.float32``
+    inputs.
+
 .. _v1_or_v2:
 
 V1 or V2? Which one should I use?
diff --git a/test/test_transforms.py b/test/test_transforms.py
index d93800d59bc..5b2658c99a4 100644
--- a/test/test_transforms.py
+++ b/test/test_transforms.py
@@ -614,8 +614,8 @@ def _get_1_channel_tensor_various_types():
         expected_output = img_data_byte.float().div(255.0).numpy()
         yield img_data_byte, expected_output, "L"
 
-        img_data_short = torch.ShortTensor(1, 4, 4).random_()
-        expected_output = img_data_short.numpy()
+        img_data_short = torch.ShortTensor(1, 4, 4).random_(0, 32767)
+        expected_output = img_data_short.numpy().astype(np.uint16)
         yield img_data_short, expected_output, "I;16" if sys.byteorder == "little" else "I;16B"
 
         img_data_int = torch.IntTensor(1, 4, 4).random_()
@@ -631,8 +631,8 @@ def _get_2d_tensor_various_types():
         expected_output = img_data_byte.float().div(255.0).numpy()
         yield img_data_byte, expected_output, "L"
 
-        img_data_short = torch.ShortTensor(4, 4).random_()
-        expected_output = img_data_short.numpy()
+        img_data_short = torch.ShortTensor(4, 4).random_(0, 32767)
+        expected_output = img_data_short.numpy().astype(np.uint16)
         yield img_data_short, expected_output, "I;16" if sys.byteorder == "little" else "I;16B"
 
         img_data_int = torch.IntTensor(4, 4).random_()
diff --git a/test/test_transforms_v2.py b/test/test_transforms_v2.py
index a87e601e1a6..a187811e340 100644
--- a/test/test_transforms_v2.py
+++ b/test/test_transforms_v2.py
@@ -6825,6 +6825,18 @@ 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/8188
+    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 7b950b0c45b..dacdd283bfb 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":