diff --git a/monai/data/utils.py b/monai/data/utils.py
index 8c5ae88289..164fa78814 100644
--- a/monai/data/utils.py
+++ b/monai/data/utils.py
@@ -50,8 +50,12 @@
     issequenceiterable,
     look_up_option,
     optional_import,
+    pytorch_after,
 )
 
+if pytorch_after(1, 13):
+    # import private code for reuse purposes, comment in case things break in the future
+    from torch.utils.data._utils.collate import collate_tensor_fn, default_collate_fn_map
 pd, _ = optional_import("pandas")
 DataFrame, _ = optional_import("pandas", name="DataFrame")
 nib, _ = optional_import("nibabel")
@@ -444,6 +448,23 @@ def pickle_operations(data, key=PICKLE_KEY_SUFFIX, is_encode: bool = True):
     return data
 
 
+def collate_meta_tensor_fn(batch, *, collate_fn_map=None):
+    """
+    Collate a sequence of meta tensor into a single batched metatensor. This is called by `collage_meta_tensor`
+    and so should not be used as a collate function directly in dataloaders.
+    """
+    collate_fn = collate_tensor_fn if pytorch_after(1, 13) else default_collate
+    collated = collate_fn(batch)  # type: ignore
+    meta_dicts = [i.meta or TraceKeys.NONE for i in batch]
+    common_ = set.intersection(*[set(d.keys()) for d in meta_dicts if isinstance(d, dict)])
+    if common_:
+        meta_dicts = [{k: d[k] for k in common_} if isinstance(d, dict) else TraceKeys.NONE for d in meta_dicts]
+    collated.meta = default_collate(meta_dicts)
+    collated.applied_operations = [i.applied_operations or TraceKeys.NONE for i in batch]
+    collated.is_batch = True
+    return collated
+
+
 def collate_meta_tensor(batch):
     """collate a sequence of meta tensor sequences/dictionaries into
     a single batched metatensor or a dictionary of batched metatensor"""
@@ -451,15 +472,7 @@ def collate_meta_tensor(batch):
         raise NotImplementedError()
     elem_0 = first(batch)
     if isinstance(elem_0, MetaObj):
-        collated = default_collate(batch)
-        meta_dicts = [i.meta or TraceKeys.NONE for i in batch]
-        common_ = set.intersection(*[set(d.keys()) for d in meta_dicts if isinstance(d, dict)])
-        if common_:
-            meta_dicts = [{k: d[k] for k in common_} if isinstance(d, dict) else TraceKeys.NONE for d in meta_dicts]
-        collated.meta = default_collate(meta_dicts)
-        collated.applied_operations = [i.applied_operations or TraceKeys.NONE for i in batch]
-        collated.is_batch = True
-        return collated
+        return collate_meta_tensor_fn(batch)
     if isinstance(elem_0, Mapping):
         return {k: collate_meta_tensor([d[k] for d in batch]) for k in elem_0}
     if isinstance(elem_0, (tuple, list)):
@@ -479,9 +492,16 @@ def list_data_collate(batch: Sequence):
         Need to use this collate if apply some transforms that can generate batch data.
 
     """
+
+    if pytorch_after(1, 13):
+        # needs to go here to avoid circular import
+        from monai.data.meta_tensor import MetaTensor
+
+        default_collate_fn_map.update({MetaTensor: collate_meta_tensor_fn})
     elem = batch[0]
     data = [i for k in batch for i in k] if isinstance(elem, list) else batch
     key = None
+    collate_fn = default_collate if pytorch_after(1, 13) else collate_meta_tensor
     try:
         if config.USE_META_DICT:
             data = pickle_operations(data)  # bc 0.9.0
@@ -490,9 +510,9 @@ def list_data_collate(batch: Sequence):
             for k in elem:
                 key = k
                 data_for_batch = [d[key] for d in data]
-                ret[key] = collate_meta_tensor(data_for_batch)
+                ret[key] = collate_fn(data_for_batch)
         else:
-            ret = collate_meta_tensor(data)
+            ret = collate_fn(data)
         return ret
     except RuntimeError as re:
         re_str = str(re)