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diff --git a/monai/tests/test_clinical_preprocessing.py b/monai/tests/test_clinical_preprocessing.py
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+++ b/monai/tests/test_clinical_preprocessing.py
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+import numpy as np
+
+from monai.transforms import ScaleIntensityRange, NormalizeIntensity
+
+
+def test_ct_windowing_range_and_shape():
+    rng = np.random.default_rng(0)
+
+    sample_ct = rng.integers(
+        -1024, 2048, size=(64, 64, 64), dtype=np.int16
+    )
+
+    transform = ScaleIntensityRange(
+        a_min=-1000,
+        a_max=400,
+        b_min=0.0,
+        b_max=1.0,
+        clip=True,
+    )
+
+    output = transform(sample_ct)
+    output = np.asarray(output)
+
+    assert output.shape == sample_ct.shape
+    assert np.isfinite(output).all()
+    assert output.min() >= -1e-6
+    assert output.max() <= 1.0 + 1e-6
+
+
+def test_mri_normalization_mean_std():
+    rng = np.random.default_rng(0)
+
+    sample_mri = rng.random((64, 64, 64), dtype=np.float32)
+
+    transform = NormalizeIntensity(nonzero=True)
+
+    output = transform(sample_mri)
+    output = np.asarray(output)
+
+    mean_val = float(output.mean())
+    std_val = float(output.std())
+
+    assert output.shape == sample_mri.shape
+    assert np.isclose(mean_val, 0.0, atol=0.1)
+    assert np.isclose(std_val, 1.0, atol=0.1)
diff --git a/monai/transforms/clinical_preprocessing.py b/monai/transforms/clinical_preprocessing.py
new file mode 100644
index 0000000000..ab6a10cb74
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+++ b/monai/transforms/clinical_preprocessing.py
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+from typing import Union
+
+from monai.transforms import (
+    Compose,
+    LoadImage,
+    EnsureChannelFirst,
+    ScaleIntensityRange,
+    NormalizeIntensity,
+)
+
+
+def get_ct_preprocessing_pipeline():
+    """
+    CT preprocessing pipeline using standard HU windowing.
+    """
+    return Compose(
+        [
+            LoadImage(image_only=True),
+            EnsureChannelFirst(),
+            ScaleIntensityRange(
+                a_min=-1000,
+                a_max=400,
+                b_min=0.0,
+                b_max=1.0,
+                clip=True,
+            ),
+        ]
+    )
+
+
+def get_mri_preprocessing_pipeline():
+    """
+    MRI preprocessing pipeline using intensity normalization.
+    """
+    return Compose(
+        [
+            LoadImage(image_only=True),
+            EnsureChannelFirst(),
+            NormalizeIntensity(nonzero=True),
+        ]
+    )
+
+
+def preprocess_dicom_series(
+    dicom_path: Union[str, bytes],
+    modality: str,
+):
+    """
+    Preprocess a DICOM series based on modality.
+
+    Args:
+        dicom_path: Path to DICOM file or directory.
+        modality: CT, MR, or MRI.
+
+    Returns:
+        Preprocessed image.
+    """
+    if not isinstance(modality, str):
+        raise TypeError("modality must be a string")
+
+    modality = modality.strip().upper()
+
+    if modality == "CT":
+        transform = get_ct_preprocessing_pipeline()
+    elif modality in ("MR", "MRI"):
+        transform = get_mri_preprocessing_pipeline()
+    else:
+        raise ValueError("Unsupported modality")
+
+    return transform(dicom_path)