A veterinary clinic wants an AI feature that reviews each uploaded x-ray image and assigns it to exactly one of four triage categories, normal, fracture, foreign object, or inconclusive, so a technician can prioritize review order. The clinic does not need the software to mark where on the x-ray any abnormality appears, only which single category best describes the whole image. Which computer vision capability is the simplest fit?

The correct answer is C. Assigning each x-ray to exactly one of four categories based on the image as a whole, without needing to indicate where on the image any abnormality appears, is a straightforward image classification task, since only a single whole-image label is required. Option A is incorrect because object detection returns the location of one or more objects within an image, typically as bounding boxes, which is more detail than the clinic's triage workflow requires. Option B is incorrect because image segmentation produces a pixel-level outline of regions within an image, an even more granular and computationally heavier capability than the clinic needs for a single triage label. Option D is incorrect because optical character recognition reads printed or handwritten text within an image, which is unrelated to interpreting the visual content of an x-ray. Since the clinic only needs a single category per image to prioritize technician review, and has no requirement to localize an abnormality, the simplest capability that satisfies the need is whole-image classification rather than a more detailed localization or text-reading capability.