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Artificial Intelligence Algorithms Are Not Clairvoyant REPLY
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DOI：10.2967/jnumed.124.267541.png)
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would mean that the AI algorithm was doing a better job than the obvious to the radiologist. At the extreme, the AI algorithm could tally, though, this is lesion detection, not image enhancement. It is important to understand that this is what is going on, not actual improvement in image quality or information content. The radiologist is (effectively) no longer making the assessment. Moreover, applicatumor volume, or total lesion glycolysis) may no longer be accurate. When processing data to form images, we are often very careful about the type of prior information we incorporate. Generally, we understand the degree (and sometimes direction) of the bias that the prior information imposes (e.g., expectation maximization's constraint to positive solutions). However, we usually avoid biases in favor of gaining an image assay that is as independent and unbiased as possible. Thus, to the extent possible, the image assay provides completely new information. For example, in PET image reconstruction of PET/MRI data, we generally forgo using the MRI as a prior even though these images will likely appear lower in noise and higher in resolution. This is because it is understood that biasing the PET image toward the MRI will result in a loss of PET information in precisely the regions containing the greatest amount of new information (i.e., the regions lacking mutual information between the PET and MRI). There is some space within the context of PET and SPECT image reconstruction (or other means of generating medical images) where it might be appropriate to apply AI techniques. For PET raw or proThus, improving accuracy in one subregion along this projection the MRI (or AI-derived prior information) during PET image reconstruction is to some extent defendable, whereas reducing scan time and then applying AI-based image enhancement after reconstruction simply is not.
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