## 4 Discussion  
To our knowledge, this study is the first attempt to propose a general AI-based whole-body 3D PET automated triage for general practice purposes, with a comparison of its performance in ideal conditions versus real-life practice. AI-based medical imaging support could become a major healthcare issue, especially due to the growing imbalance between the available dead manpower and the daily mass of imaging data [17]. In recent years, several PET studies have highlighted the promising performance of AI applied to basic segmentation or classification tasks [18; 19]. However, its clinical relevance to patient workflow remains an open question, especially because of the drawbacks presented by very limited data samples (mono- or multicenter), very limited application scopes and strictly nonambiguous data samples. Taking advantage of an institutional clinical data warehouse, nearly 5000 monocentric miscellaneous FDG PET data were fully exploited. This proof-of-concept study assessed the accuracy of a fully 3D-CNN at automatically classifying normal and abnormal whole-body FDG PETs in standard practice. Our results showed the 3D-CNN had a very impressive deep-learning capability to classify miscellaneous whole-body FDG PET scans when the corresponding PET data were voluntary and unequivocal. In this hyperselected context, the automated classification reached a Sen and Spe of 84% and 98%, respectively, with a PPV and NPV of 98% and 85% and a LR+ and LR- of 42 and 0.16, respectively. Interestingly, the model showed high specificity and NPV, emphasizing its capability to accurately identify \"completely normal\" from \"highly pathological\" scans. Our results from the \"unambiguous\" dataset are close to those obtained by [PERSON] et al. for the classification of clinical FDG PET MIP into benign/malignant or equivocal categories [19]. In their study, a ResNet-based 2D-CNN architecture reached 87.5-99% accuracy depending on the category. A low representation of non-oncological patients and equivocal scans, together with unknown characteristics of the training and test sets, should position their study as done under ideal conditions. Indeed, when applied to the remaining test set composed of ambiguous PET studies, our model performance dropped dramatically, reaching an NPV of only 5%. Recently, [PERSON] and coworkers [20] showed that their 2D neural network was highly efficient at identifying and delineating suspicious and nonsuspicious whole-body PET/CT uptakes in a monocentric cohort of 629 lymphoma and lung cancer patients. Similarly, when their PET Assisted Reporting System prototype (PARS) was applied to two additional external cohorts (119 lymphoma patients and 430 miscellaneous cancers) it failed to confirm the high initial performance [7]. One should consider the issues related to patient selection bias which leads to overrated models in AI-based imaging studies, especially in the high-pressure publishing context of a hot-topic field. In this sense, a recent review of AI-based radiological detection methods for COVID-19 highlighted the major methodological drawbacks and lack of model transparency in the majority of related models published in recent years, diminishing their clinical relevance to real-life practice [21]. Considering the intrinsic physiological variability of FDG distribution in the body, PET labeling conceptually appeared to be a hard task in our study. In particular, numerous inflammatory conditions show subtle FDG PET abnormalities, which would be, for the same patient, of low relevance in the case of oncological purposes. Thus, the right definition of a \"normal\" scan is challenging and needs to be further refined outside of optimized clinical contexts, in order to make automated general triage feasible in real clinical practice.