# Generative Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease
## 3 Results  
### Summary  
The summary of the review is shown in Table 1 (or Table 2) for augmentation, classification, detection, generation and segmentation based on the GAN (or transformer). The tables have seven summary measures: (1) sample size (participants); (2-4) deep learning title, innovation and baseline (interventions and comparisons); (5-6) innovation vs. baseline performance outcomes; and (7) dependent variable (participants) for classification, detection and/or segmentation. Based on the results of this study, different generative artificial intelligence methods would be appropriate for different tasks for the early diagnosis of gastrointestinal disease. For example, patch GAN (accuracy 91.9%) in the case of classification, bi-directional cycle GAN (structural similarity 98.8%) in the case of data generation and semi-supervised GAN (Dice 89.4%) in the case of segmentation. Their performance indicators reported varied within 87.1-91.9% for accuracy, 83.0-98.8% for structural similarity and 86.6-89.4% for Dice. Likewise, vision transformer (accuracy 96.9%) in the case of classification, multi-modal transformer (IOU 79.5%) in the case of detection and multi-modal transformer (Dice 89.5%) in the case of segmentation. Their performance measures reported registered a variation within 85.7-96.9% for accuracy, 79.5% for IOU and 77.8-89.5% for Dice. However, artificial intelligence is a data-driven method and more study is to be completed with more external data for greater external validity.  
Figure 2: Flow diagram.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**ID** & **Sample Size** & **Method** & **Constative Adversarial Network** & & **Performance** & & **Dependent Variable** \\  & _Title_ & _Imputation_ & _Baseline_ & _Innovation_ & _Baseline_ & & \\ \hline
21 & 630 & Patch & Endoscopist & 91.9 & 83.6 & CL Cancer Gastric \\
21 & 630 & Patch & Unet & 86.6 & 77.6 & EC Cancer Gastric \\
22 & 16,000 & Conditional & Conditional & 02.5 & 02.6 & 01.4 & CL Cancer Colorectal \\
23 & 80,000 & Endol.2H & Conditional \& Spatial Attention & Conditional & 83.0 & 69.0 & GE Adenoma Small Intestine \\
24 & 589 & Deep Convolutional & Alex & 90.0 & 81.0 & AU Adenocarcinoma Esophagues \\
27 & 4880 & Semi-Supervised & Supervised & 89.4 & 82.1 & SE GID \\
28 & & Cycle-Desmoking & Cycle & 91.0 & 81.1 & GE CID \\
30 & 1290 & Cycle-B-Direction & Cycle-Unit-Direction & 98.8 & 96.8 & GE CID \\
31 & 1508 & Cycle \& K-Means & K-Means & 87.1 & 84.8 & CL Cancer Gastrointestinal \\
32 & 107,060 & Progressive Growing & Endoscopist & 61.3 & 61.3 & GE CID \\ \hline \multicolumn{2}{c}{Note} & \multicolumn{1}{c}{Abbreviations: AU—Augmentation: CL—Classification: DE—Detection: GE—Generation: CID—Castrointestinal Disease: NE—Nrontal Endotracheal Inhibition: NIC—Nrontal: Invasive Care Unit: SE-Segmentation.} \\ \hline \end{tabular}
\end{table}
Table 1: Summary of review—Generative Adversarial Network.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**ID** & **Sample Size** & **Method** & **Constative Adversarial Network** & & **Performance** & & **Dependent Variable** \\  & _Title_ & _Imputation_ & _Baseline_ & _Innovation_ & _Baseline_ & & \\ \hline
25 & 7983 & Vision & Pathologist & 91.2 & 91.2 & CL Besophagal Disease \\
26 & 1645 & TransMT-Net & Vision & Alex & 96.9 & 95.7 & CL GID \\
26 & 1645 & TransMT-Net & Vision & Unet & 77.8 & 65.2 & SE GID \\
29 & 1886 & ECCCap & Multi-Modal & Endoscopist Only & 79.5 & 74.9 & DE Cancer Gastric \\
33 & 219 & Multi-Modal & Numeric & 85.7 & 64.8 & CL NICU-NEI \\
34 & 11,500 & DSM-Net & Vision Self-Supervised & Vision & 89.2 & 79.1 & CL Cancer Breast- Pancreatic \\
35 & 41,987 & BiFransNet & Multi-Modal & Unet & 89.5 & 88.3 & SE Cancer Gastrointestinal \\