# Generative Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease
## 4 Discussion
### Generative Artificial Intelligence with Reinforcement Learning
\begin{table}
\begin{tabular}{|p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}|} \hline
**Measure** & **Description** & **Discriminable** & **Overfitting** & **Sensitive** \\ \hline
01 Average Likelihood & Average likelihood of model distribution given generated sample data & Low & Low & Low \\
02 Coverage Metric & Probability mass of generated sample data covered by model distribution & Low & Low & Low \\
03 Inception Score (IS) & Quality and overall diversity of generated sample data & High & Middle & Middle \\
04 Modified IS & Quality and within-class diversity of generated sample data & High & Middle & Middle \\
05 Mode Score & IS with the consideration of prior label distribution over sample data & High & Middle & Middle \\
06 AM Score & IS with the consideration of training vs. test label distribution over sample data & High & Middle & Middle \\
07 FID & Gaussian distance between generated sample data and real data distributions & High & Middle & High \\
08 MMD & Discrepancy between model distributions given generated sample data & High & Low & - \\
09 Wasserstein Critic & Wasserstein distance between generated sample data and real data distributions & High & Middle & - \\
10 BPT & Support size of a discrete distribution & Low & High & Low \\
11 C2 ST & Classifier whether two sample data are drawn from the same distribution & High & Low & - \\
12 Classification & Classifier how well extracted features predict class labels & High & Low & - \\
13 Boundary Distortion & Classifier how much diversity loss boundary distortion causes & Low & Low & \\
14 NSDB & Number of statistical different bins denoting the diversity of generated sample data & Low & High & Low \\
15 Image Retrieval & Quality for the nearest neighbors of generated sample data in the test set retrieved & Middle & Low & - \\
16 GAM & Likelihood ratio of two models with generators or discriminators being switched & High & Low & - \\
17 Tournament Win & Tournament win rates of competing models & High & High & - \\
18 NRDS & Number of training epochs needed to distinguish generated sample data from real data & High & Low & - \\
19 AAAD & Accuracies of two classifiers on generated sample data and real data & High & Low & - \\
20 Geometry Score & Geometric properties of generated samples data vs. real data & Low & Low & Low \\
21 Reconstruction Error & Reconstruction error between generated sample data and real data & Low & Middle \\
22 Structural Similarity & Similarity in terms of luminance, contrast and structure & Low & Middle & High \\
23 LIIS & Similarity in terms of four low-level image statistics such as mean & Low & Low & Low \\
24 F1 Score & F1 scores of two classifiers on generated sample data and real data & Low & High & - \\ \hline \end{tabular} Note: FID Frechet Inception Distance, MMD, Maximum Mean Discrepancy, BPT Birthday Paradox Test, C2 ST Classifier Two Sample Test; NSDB Number of Statistically Different Bins, GAM Generative Adversarial Metric, NRDS Normalized Relative Discriminative Score; AAAD Adversarial Accuracy and Adversarial Divergence, LIIS Low-Level Image Statistics.  
\end{table} 
Table 3: Evaluation measures of generative artificial intelligence in data generation. small) [44]. In a recent study, stomach coverage scanning of wireless capsule endoscopy with reinforcement learning was found to achieve a much higher coverage rate than its manual counterpart, i.e., 98% vs. 87% during 150 s [43]. However, little research has been conducted and more study is needed on generative artificial intelligence with reinforcement learning.