Generative Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease - Kwang-Sig Lee; Eun Sun Kim (Applied Sciences)
This study reviews the recent progress of generative artificial intelligence for gastrointestinal disease (GID) from detection to diagnosis. The source of data was 16 original studies in PubMed. The search terms were ((gastro* [title]) or (endo* [title])) and ((GAN [title/abstract] or (transformer [title/abstract]). The eligibility criteria were as follows: (1) the dependent variable of gastrointestinal disease; (2) the interventions of generative adversarial network (GAN) and/or transformer for classification, detection and/or segmentation; (3) the outcomes of accuracy, intersection of union (IOU), structural similarity and/or Dice; (3) the publication period of 2021–2023; (4) the publication language of English. 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. Synthesizing different kinds of generative artificial intelligence for different kinds of GID data would further the horizon of research on this topic. In conclusion, however, generative artificial intelligence provides an effective, non-invasive decision support system for the early diagnosis of gastrointestinal disease from detection to diagnosis.