Korean J Gastroenterol.  2023 Jul;82(1):43-45. 10.4166/kjg.2023.071.

Interpretation of Medical Images Using Artificial Intelligence: Current Status and Future Perspectives

• Gong EJ
• Bang CS

• KMID: 2544940
• DOI: http://doi.org/10.4166/kjg.2023.071

Reference

1. Rajpurkar P, Lungren MP. 2023; The current and future state of AI interpretation of medical images. N Engl J Med. 388:1981–1990. DOI: 10.1056/NEJMra2301725. PMID: 37224199.

2. Bang CS. 2020; [Deep learning in upper gastrointestinal disorders: Status and future perspectives]. Korean J Gastroenterol. 75:120–131. Korean. DOI: 10.4166/kjg.2020.75.3.120. PMID: 32209800.

3. Bang CS. 2021; Artificial intelligence in the analysis of upper gastrointestinal disorders. Korean J Helicobacter Up Gastrointest Res. 21:300–310. DOI: 10.7704/kjhugr.2021.0030.

4. Bang CS, Lee JJ, Baik GH. 2021; Computer-aided diagnosis of esophageal cancer and neoplasms in endoscopic images: a systematic review and meta-analysis of diagnostic test accuracy. Gastrointest Endosc. 93:1006–1015.e13. DOI: 10.1016/j.gie.2020.11.025. PMID: 33290771.

5. Gong EJ, Bang CS, Lee JJ, et al. Deep learning-based clinical decision support system for gastric neoplasms in real-time endoscopy: development and validation study. Endoscopy. 2023; Apr. 17. doi: 10.1055/a-2031-0691. DOI: 10.1055/a-2031-0691.

6. Bang CS, Lim H, Jeong HM, Hwang SH. 2021; Use of endoscopic images in the prediction of submucosal invasion of gastric neoplasms: Automated deep learning model development and usability study. J Med Internet Res. 23:e25167. DOI: 10.2196/25167. PMID: 33856356. PMCID: PMC8085753.

7. Moor M, Banerjee O, Abad ZSH, et al. 2023; Foundation models for generalist medical artificial intelligence. Nature. 616:259–265. DOI: 10.1038/s41586-023-05881-4. PMID: 37045921.

8. Yang YJ, Bang CS. 2019; Application of artificial intelligence in gastroenterology. World J Gastroenterol. 25:1666–1683. DOI: 10.3748/wjg.v25.i14.1666. PMID: 31011253. PMCID: PMC6465941.

9. Gong EJ, Bang CS, Lee JJ, Yang YJ, Baik GH. 2022; Impact of the volume and distribution of training datasets in the development of deep-learning models for the diagnosis of colorectal polyps in endoscopy images. J Pers Med. 12:1361. DOI: 10.3390/jpm12091361. PMID: 36143146. PMCID: PMC9505038.

10. 2023. ChatGPT (Mar 14 version) [Large language model] [Internet]. OpenAI;Available from: https://chat.openai.com/chat. cited 2023 Jun 6.

11. 2023. Bard [Large language model] [Internet]. Google AI;Available from: https://bard.google.com. cited 2023 Jun 6.

12. Cho BJ, Bang CS, Park SW, et al. 2019; Automated classification of gastric neoplasms in endoscopic images using a convolutional neural network. Endoscopy. 51:1121–1129. DOI: 10.1055/a-0981-6133. PMID: 31443108.

13. Cho BJ, Bang CS. 2020; Artificial intelligence for the determination of a management strategy for diminutive colorectal polyps: Hype, hope, or help. Am J Gastroenterol. 115:70–72. DOI: 10.14309/ajg.0000000000000476. PMID: 31770118.