Exploring chatGPT'S potential for consultation, recommendations and report diagnosisGastric cancer and gastroscopy reports’ case

  1. Jiaming Zhou 1
  2. Tengyue Li 1
  3. Simon James Fong 1
  4. Nilanjan Dey 2
  5. Rubén González Crespo 3
  1. 1 University of Macau
    info

    University of Macau

    Macao, Macao

    ROR https://ror.org/01r4q9n85

  2. 2 Techno International New Town
  3. 3 Universidad Internacional de La Rioja
    info

    Universidad Internacional de La Rioja

    Logroño, España

    ROR https://ror.org/029gnnp81

Revista:
IJIMAI

ISSN: 1989-1660

Año de publicación: 2023

Volumen: 8

Número: 2

Páginas: 7-13

Tipo: Artículo

DOI: 10.9781/IJIMAI.2023.04.007 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: IJIMAI

Resumen

Artificial intelligence (AI) has shown its effectiveness in helping clinical users meet evolving challenges. Recently, ChatGPT, a newly launched AI chatbot with exceptional text comprehension capabilities, has triggered a global wave of AI popularization and application in seeking answers through human‒machine dialogues. Gastric cancer, as a globally prevalent disease, has a five-year survival rate of up to 90% when detected early and treated promptly. This research aims to explore ChatGPT's potential in disseminating gastric cancer knowledge, providing consultation recommendations, and interpreting endoscopy reports. Through experimentation, the GPT-4 model of ChatGPT achieved an appropriateness of 91.3% and a consistency of 95.7% in a gastric cancer knowledge test. Furthermore, GPT-4 has demonstrated considerable potential in consultation recommendations and endoscopy report analysis.

Referencias bibliográficas

  • Y. Horie et al., “Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks,” Gastrointestinal Endoscopy, vol. 89, no. 1, pp. 25–32, 2019, doi: 10.1016/j.gie.2018.07.037.
  • A. S. Kumarasuvamy and R. S. Rajendran, “Design an Early Detection and Classification for Diabetic Retinopathy by Deep Feature Extraction based Convolution Neural Network,” Journal of Trends in Computer Science and Smart Technology, vol. 3, no. 2, pp. 81–94, 2021, doi: 10.36548/ jtcsst.2021.2.002.
  • T. Rahman et al., “Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray,” Applied Sciences, vol. 10, no. 9, Art. no. 9, 2020, doi: 10.3390/app10093233.
  • A. Laishram and K. Thongam, “Automatic Classification of Oral Pathologies Using Orthopantomogram Radiography Images Based on Convolutional Neural Network,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 7, no. 4, pp. 69-77, 2022, doi: 10.9781/ijimai.2021.10.009.
  • M. A. Khemchandani, S. M. Jadhav, and B. R. Iyer, “Brain Tumor Segmentation and Identification Using Particle Imperialist Deep Convolutional Neural Network in MRI Images,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 7, no. 7, pp. 38-47, 2022, doi: 10.9781/ijimai.2022.10.006.
  • B. G. Patra et al., “Extracting social determinants of health from electronic health records using natural language processing: a systematic review,” Journal of the American Medical Informatics Association, vol. 28, no. 12, pp. 2716–2727, 2021, doi: 10.1093/jamia/ocab170.
  • H. Sung et al., “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” CA: A Cancer Journal for Clinicians, vol. 71, no. 3, pp. 209–249, 2021, doi: 10.3322/caac.21660.
  • E. C. Smyth, M. Nilsson, H. I. Grabsch, N. C. van Grieken, and F. Lordick, “Gastric cancer,” The Lancet, vol. 396, no. 10251, pp. 635–648, 2020, doi: 10.1016/S0140-6736(20)31288-5.
  • R. L. Siegel, K. D. Miller, H. E. Fuchs, and A. Jemal, “Cancer statistics, 2022,” CA A Cancer J Clinicians, vol. 72, no. 1, pp. 7–33, 2022, doi: 10.3322/ caac.21708.
  • K. Yashima, M. Shabana, H. Kurumi, K. Kawaguchi, and H. Isomoto, “Gastric Cancer Screening in Japan: A Narrative Review,” Journal of Clinical Medicine, vol. 11, no. 15, Art. no. 15, 2022, doi: 10.3390/ jcm11154337.
  • A. Jaroenlapnopparat, K. Bhatia, and S. Coban, “Inflammation and Gastric Cancer,” Diseases, vol. 10, no. 3, Art. no. 3, 2022, doi: 10.3390/ diseases10030035.
  • S. Kikuchi, Y. Obata, T. Sasakabe, S. Kawai, C. Wang, and Y. Lin, “Relative risk of gastric cancer between those with and without Helicobacter pylori infection history in Japan,” JGH Open, vol. 6, no. 3, pp. 166–170, 2022, doi: 10.1002/jgh3.12714.
  • H. L. Haver, E. B. Ambinder, M. Bahl, E. T. Oluyemi, J. Jeudy, and P. H. Yi, “Appropriateness of Breast Cancer Prevention and Screening Recommendations Provided by ChatGPT,” Radiology, p. 230424, 2023, doi: 10.1148/radiol.230424.
  • T. H. Kung et al., “Performance of ChatGPT on USMLE: Potential for AIassisted medical education using large language models,” PLOS Digital Health, vol. 2, no. 2, p. e0000198, 2023, doi: 10.1371/journal.pdig.0000198.
  • R. Vaishya, A. Misra, and A. Vaish, “ChatGPT: Is this version good for healthcare and research?,” Diabetes & Metabolic Syndrome: Clinical Research & Reviews, vol. 17, no. 4, p. 102744, 2023, doi: 10.1016/j. dsx.2023.102744.
  • S. Biswas, “ChatGPT and the Future of Medical Writing,” Radiology, vol. 307, no. 2, p. e223312, 2023, doi: 10.1148/radiol.223312.
  • M. Sallam, “ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns,” Healthcare, vol. 11, no. 6, Art. no. 6, 2023, doi: 10.3390/ healthcare11060887.
  • E. Kasneci et al., “ChatGPT for good? On opportunities and challenges of large language models for education,” Learning and Individual Differences, vol. 103, p. 102274, 2023, doi: 10.1016/j.lindif.2023.102274.
  • P.-H. Niu, L.-L. Zhao, H.-L. Wu, D.-B. Zhao, and Y.-T. Chen, “Artificial intelligence in gastric cancer: Application and future perspectives,” World J Gastroenterol, vol. 26, no. 36, pp. 5408–5419, 2020, doi: 10.3748/wjg.v26. i36.5408.
  • Z. X. Dong et al., “Endoscopic diagnosis and treatment for gastric adenocarcinoma of fundic gland type: report of 3 cases”, Chin J Dig Endosc, vol. 39, no. 11, pp.931-934, 2022, doi: 10.3760/ cma.j.cn321463-20210325-00206
  • H. Y. Dong, X. Y. Jia, Q. P. Pang, G. D. Li, “A case of simultaneous fiveorigin early gastric cancer treated by gastroscopy”, Chin J Dig Endosc, vol. 34, no. 12, pp. 913-914, 2017, doi: 10.3760/cma.j.issn.1007-5232.2017.12.018
  • H. C. Huang et al.,” Zhonghua Nei Ke Za Zhi, vol. 61, no. 6, pp. 685–687, 2022, doi: 10.3760/ cma.j.cn112138-20210714-00482.
  • S. Kamran, M. K. Dilling, N. A. Parker, J. Alderson, N. D. Tofteland, and Q. V. Truong, “Case Report: Simultaneously, diagnosed gastric adenocarcinoma and pernicious anemia – a classic association.” F1000Research, 2020. doi: 10.12688/f1000research.24353.2.
  • M. Moriya, A. Uehara, T. Okumura, M. Miyamoto, and Y. Kohgo, “Stressinduced hemorrhagic gastric ulcer after successful Helicobacter pylori eradication: two case reports,” Journal of Medical Case Reports, vol. 5, no. 1, p. 252, 2011, doi: 10.1186/1752-1947-5-252.
  • K. Barrett, M. W. Hii, and R. J. Cade, “Benign gastro-colic fistula in a woman presenting with weight loss and intermittent vomiting: a case report,” Journal of Medical Case Reports, vol. 5, no. 1, p. 313, 2011, doi: 10.1186/1752-1947-5-313.
  • I. B. Ismail, H. Zenaidi, R. Jouini, S. Rebii, and A. Zoghlami, “Case Report: Primary pure clear cell gastric carcinoma.” F1000Research, 2020. doi: 10.12688/f1000research.25810.1.
  • F. C. Kitamura, “ChatGPT Is Shaping the Future of Medical Writing However, Still Requires Human Judgment,” Radiology, vol. 307, no. 2, p. e230171, 2023, doi: 10.1148/radiol.230171.