{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T19:08:26Z","timestamp":1781291306145,"version":"3.54.1"},"reference-count":22,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: Acute pancreatitis (AP) is an illness that requires prompt diagnosis and treatment since it has the potential to become life-threatening. The American College of Gastroenterology 2024 (ACG24) guidelines offer a framework for diagnosis, severity, and treatment criteria. To assess Google Gemini application of ACG24 guidelines to Medical Information Mart for Intensive Care-III AP cases for risk, nutrition, and complication management. Methods: This observational cross-sectional study was based on 512 patients with AP who were treated in the Medical Information Mart for Intensive Care-III database from 2001 to 2012. The study compared the efficiency of Gemini in relation to the ACG24 guidelines in the three main areas of risk stratification, enteral nutrition timing, and necrotizing pancreatitis management. Enteral nutrition, according to the ACG24 guidelines, should be started within 48 h for patients who are capable, and antibiotics should only be used for confirmed infected necrosis. Results: The study included 512 patients who were divided into two groups: 213 patients with mild pancreatitis (41.6%) and 299 patients with severe pancreatitis (58.4%). The model achieved 85% accuracy for mild cases and 82% accuracy for severe cases of pancreatitis. The Acute Physiology and Chronic Health Evaluation II and Ranson scores matched the predictions of Gemini for both mild cases (p = 0.28 and p = 0.33, respectively) and severe cases (p = 0.31 and p = 0.27, respectively). The recommendations for early enteral nutrition and delayed feeding in mild cases were correct for 78% of patients, but the system suggested oral intake prematurely in 8% of severe cases. The antibiotic guideline compliance reached 82% among 156 patients with necrotizing pancreatitis, and the procedure for draining infected necrosis was correct 85% of the time. Conclusions: The Gemini model achieved 78\u201385% accuracy in determining pancreatitis severity and adherence to treatment guidelines but showed lower accuracy in nutrition timing compared to other parameters. Core Tip: This study evaluated the Google Gemini model in applying the American College of Gastroenterology 2024 guidelines for acute pancreatitis across 512 Medical Information Mart for Intensive Care-III cases. Results demonstrated 85% accuracy in severity classification, precise prediction of Acute Physiology and Chronic Health Evaluation II and Ranson scores, and 78\u201385% compliance with nutritional and necrotizing pancreatitis management guidelines. These findings suggest that artificial intelligence-based clinical decision support systems can provide rapid, consistent, and guideline-concordant recommendations, which are particularly valuable in settings with limited specialist expertise.<\/jats:p>","DOI":"10.3390\/jcm14124347","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T09:36:00Z","timestamp":1750239360000},"page":"4347","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Evaluating the Role of Artificial Intelligence in Making Clinical Decisions for Treating Acute Pancreatitis"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2321-2642","authenticated-orcid":false,"given":"Mete","family":"Ucdal","sequence":"first","affiliation":[{"name":"Division of Internal Medicine, Faculty of Medicine, Hacettepe University, Ankara 06800, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9033-2475","authenticated-orcid":false,"given":"Amir","family":"Bakhshandehpour","sequence":"additional","affiliation":[{"name":"Division of Internal Medicine, Faculty of Medicine, Hacettepe University, Ankara 06800, T\u00fcrkiye"},{"name":"Faculty of Medicine, Hacettepe University, Ankara 06800, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammed Bahaddin","family":"Durak","sequence":"additional","affiliation":[{"name":"Division of Internal Medicine, Faculty of Medicine, Hacettepe University, Ankara 06800, T\u00fcrkiye"},{"name":"Division of Gastroenterology, Faculty of Medicine, Hacettepe University, Ankara 06800, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0901-9192","authenticated-orcid":false,"given":"Yasemin","family":"Balaban","sequence":"additional","affiliation":[{"name":"Division of Internal Medicine, Faculty of Medicine, Hacettepe University, Ankara 06800, T\u00fcrkiye"},{"name":"Division of Gastroenterology, Faculty of Medicine, Hacettepe University, Ankara 06800, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Murat","family":"Kekilli","sequence":"additional","affiliation":[{"name":"Division of Internal Medicine, Faculty of Medicine, Hacettepe University, Ankara 06800, T\u00fcrkiye"},{"name":"Division of Gastroenterology, Faculty of Medicine, Gazi University, Ankara 06800, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cem","family":"Simsek","sequence":"additional","affiliation":[{"name":"Division of Internal Medicine, Faculty of Medicine, Hacettepe University, Ankara 06800, T\u00fcrkiye"},{"name":"Division of Gastroenterology, Faculty of Medicine, Hacettepe University, Ankara 06800, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1053\/j.gastro.2018.08.063","article-title":"Burden and Cost of Gastrointestinal, Liver, and Pancreatic Diseases in the United States: Update 2018","volume":"156","author":"Peery","year":"2019","journal-title":"Gastroenterology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"419","DOI":"10.14309\/ajg.0000000000002645","article-title":"American College of Gastroenterology Guidelines: Management of Acute Pancreatitis","volume":"119","author":"Tenner","year":"2024","journal-title":"Am. J. Gastroenterol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bekbolatova, M., Mayer, J., Ong, C.W., and Toma, M. (2024). Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives. Healthcare, 12.","DOI":"10.3390\/healthcare12020125"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yuan, L., Ji, M., Wang, S., Wen, X., Huang, P., Shen, L., and Xu, J. (2022). Machine learning model identifies aggressive acute pancreatitis within 48 h of admission: A large retrospective study. BMC Med. Inform. Decis. Mak., 22.","DOI":"10.1186\/s12911-022-02066-3"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s13244-024-01887-2","article-title":"Integrating CT-based radiomics and clinical features to better predict the prognosis of acute pancreatitis","volume":"16","author":"Chen","year":"2025","journal-title":"Insights Imaging"},{"key":"ref_6","first-page":"101092","article-title":"Exploring the performance of large language models on hepatitis B infection-related questions: A comparative study","volume":"31","author":"Li","year":"2025","journal-title":"World J. Gastroenterol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"160035","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"Johnson","year":"2016","journal-title":"Sci. Data"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5268","DOI":"10.3748\/wjg.v29.i37.5268","article-title":"Acute pancreatitis: A review of diagnosis, severity prediction and prognosis assessment from imaging technology, scoring system and artificial intelligence","volume":"29","author":"Hu","year":"2023","journal-title":"World J. Gastroenterol."},{"key":"ref_9","unstructured":"Basit, H., Ruan, G.J., and Mukherjee, S. (2025). Ranson Criteria. StatPearls, StatPearls Publishing LLC."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tan, Z., Li, G., Zheng, Y., Li, Q., Cai, W., Tu, J., and Jin, S. (2025). Advances in the clinical application of machine learning in acute pancreatitis: A review. Front. Med., 11.","DOI":"10.3389\/fmed.2024.1487271"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kiss, S., Pint\u00e9r, J., Molontay, R., Nagy, M., Farkas, N., Sipos, Z., Feh\u00e9rv\u00e1ri, P., Pecze, L., F\u00f6ldi, M., and Vincze, \u00c1. (2022). Early prediction of acute necrotizing pancreatitis by artificial intelligence: A prospective cohort-analysis of 2387 cases. Sci. Rep., 12.","DOI":"10.1016\/j.pan.2022.06.064"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kung, T.H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepa\u00f1o, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., and Maningo, J. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLoS Digit. Health, 2.","DOI":"10.1371\/journal.pdig.0000198"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Brin, D., Sorin, V., Vaid, A., Soroush, A., Glicksberg, B.S., Charney, A.W., Nadkarni, G., and Klang, E. (2023). Comparing ChatGPT and GPT-4 performance in USMLE soft skill assessments. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-43436-9"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e63430","DOI":"10.2196\/63430","article-title":"ChatGPT-4 Omni Performance in USMLE Disciplines and Clinical Skills: Comparative Analysis","volume":"10","author":"Bicknell","year":"2024","journal-title":"JMIR Med. Educ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1038\/s41591-024-03423-7","article-title":"Toward expert-level medical question answering with large language models","volume":"31","author":"Singhal","year":"2025","journal-title":"Nat. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","article-title":"Large language models encode clinical knowledge","volume":"620","author":"Singhal","year":"2023","journal-title":"Nature"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cross, J.L., Choma, M.A., and Onofrey, J.A. (2024). Bias in medical AI: Implications for clinical decision-making. PLoS Digit. Health, 3.","DOI":"10.1371\/journal.pdig.0000651"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/s13000-024-01464-7","article-title":"Challenges and barriers of using large language models (LLM) such as ChatGPT for diagnostic medicine with a focus on digital pathology\u2014A recent scoping review","volume":"19","author":"Ullah","year":"2024","journal-title":"Diagn. Pathol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hameed, M.A.B., and Alamgir, Z. (2022). Improving mortality prediction in Acute Pancreatitis by machine learning and data augmentation. Comput. Biol. Med., 150.","DOI":"10.1016\/j.compbiomed.2022.106077"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1186\/s12967-024-05302-8","article-title":"Exploring the performance of ChatGPT on acute pancreatitis-related questions","volume":"22","author":"Du","year":"2024","journal-title":"J. Transl. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e271","DOI":"10.1016\/S2589-7500(19)30123-2","article-title":"A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis","volume":"1","author":"Liu","year":"2019","journal-title":"Lancet Digit. Health"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.cmi.2017.02.028","article-title":"A systematic review of clinical decision support systems for antimicrobial management: Are we failing to investigate these interventions appropriately?","volume":"23","author":"Rawson","year":"2017","journal-title":"Clin. Microbiol. Infect."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/12\/4347\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:54:19Z","timestamp":1760032459000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/12\/4347"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,18]]},"references-count":22,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["jcm14124347"],"URL":"https:\/\/doi.org\/10.3390\/jcm14124347","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,18]]}}}