Diagnostic Machine Learning Models for Acute Abdominal Pain: Towards an e-Learning Tool for Medical Students
Abstract
Computer-aided learning systems (e-learning systems) can help medical students gain more experience with diagnostic reasoning and decision making. Within this context, providing feedback that matches students' needs (i.e. personalised feedback) is both critical and challenging. In this paper, we describe the development of a machine learning model to support medical students' diagnostic decisions. Machine learning models were trained on 208 clinical cases presenting with abdominal pain, to predict five diagnoses. We assessed which of these models are likely to be most effective for use in an e-learning tool that allows students to interact with a virtual patient. The broader goal is to utilise these models to generate personalised feedback based on the specific patient information requested by students and their active diagnostic hypotheses.
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Students in AI-integrated healthcare systems, such as those in the United States and Germany, benefit from structured exposure to AI tools, including ChatGPT-based simulations and clinical decision-support systems, which fosters greater confidence in AI applications.
“In contrast, students in AI-integrated healthcare systems, such as those in the United States and Germany, benefit from structured exposure to AI tools, including ChatGPT-based simulations and clinical decision-support systems, which fosters greater confidence in AI applications [10].”
From Knowledge, attitudes, and practices toward AI technology (ChatGPT) among nursing students at Palestinian universities (2025) · cites this paper as [10]
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- Authors
- Piyapong, K., Anna, R., Terry, J., & Karin, V.
- Affiliations
- The University of Melbourne (AU)
- Venue
- Studies in Health Technology and Informatics
- Publisher
- IOS Press
- Published
- 2017
- Volume
- 245
- Pages
- 447-451
- ISSN
- 0926-9630 (ISSN-L)
- OpenAlex
- W2787181260
- Cited by
- 31
- References
- 0
- Citations by year
- 2025: 6 · 2024: 6 · 2023: 2 · 2022: 5 · 2021: 4 · 2020: 3 · 2019: 2 · 2018: 3
- FWCI
- 4.30
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Studies in health technology and informatics (publishedVersion)
- PubMed (publishedVersion)
- RMIT Research Repository (RMIT University Library) (submittedVersion)
- Figshare (submittedVersion)
- PubMed
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- acute abdominal pain, computer-aided diagnosis, online learning, medical students, diagnostic reasoning, personalized feedback, virtual patients, clinical decision support, diagnostic hypothesis
- MeSH terms
- Machine Learning, Clinical Competence, Decision Making, Education, Medical, Undergraduate, Feedback, Humans, Learning, Students, Medical, Abdominal Pain
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