⚡ Quick Summary
This study introduces the Transparent Reporting Tool for AI-based Diagnostic Prediction Models of Disease and Syndrome in Chinese Medicine (TRAPODS-CM), aimed at enhancing the transparency and reproducibility of research in Chinese Medicine (CM). Utilizing a multi-phase Delphi protocol, the project seeks to establish standardized reporting guidelines tailored specifically for CM.
🔍 Key Details
- 📊 Focus: AI-based diagnostic prediction models in Chinese Medicine
- 🧩 Methodology: Delphi protocol involving expert consensus
- ⚙️ Core Group: Multidisciplinary experts in CM, computer science, and evidence-based methodology
- 🏆 Ethical Approval: National Natural Science Foundation of China and Nanyang Technological University
🔑 Key Takeaways
- 📈 Rapid Growth: The application of AI in CM diagnostics is expanding significantly.
- 💡 Need for Standards: Existing guidelines are primarily focused on Western medicine, highlighting a gap for CM.
- 👥 Expert Involvement: A diverse panel of experts will contribute to the development of the TRAPODS-CM checklist.
- 🗂️ Comprehensive Review: Initial item pool will be based on a thorough review of existing studies.
- 🌐 Dissemination: The finalized checklist will be shared through various multimedia platforms and academic events.
- 🔍 Focus on Transparency: The tool aims to improve the transparency and reproducibility of CM research findings.
- 📅 Future Research: The study’s findings will be published in peer-reviewed journals to reach a wider audience.
📚 Background
The integration of artificial intelligence in healthcare has been transformative, particularly in diagnostic prediction models. However, the unique theoretical frameworks and terminologies of Chinese Medicine necessitate the development of specialized reporting tools. This study addresses the critical need for a standardized approach to enhance the quality and applicability of research in this field.
🗒️ Study
The research employs a structured, multi-phase Delphi protocol to gather expert opinions and achieve consensus on the TRAPODS-CM checklist. The core working group will initiate the process by reviewing published studies on CM diagnostic prediction models, creating an initial item pool that reflects the unique aspects of CM.
📈 Results
The study anticipates that the Delphi process will yield a comprehensive checklist that reflects expert consensus on the essential elements of reporting AI-based diagnostic models in CM. This checklist is expected to significantly enhance the transparency and reproducibility of research findings, ultimately benefiting clinical practice and research integrity.
🌍 Impact and Implications
The establishment of the TRAPODS-CM checklist could have profound implications for the field of Chinese Medicine. By providing a standardized reporting tool, researchers can ensure that their findings are more accessible and applicable in clinical settings. This initiative not only promotes rigorous scientific standards but also fosters greater collaboration between traditional and modern medical practices.
🔮 Conclusion
The development of the TRAPODS-CM represents a significant step forward in the integration of AI within Chinese Medicine. By addressing the unique challenges of reporting in this field, the study aims to enhance the quality and impact of research, paving the way for future advancements in diagnostic prediction models. The future of CM research looks promising, and we encourage ongoing exploration and dialogue in this exciting area!
💬 Your comments
What are your thoughts on the development of standardized reporting tools in Chinese Medicine? We would love to hear your insights! 💬 Join the conversation in the comments below or connect with us on social media:
Developing a transparent reporting tool for AI-based diagnostic prediction models of disease and syndrome in Chinese medicine: a Delphi protocol.
Abstract
INTRODUCTION: The application of artificial intelligence in diagnostic prediction models for diseases and syndromes in Chinese Medicine (CM) has been rapidly expanding, accompanied by a significant increase in related research publications. However, existing reporting guidelines for diagnostic prediction models are primarily tailored to Western medicine, which differs fundamentally from CM in its theoretical framework, terminology, and classification systems. To address this gap, it is essential to establish a transparent and standardized reporting tool specifically designed for CM diagnostic and syndrome prediction models. This will enhance the transparency, reproducibility, and clinical relevance of research findings in this emerging field.
METHODS: This study adopts a structured, multi-phase Delphi protocol. A core working group will first conduct a comprehensive review of published studies on CM diagnostic prediction models to develop an initial item pool for the Transparent Reporting Tool for AI-based Diagnostic Prediction Models of Disease and Syndrome in Chinese Medicine (TRAPODS-CM). Delphi questionnaires will then be distributed via email to a multidisciplinary panel of experts in CM, computer science, and evidence-based methodology who meet the inclusion criteria. The number of Delphi rounds will be determined by evaluating the active coefficient, expert authority, and expert consensus. Final consensus on the TRAPODS-CM checklist will be achieved through online meetings. The study will be governed by a Steering Committee, with the core working group responsible for implementation. After publication, the finalized checklist will be disseminated via multimedia platforms, seminars, and academic conferences to maximize its academic and clinical impact.
ETHICS AND DISSEMINATION: This project has received ethical approval from the National Natural Science Foundation of China (Grant No. 82374336) and the Institutional Review Board of Nanyang Technological University (IRB-2024-1007). The study findings will be disseminated through peer-reviewed publications.
Author: [‘Li J’, ‘Seetoh WS’, ‘Lim J’, ‘Xiao X’, ‘Yang K’, ‘Yeo SY’, ‘Sun B’, ‘Liu J’, ‘Xu Z’, ‘Zhong LLD’]
Journal: Front Digit Health
Citation: Li J, et al. Developing a transparent reporting tool for AI-based diagnostic prediction models of disease and syndrome in Chinese medicine: a Delphi protocol. Developing a transparent reporting tool for AI-based diagnostic prediction models of disease and syndrome in Chinese medicine: a Delphi protocol. 2025; 7:1575320. doi: 10.3389/fdgth.2025.1575320