Developing a transparent reporting tool for AI-based diagnostic prediction models of disease and syndrome in Chinese medicine: a Delphi protocol.

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.