Table 3.

Quality Assessment Guidelines

Article Sections	Parameters	Explanation
Title and Abstract	Title (Nature of study)	Introduce predictive model
	Abstract (Structured summary)	Include background, objectives, data sources, performance metrics of predictive models and conclusion about model value
Introduction	Rationale	Define the clinical goal, and review the current practice and performance of existing models
	Objectives	Identify how the proposed method can benefit the clinical target
Method	Describe the setting	Describe the data source, sample size, year and duration of the data
	Define the prediction problem	Define the nature of the study (retrospective/prospective), model function (prognosis, diagnosis, etc.) and performance metrics
	Prepare data for model building	Describe the inclusion and exclusion criteria of the data, data pre-processing method, performance metrics for validation, and define the training and testing set. External validation is recommended
	Build the predictive model	Describe how the model was built including AI modelling techniques used (eg random forest, ANN, CNN)
Results	Report the final model and performance	Reports the performance of the final proposed model, comparison with other models and human performance. It is recommended to include confidence intervals
Discussion	Clinical implications	Discuss any significant findings
	Limitations of the model	Discuss any possible limitations found
Conclusion		Discuss the clinical benefit of the model and summarize the result and findings

Note: Data from the guideline of Luo et al.⁸