Table 1 MAIC-10 checklist to assess quality of AI-based medical imaging research studies
From: MAIC–10 brief quality checklist for publications using artificial intelligence and medical images
Checklist item | Article section | Description | Reported |
|---|---|---|---|
1. Clinical need | Introduction | The study is clearly put into context by describing the target clinical problem and any previous approaches in the literature | □ |
2. Study design | Materials and methods | The type of study (observational/interventional, single/multicentre) and inclusion/exclusion criteria are explicitly described, and a sample size estimate is given | □ |
3. Safety and privacy | Materials and methods | ELSI (ethical, legal, social implications), specifically including ethics committee approval and data de-identification issues, are discussed | □ |
4. Data curation | Materials and methods | Data extraction, cleaning, and transformation methods, including image pre-processing steps, are clearly described | □ |
5. Data annotation | Materials and methods | The ground truth reference is defined and the annotation process, including measures of inter/intra-observer variability, is described | □ |
6. Data partitioning | Materials and methods | Methods and criteria for data set splitting into train-tune-test-validation sets are indicated | □ |
7. AI model | Materials and methods, results | The AI model building methodology is sufficiently detailed by including used technologies (software and hardware), training–tuning–testing methods, performance metrics, and resulting AI model architecture | □ |
8. Robustness | Results, discussion | The generalizability of the AI model in real-world conditions is explicitly discussed | □ |
9. Explainability | Discussion | The interpretability of the model (including the use of uncertainty or confidence metrics) is explicitly discussed | □ |
10.Transparency | Discussion | Any possibility of access to original data sets and source code is clearly stated. Financing and conflicts of interest are detailed | □ |
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