Table 5 Studies comparing artificial intelligence algorithms versus (or combined with) human reader, organised by publication date
From: Artificial intelligence for radiological paediatric fracture assessment: a systematic review
Author, year | Human/AI | Accuracy, % (95% CI) | Sensitivity, % (95% CI) | Specificity, % (95% CI) | TP | FP | FN | TN |
|---|---|---|---|---|---|---|---|---|
England [31] | AI | 0.907 (0.843–0.951) | 0.909 (0.788–1.000) | 0.906 (0.844–0.958) | 87 | 9 | 3 | 30 |
PGY5 emergency medicine trainee (non-radiologist) | 0.915 (0.852–0.957) | 0.848 (0.681–0.949) | 0.938 (0.869–0.977) | 90 | 6 | 5 | 28 | |
Choi, [17] | AI (Geographical test set) | 0.895 (0.817–0.942) | 1.000 (0.852–1.000) | 0.861 (0.759–0.931) | 23 | 10 | 0 | 62 |
Summated score of three radiologists (2–7-year experience) from different institution to test dataset | 0.975 (0.950–0.988) | 0.957 (0.880–0.985) | 0.981 (0.953–0.993) | 66 | 4 | 3 | 212 | |
Lowest performing radiologist alone | NS (AUC 0.977 (0.924–0.997)) | 0.957 (0.781–0.999) | 0.972 (0.903–0.997) | NS | NS | NS | NS | |
Lowest performing radiologist with AI assistance | NS (AUC 0.993 (0.949–1.000)) | 1.000 (0.852–1.000) | 0.972 (0.903–0.997) | NS | NS | NS | NS | |
Zhang [35] | AI (Test set—data undefined) | 0.920 | 1.000 | 0.870 | NS | NS | NS | NS |
Human: paediatric musculoskeletal radiologist | 0.89 (0.782–0.949) | 1.000 (0.833–1.000) | 0.833 (0.681–0.921) | 19 | 6 | 0 | 30 |