ML device output by type
| Output type | Devices | Description |
| Quantification | 13 | Quantification of information derived from the images, such as, cardiac function and blood flow,23 30 32 33 37 or volume of structures including the brain,31 35 36 70 and prostate.34 |
| Triage notifications | 10 | Triage notification alert clinicians to cases with suspected positive findings.25 40–48 |
| Case-level finding of disease | 6 | Case level finding of disease such as, wrist fractures,52 diabetic retinopathy,49 osteoarthritis,51 heart murmurs24 50 and cardiac arrhythmias.24 53 |
| Identify features of disease | 6 | Identify features of disease thereby drawing clinician attention to them, such prompting breast55–57 59 or lung58 cancers on images or cardiac arrythmias on ECG tracings.54 |
| Clinical grading or scoring | 5 | Clinical grading or scoring (n=5) on standardised clinical assessment instruments, such as BI-RADS,60 63 64 LI-RADS,62 lung-RADS,62 or Agatston-equivalent scores.61 |
| Enhanced images | 4 | Enhanced images with reduced noise and improved image quality.26–29 |
| Automatic coding of features or events | 2 | Automatic coding of features or events in the data, such as sleep stages and respiratory events in polysomnography data,39 or colour coding structures in optical coherence tomography.38 |
| Automatic control of electronic or mechanical devices | 2 | Automatic control of electronic or mechanical devices, such as fluoroscope collimator67 and automatic recording of ultrasound clips dependent on detected image quality.66 |
| Treatment recommendations | 1 | Treatment recommendations, such as adjustments to insulin pump dose ratios.87 |
BI-RADS, Breast Imaging-Reporting and Data System; LI-RADS, Liver Imaging-Reporting and Data System; ML, machine learning.