Converting Performance Difference to Outcome Difference
|Input||Example: Comparison of AI-assisted diagnosis (intervention) and conventional diagnosis (control)|
|ΔSensitivity||%||Sensitivity of AI-assisted diagnosis - Sensitivity of conventional diagnosis, for example, 5.0%|
|ΔSpecificity||%||Specificity of AI-assisted diagnosis - Specificity of conventional diagnosis, for example, -0.5%|
|Disease prevalence||% to %||For example, 5 to 8%.
In case of a single value instead of a range, put in the same value for both boxes, for example, 5 to 5%.
|FP-to-TP outcome ratio||to||
The ratio between the absolute amounts of net loss in patient outcome
incurred by an FP
instead of leaving the
patient alone and net outcome gain provided by a TP decision compared
with neglecting the
disease in the patient, for
example, 0.05 to 0.1.
In case of a single value instead of a range, put in the same value for both boxes, for example, 0.1 to 0.1.
Taking surveillance of hepatocellular carcinoma as an example, the ratio is A/B as shown below.
A: Absolute amount of net outcome loss in a single FP patient = (negative consequences of unnecessary follow-up imaging tests, such as adverse effects from contrast agents, radiation exposure, and waste of time and money) + (harm from any invasive procedures that may follow the FP decision, such as liver biopsy) + (harm from unnecessary treatments) + (emotional distress) - (a slim theoretical benefit, such as an incidental detection of unrelated significant diseases on the follow-up tests)
B: Absolute amount of net outcome gain in a single TP patient = (earlier diagnosis of the tumor, which may improve patient survival and allow for less invasive treatments with less treatment-related harm compared with later-stage diagnoses) - (small potential harm, such as adverse effects from treatments)