Table 1: Six-step decision algorithm for evaluating AI tools in spine imaging for conservative care. Practitioners apply steps sequentially; deficiencies at any stage warrant caution or rejection.
|
Step |
Critical Question |
Evidence Required to Proceed |
|
1 |
Is the tool technically valid for my population? |
External validation in ≥ 1 independent dataset; performance metrics stratified by age, sex, severity; evaluation in community practice settings; imaging protocol compatibility documented |
|
2 |
Does it improve patient outcomes relevant to conservative care? |
Evidence linking AI outputs to functional outcomes (pain, disability); impact on treatment decisions documented; evaluation in non-surgical populations; relationship between detected findings and clinical presentation established |
|
3 |
Will it reduce or exacerbate health disparities? |
Training data demographic composition reported; performance analyzed across race/ethnicity, socioeconomic status; validation in safety-net/community settings; accessibility barriers identified and addressed |
|
4 |
Does it support or replace clinical reasoning? |
Explainability features present; positioned as decision support not autonomous diagnosis; practitioner override mechanisms clear; training resources available; automation bias risks assessed |
|
5 |
Are safety governance mechanisms adequate? |
Regulatory clearance appropriate for intended use; post-market surveillance plans documented; adverse event reporting mechanisms exist; version control and update procedures clear; liability framework defined |
|
6 |
Does it support patient-centered care? |
Patient communication supports available; informed consent processes defined; integration with shared decision-making demonstrated; psychological impacts (anxiety, medicalization) considered |