To assess the impact of automated algorithms on the trainees’ decision-making capacity and confidence for individualized surgical planning.
At Chinese PLA General Hospital, trainees were enrolled to undergo decision-making capacity and confidence training through three alternative visual tasks of the inferior clivus model formed from an automated algorithm and given consecutively in three exemplars. The rationale of automated decision-making was used to instruct each trainee.
Following automated decision-making calculation in 50 skull base models, we screened out three optimal plans, infra-tubercle approach (ITA), trans-tubercle approach (TTA), and supra-tubercle approach (STA) for 41 (82.00%), 8 (16.00%), and 1 (2.00%) subject, respectively. From September 1, 2023, through November 17, 2023, 62 trainees (median age [range]: 27 [26–28]; 28 [45.16%] female; 25 [40.32%] neurosurgeons) made a decision among the three plans for the three typical models (ITA, TTA, and STA exemplars). The confidence ratings had fine test-retest reliability (Spearman's rho: 0.979; 95% CI: 0.970 to 0.988) and criterion validity with time spent (Spearman's rho: −0.954; 95%CI: −0.963 to −0.945). Following instruction of automated decision-making, time spent (initial test: 24.02 vs. 7.13 in ITA; 30.24 vs. 7.06 in TTA; 34.21 vs. 12.82 in STA) and total hits (initial test: 30 vs. 16 in ITA; 37 vs. 17 in TTA; 42 vs. 28 in STA) reduced significantly; confidence ratings (initial test: 2 vs. 4 in ITA; 2 vs. 4 in TTA; 1 vs. 3 in STA) increased correspondingly. Statistically significant differences (
The education tool generated by automated decision-making considers surgical freedom and injury risk for the individualized risk-benefit assessment, which may provide explicit information to increase trainees’ decision-making capacity and confidence.