Context Medical image perception training generally focuses on abnormalities, whereas normal images are more prevalent in medical practice. Furthermore, instructional sequences that let students practice prior to expert instruction (inductive) may lead to improved performance compared with methods that give students expert instruction before practice (deductive). This study investigates the effects of the proportion of normal images and practice–instruction order on learning to interpret medical images. It is hypothesised that manipulation of the proportion of normal images will lead to a sensitivity–specificity trade‐off and that students in practice‐first (inductive) conditons need more time per practice case but will correctly identify more test cases. Methods Third‐year medical students (n = 103) learned radiograph interpretation by practising cases with, respectively, 30% or 70% normal radiographs prior to expert instruction (practice‐first order) or after expert instruction (instruction‐first order). After training, students performed a test (60% normal) and sensitivity (% of correctly identified abnormal radiographs), specificity (% of correctly identified normal radiographs), diagnostic performance (% of correct diagnoses) and case duration were measured. Results The conditions with 30% of normal images scored higher on sensitivity but the conditions with 70% of normal images scored higher on specificity, indicating a sensitivity and specificity trade‐off. Those who participated in inductive conditions took less time per practice case but more per test case. They had similar test sensitivity, but scored lower on test specificity. Conclusions The proportion of normal images impacted the sensitivity–specificity trade‐off. This trade‐off should be an important consideration for the alignment of training with future practice. Furthermore, the deductive conditions unexpectedly scored higher on specificity when participants took less time per case. An inductive approach did not lead to higher diagnostic performance, possibly because participants might already have relevant prior knowledge. Deductive approaches are therefore advised for the training of advanced learners.