From subtle breaks to missed diagnoses: Real-world evaluation of an artificial intelligence fracture detection tool

Table 1 Reasons for false positives and false negatives, n (%)

Category	False positives	False negatives	Total errors
Old/healed fracture/variant	4 (6.9)	3 (5.6)	7 (6.2)
Subtle/minimally displaced fracture	1 (1.7)	50 (92.6)	51 (45.5)
Overlapping and projection artifact	0 (0.0)	1 (1.9)	1 (0.9)
Growth plate/degenerative changes/prosthesis	53 (91.4)	0 (0.0)	53 (47.3)
Total	58 (100)	54 (100)	112 (100)

Table 2 Clinical outcomes of false positive and false negative cases, n (%)

Outcome category	False negatives	False positives	Total
Outpatient/fracture clinic follow-up	33 (61.1)	31 (53.4)	64 (57.1)
CT/MRI performed	8 (14.8)	8 (13.8)	16 (14.3)
ED discharge/conservative management	10 (18.5)	9 (15.5)	19 (17.0)
Admitted	3 (5.6)	6 (10.3)	9 (8.0)
Total	54 (100)	58 (100)	112 (100)

CT: Computed tomography; MRI: Magnetic resonance imaging; ED: Emergency department.