OpenEvidence performs at similar levels compared to current and previous GPT models on orthopedic training and education questions

Table 2 Image-based question characteristics by language model and response accuracy (n = 46), n (%)

	GPT-4		GPT-5		OE		OE (described)
	Correct	P value1	Correct	P value1	Correct	P value1	Correct	P value1
Total correct	31 (67)		36 (78)		26 (57)		37 (80)
Specialty		0.3		0.3		0.5		0.2
Basic science	2 (100)		2 (100)		2 (100)		2 (100)
Foot and ankle	1 (25)		2 (50)		1 (25)		2 (50)
Hand	6 (100)		5 (83)		4 (67)		5 (83)
Knee and sports	3 (60)		4 (80)		3 (60)		4 (80)
Pathology	4 (100)		4 (100)		4 (100)		4 (100)
Pediatrics	5 (71)		6 (86)		3 (43)		7 (100)
Recon	2 (50)		1 (25)		1 (25)		2 (50)
Shoulder and elbow	3 (75)		4 (100)		3 (75)		4 (100)
Spine	3 (50)		5 (83)		3 (50)		5 (83)
Trauma	2 (50)		3 (75)		2 (50)		2 (50)
Perceived importance		0.3		0.5		0.5		0.6
A	5 (63)		7 (88)		5 (63)		7 (88)
B	6 (75)		7 (88)		5 (63)		7 (88)
C	8 (80)		9 (90)		7 (70)		8 (80)
D	8 (80)		7 (70)		6 (60)		9 (90)
E	4 (40)		6 (60)		3 (30)		6 (60)
Question complexity		0.2		0.3		0.6		> 0.9
1	8 (80)		10 (100)		7 (70)		8 (80)
2	8 (80)		7 (70)		5 (50)		9 (90)
3	8 (80)		8 (80)		7 (70)		7 (70)
4	4 (40)		6 (60)		4 (40)		8 (80)
5	3 (50)		5 (83)		3 (50)		5 (83)

¹Fisher’s exact test; Wilcoxon rank sum test.

OE: OpenEvidence.