Artificial intelligence awareness and perceptions among pediatric orthopedic surgeons: A cross-sectional observational study

Table 1 Participants’ demographic characteristics, n (%)

Variables	Subcategory	Frequency (n = 71)
Age in years	< 40	33 (46.5)
	40-60	32 (45.1)
	> 60	6 (8.5)
Experience as a pediatric orthopedic surgeon in years	< 10	41 (57.8)
	11-20	21 (29.6)
	> 20	9 (12.7)
Main hospital affiliation	University	15 (21.1)
	1Military	14 (19.7)
	Ministry of health	26 (36.6)
	Specialist or medical city	11 (15.5)
	Private	5 (7.0)
Region of practice	Central	29 (40.9)
	Northern	2 (2.8)
	Southern	9 (12.7)
	Eastern	13 (18.3)
	Western	13 (18.3)
	2Outside Saudi Arabia	5 (7.0)
Place of fellowship	Saudi Arabia	31 (43.7)
	Northern America (Canada and United States)	23 (32.4)
	Europe	11 (15.5)
	3Others	6 (8.5)

¹Military, Security forces, National guard.

²Bahrain, Kuwait (1), Oman (3), Qatar (1), Egypt (1).

³Other places of fellowship include Eastern Asia (2), while others were not specified.

Table 2 Participants’ questionnaire responses, n (%)

Topical category	Response categories and data
Perceptions towards AI	Strongly disagree	Disagree	Neutral	Agree	Strongly agree
AI can enhance diagnostic accuracy in pediatric orthopedic cases	2 (2.8)	6 (8.5)	30 (42.3)	25 (35.2)	8 (11.3)
AI has the potential to improve treatment planning for pediatric orthopedic conditions	1 (1.4)	4 (5.6)	26 (36.6)	34 (47.9)	6 (8.5)
AI integration in pediatric orthopedics can enhance surgical outcomes	1 (1.4)	9 (12.7)	32 (45.1)	23 (32.4)	6 (8.5)
AI applications can save time and enhance productivity in pediatric orthopedic surgery	2 (2.8)	1 (1.4)	24 (33.8)	34 (47.9)	10 (14.1)
Familiarity with AI	Not at all familiar	Slightly familiar	Moderately familiar	Very familiar	Extremely familiar
How familiar are you with AI in medicine?	18 (25.4)	26 (36.6)	24 (33.8)	2 (2.8)	1 (1.4)
Willingness to adopt AI tools	Very unwilling	Somewhat unwilling	Neutral	Somewhat willing	Very willing
How willing are you to adopt AI-based tools or systems in your clinical practice, if they are proven to be safe and effective?	0 (0)	0 (0)	9 (12.7)	30 (42.3)	32 (45.1)
Factors affecting decision making	Not important at all	Slightly important	Moderately important	Quite important	Highly important
Evidence-based research supporting AI in pediatric orthopedics	0 (0)	4 (5.6)	13 (18.3)	19 (26.8)	35 (49.3)
Trust in the accuracy of AI-driven diagnostics	1 (1.4)	6 (8.5)	12 (16.9)	21 (29.6)	31 (43.7)
Support and training provided for AI utilization	1 (1.4)	0 (0)	13 (18.3)	18 (25.4)	39 (54.9)
Protection of patient privacy and data security with AI implementation	1 (1.4)	2 (2.8)	4 (5.6)	16 (22.5)	48 (67.6)
Ease of integration of AI systems into current practice	0 (0.0)	2 (2.8)	13 (18.3)	22 (31.0)	34 (47.9)
Interest in learning about AI	Very uninterested	Somewhat uninterested	Neutral	Somewhat interested	Very interested
How interested are you in learning more about AI and how to apply them in your clinical practice?	0 (0)	3 (4.2)	8 (11.3)	24 (33.8)	36 (50.7)

AI: Artificial intelligence.

Table 3 Participants’ questionnaire responses continued, n (%)

Topical category	Response categories and data
1Which of the following AI tools have you encountered?
AI speech-to-text tools (e.g., mobius conveyor, the nuance dragon ambient experience, augmedix)	23 (32.4)
Image analysis tools (e.g., nuance’s precision imaging network, zebra medical vision)	16 (22.5)
AI clinical decision support tools (e.g., Sepsis Watch, ChatGPT 35/4)	14 (19.7)
Surgical support tools (e.g., Da Vinci surgical system, ActivSight system)	10 (14.1)
None of the above	34 (47.9)
Would you recommend a tested and proven AI tool in pediatric orthopedic surgery to other clinicians?
Yes	64 (91.4)
No	6 (8.6)
What role do you think AI will play in pediatric orthopedic surgery over the next 5 to 10 years?
It will not have a significant impact	2 (2.8)
It will be used in a limited capacity	31 (43.7)
It will become a fundamental part of the field	23 (32.4)
Uncertain	15 (21.1)

¹Participants can choose one, more than one, or not at all.

AI: Artificial intelligence.

Table 4 Factors related to the domains in the study

Variables	Subcategory	Familiarity		Perception		Willingness		Decision-making
		Median (IQR)	4P value	Median (IQR)	4P value	Median (IQR)	4P value	Median (IQR)	4P value
Age in years	< 40	2.0 (1)	1.722 (0.423)	19.0 (5)	1.723 (0.422)	5.0 (1)	0.619 (0.734)	23.0 (7)	0.030 (0.985)
	40-60	2.0 (2)		18.5 (5)		5.0 (1)		22.5 (5)
	> 60	1.5 (2)		16.0 (5)		5.0 (2)		21.0 (7)
Experience in years	< 10	2.0 (1)	7.326 (0.026)a	18.0 (4)	7.037 (0.030)a	5.0 (1)	1.409 (0.494)	23.0 (6)	0.287 (0.866)
	11-20	3.0 (1)		20.0 (5)		5.0 (1)		22.0 (5)
	> 20	1.0 (2)		15.0 (5)		5.0 (2)		21.0 (8)
Hospital affiliation	University	3.0 (2)	1.575 (0.813)	18.0 (6)	6.079 (0.193)	6.0 (1)	1.141 (0.888)	22.0 (6)	1.744 (0.783)
	1Military	2.0 (1)		19.5 (3)		5.0 (1)		24.0 (6)
	Ministry of Health	2.0 (2)		18.5 (4)		5.0 (1)		23.0 (6)
	2Specialist	2.0 (1)		17.0 (3)		5.0 (1)		21.0 (9)
	Private	2.0 (2)		15.0 (4)		5.0 (1)		19.0 (6)
Region	Central region	3.0 (1)	9.019 (0.108)	19.0 (6)	3.464 (0.629)	5.0 (1)	6.033 (0.303)	24.0 (6)	8.445 (0.133)
	Northern	2.5 (0)		18.5 (0)		6.0 (0)		24.5 (0)
	Southern	1.0 (2)		17.0 (4)		5.0 (2)		22.0 (6)
	Eastern	2.0 (2)		20.0 (6)		6.0 (2)		22.0 (5)
	Western	2.0 (1)		17.0 (6)		5.0 (2)		19.0 (10)
	Gulf countries	2.0 (1)		17.0 (3)		5.0 (1)		21.0 (5)
Fellowship	Saudi Arabia	2.0 (2)	1.055 (0.788)	17.0 (6)	1.063 (0.786)	5.0 (1)	2.053 (0.561)	24.0 (6)	5.545 (0.136)
	3Northern America	2.0 (1)		19.0 (5)		5.0 (1)		21.0 (6)
	Europe	2.0 (2)		20.0 (6)		6.0 (1)		22.0 (5)
	Others	1.5 (2)		17.5 (4)		5.5 (1)		20.0 (8)
Future outlook	Significant impact	1.5 (0)	2.219 (0.528)	13.0 (0)	9.985 (0.019)a	4.5 (0)	1.452 (0.693)	17.0 (0)	5.572 (0.134)
	Limited capacity	2.0 (1)		17.0 (6)		5.0 (1)		23.0 (8)
	Fundamental part	2.0 (1)		20.0 (4)		6.0 (1)		21.0 (5)
	Uncertain	2.0 (2)		17.0 (6)		5.0 (1)		25.0 (5)

¹Military hospital (military, security forces, national guard).

²Specialist hospital or medical city.

³Northern America (Canada and United States).

⁴Kruskal-Wallis H test.

^aP < 0.05.

IQR: Interquartile range.

Table 5 Bivariate correlation among study domains

Domain	Familiarity	Perception	Willingness	Decision-making
Familiarity	1.000 (0)	0.568 (< 0.001)	0.262 (0.027)	0.269 (0.023)
Perception	0.568 (< 0.001)	1.000 (0)	0.560 (< 0.001)	0.456 (< 0.001)
Willingness	0.262 (0.027)	0.560 (< 0.001)	1.000 (0)	0.430 (< 0.001)
Decision-making	0.269 (0.023)	0.356 (< 0.001)	0.430 (< 0.001)	1.000 (0)