AI of the Beholder: How Surgical and Medical Specialties View Intelligent Technology

BackgroundHow clinicians conceptualize artificial intelligence reveals underlying assumptions about professional authority and decision-making. This study examined whether surgical and medical specialties frame AI differently in research and if such differences reflect divergent professional norms.Methods1561 AI-related research abstracts published between January 1, 2019, and March 27, 2025, in 30 high-impact journals. Abstracts were identified through a structured PubMed query and analyzed using a large language model (DeepSeek Reasoner) trained to classify along three dimensions: the human-AI relationship, the impact on professional autonomy, and the locus of decision control. A stratified validation sample was independently coded by a human rater. Chi-square testing and logistic regression were used to assess differences by specialty and publication year.ResultsSurgical abstracts more frequently framed AI as assistive (69.8% vs 54.9%; <i>P</i> < .001), explicitly addressed professional autonomy (73.5% vs 61.3%; <i>P</i> < .001), and specified decision control (69.3% vs 58.6%; <i>P</i> < .001) compared to medical abstracts. These differences persisted across the 7-year period. In multivariable logistic regression, assistive framing (OR, 2.43; 95% CI, 1.82-3.23) and explicit autonomy discussion (OR, 1.46; 95% CI, 1.11-1.92) were independently associated with surgical specialty.ConclusionsSurgical and medical specialties exhibit distinct patterns in how they conceptualize AI, reflecting established perspectives on authority, expertise, and the human-machine relationship. These framings have implications for AI tool design, clinical implementation, and healthcare governance. Recognizing conceptual differences on AI is critical as healthcare transitions toward algorithmically mediated decision-making, as they may shape the future culture of clinical care.

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Publication Details

Journal
Surgical Innovation
Published
2026-01-13
DOI
https://doi.org/10.1177/15533506251413069
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
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article

AI of the Beholder: How Surgical and Medical Specialties View Intelligent Technology

Max Ward, Aladine A. Elsamadicy, Timothy G. White, Jung Woo Park et al.
Surgical Innovation
Artificial Intelligence in Healthcare and Education
article

AI of the Beholder: How Surgical and Medical Specialties View Intelligent Technology

Max Ward, Aladine A. Elsamadicy, Timothy G. White, Jung Woo Park, Daniel Schneider, Ethan D. L. Brown, Daniel M. Sciubba, Sheng-Fu Larry Lo
article en

Abstract

BackgroundHow clinicians conceptualize artificial intelligence reveals underlying assumptions about professional authority and decision-making. This study examined whether surgical and medical specialties frame AI differently in research and if such differences reflect divergent professional norms.Methods1561 AI-related research abstracts published between January 1, 2019, and March 27, 2025, in 30 high-impact journals. Abstracts were identified through a structured PubMed query and analyzed using a large language model (DeepSeek Reasoner) trained to classify along three dimensions: the human-AI relationship, the impact on professional autonomy, and the locus of decision control. A stratified validation sample was independently coded by a human rater. Chi-square testing and logistic regression were used to assess differences by specialty and publication year.ResultsSurgical abstracts more frequently framed AI as assistive (69.8% vs 54.9%; <i>P</i> < .001), explicitly addressed professional autonomy (73.5% vs 61.3%; <i>P</i> < .001), and specified decision control (69.3% vs 58.6%; <i>P</i> < .001) compared to medical abstracts. These differences persisted across the 7-year period. In multivariable logistic regression, assistive framing (OR, 2.43; 95% CI, 1.82-3.23) and explicit autonomy discussion (OR, 1.46; 95% CI, 1.11-1.92) were independently associated with surgical specialty.ConclusionsSurgical and medical specialties exhibit distinct patterns in how they conceptualize AI, reflecting established perspectives on authority, expertise, and the human-machine relationship. These framings have implications for AI tool design, clinical implementation, and healthcare governance. Recognizing conceptual differences on AI is critical as healthcare transitions toward algorithmically mediated decision-making, as they may shape the future culture of clinical care.

Surgical Innovation
Northwell Health (US), North Shore University Hospital (US), The Ohio State University Wexner Medical Center (US), Northwell Health Orthopaedic Institute (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 89%
Artificial Intelligence in Healthcare and Education
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