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Structural barriers and pathways to artificial intelligence integration in plastic surgery.

Georgios Karamitros1, Wesley P Thayer1, Gregory A Lamaris2

  • 1Department of Plastic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.

Journal of Plastic, Reconstructive & Aesthetic Surgery : JPRAS
|October 30, 2025
PubMed
Summary
This summary is machine-generated.

Integrating artificial intelligence (AI) into plastic surgery requires addressing organizational gaps, not just technical ones. Solutions include boosting AI literacy, fostering collaboration, and aligning incentives for responsible clinical adoption.

Keywords:
Artificial intelligenceClinical governanceEducationFundingOrganizational readinessPlastic surgery

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Plastic Surgery

Background:

  • Artificial intelligence (AI) shows significant promise in plastic surgery, with numerous proofs-of-concept available.
  • However, the responsible clinical integration of AI tools remains a significant challenge, indicating an organizational readiness gap rather than a deficit in AI modeling capabilities.

Purpose of the Study:

  • To identify and address the key barriers hindering the effective clinical integration of AI in plastic surgery.
  • To propose a pragmatic agenda for overcoming these barriers and facilitating the adoption of AI in patient care.

Main Methods:

  • Analysis of organizational and systemic barriers to AI implementation in plastic surgery.
  • Proposal of actionable strategies including enhanced AI literacy, improved clinician-data-science collaboration, and pipeline development for AI champions.
  • Recommendations for aligning incentives and funding structures to support data, code, and validation.

Main Results:

  • Identified three primary modifiable barriers: limited AI literacy and collaboration, an underdeveloped human-capital pipeline for AI champions, and misaligned incentives/funding that undervalue essential components like data sets and validation.
  • Proposed a multi-faceted agenda focusing on embedding AI literacy, formalizing interdisciplinary partnerships, allocating protected time for AI development and deployment, and enabling multi-institutional studies.

Conclusions:

  • Overcoming organizational readiness gaps is crucial for integrating AI into plastic surgery.
  • Implementing the proposed agenda can facilitate the shift from isolated AI prototypes to accountable, generalizable tools that enhance patient care.