Generative Artificial Intelligence as a Research Partner in Orthopaedics: State of the Art

The integration of Artificial Intelligence (AI) into medicine has progressed from discriminative models to Generative AI (GenAI), which can synthesize novel content. For orthopaedic surgeons, scientific publication remains a vital marker of academic success but is often constrained by clinical workload. This review proposes a structured, practical framework to help orthopaedists effectively harness AI tools, transitioning from opaque, "black box" generation to grounded, verifiable research assistance through Retrieval-Augmented Generation (RAG). A PubMed search was conducted to explore the application of GenAI in the context of orthopaedic scientific research. An interactive review with experts in GenAI was also conducted, from which the proposed structure was developed. From this synthesis, a three-phase workflow is proposed: (1) Evidence selection using semantic discovery systems to identify and map relevant literature beyond keyword matching; (2) Data extraction and synthesis employing RAG-based systems to anchor AI responses to verified PDF sources, thereby minimizing hallucinations; and (3) Drafting and refining using Large Language Models (LLMs) for structured composition, linguistic clarity, and iterative manuscript improvement. The workflow integrates platform features to enhance efficiency, accuracy, and accessibility in orthopaedic research. When applied within a controlled, evidence-grounded environment, these systems can automate literature synthesis, expedite data extraction, and assist with scientific writing, while preserving authorial intent and accountability. However, challenges remain. Risks include algorithmic bias, "hallucinations", privacy concerns, and ethical issues related to authorship. Despite these limitations, AI represents a paradigm shift in orthopaedic scholarship, functioning as a cognitive exoskeleton that augments rather than replaces human expertise. With vigilant human oversight and adherence to journal ethics, orthopaedic surgeons can leverage AI to enhance research productivity, reproducibility, and quality while upholding the highest standards of scientific integrity.

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Publication Details

Journal
Journal of ISAKOS Joint Disorders & Orthopaedic Sports Medicine
Published
2026-05-01
DOI
https://doi.org/10.1016/j.jisako.2026.101126
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Generative Artificial Intelligence as a Research Partner in Orthopaedics: State of the Art

Sthefan Gabriel Berwanger, Philippe Neyret, Américo Dias, Paulo Amado et al.
Journal of ISAKOS Joint Disorders & Orthopaedic Sports Medicine
Artificial Intelligence in Healthcare and Education
article

Generative Artificial Intelligence as a Research Partner in Orthopaedics: State of the Art

Sthefan Gabriel Berwanger, Philippe Neyret, Américo Dias, Paulo Amado, João Espregueira-Mendes, Ricardo Bastos
article en

Abstract

The integration of Artificial Intelligence (AI) into medicine has progressed from discriminative models to Generative AI (GenAI), which can synthesize novel content. For orthopaedic surgeons, scientific publication remains a vital marker of academic success but is often constrained by clinical workload. This review proposes a structured, practical framework to help orthopaedists effectively harness AI tools, transitioning from opaque, "black box" generation to grounded, verifiable research assistance through Retrieval-Augmented Generation (RAG). A PubMed search was conducted to explore the application of GenAI in the context of orthopaedic scientific research. An interactive review with experts in GenAI was also conducted, from which the proposed structure was developed. From this synthesis, a three-phase workflow is proposed: (1) Evidence selection using semantic discovery systems to identify and map relevant literature beyond keyword matching; (2) Data extraction and synthesis employing RAG-based systems to anchor AI responses to verified PDF sources, thereby minimizing hallucinations; and (3) Drafting and refining using Large Language Models (LLMs) for structured composition, linguistic clarity, and iterative manuscript improvement. The workflow integrates platform features to enhance efficiency, accuracy, and accessibility in orthopaedic research. When applied within a controlled, evidence-grounded environment, these systems can automate literature synthesis, expedite data extraction, and assist with scientific writing, while preserving authorial intent and accountability. However, challenges remain. Risks include algorithmic bias, "hallucinations", privacy concerns, and ethical issues related to authorship. Despite these limitations, AI represents a paradigm shift in orthopaedic scholarship, functioning as a cognitive exoskeleton that augments rather than replaces human expertise. With vigilant human oversight and adherence to journal ethics, orthopaedic surgeons can leverage AI to enhance research productivity, reproducibility, and quality while upholding the highest standards of scientific integrity.

Journal of ISAKOS Joint Disorders & Orthopaedic Sports Medicine
Universidade Federal Fluminense (BR), Universidade do Porto (PT), American Orthopaedic Foot and Ankle Society (US), Infirmerie Protestante (FR), INESC TEC (PT), University of Minho (PT)
Reduced inequalities
Openalex Percentile: Top 14%
Artificial Intelligence in Healthcare and Education
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