25dkou654
10.3205/25dkou654
urn:nbn:de:0183-25dkou6548
Meeting Abstract
Assessing the potential and capability of different Large-Language Models (LLMs) in spine surgery for patients and medical experts
Rosenkranz
Rosenkranz
Till
T
Orthopädie, Unfallchirurgie und Plastisch-Ästhetische Chirurgie Uniklinik Köln, Köln, Deutschland
author
Wegmann
Wegmann
Sebastian
S
Orthopädie, Unfallchirurgie und Plastisch-Ästhetische Chirurgie Uniklinik Köln, Köln, Deutschland
author
Egenolf
Egenolf
Philipp
P
Orthopädie, Unfallchirurgie und Plastisch-Ästhetische Chirurgie Uniklinik Köln, Köln, Deutschland
author
Perera
Perera
Akanksha
A
Orthopädie, Unfallchirurgie und Plastisch-Ästhetische Chirurgie Uniklinik Köln, Köln, Deutschland
author
Weber
Weber
Maximilian
M
Orthopädie, Unfallchirurgie und Plastisch-Ästhetische Chirurgie Uniklinik Köln, Köln, Deutschland
author
Müller
Müller
Lars P.
LP
Orthopädie, Unfallchirurgie und Plastisch-Ästhetische Chirurgie Uniklinik Köln, Köln, Deutschland
author
Eysel
Eysel
Peer
P
author
Lenz
Lenz
Maximilian
M
Orthopädie, Unfallchirurgie und Plastisch-Ästhetische Chirurgie Uniklinik Köln, Köln, Deutschland
author
German Medical Science GMS Publishing House
Düsseldorf
610
20251031
engl
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung).
M0634
654
Deutsche Gesellschaft für Orthopädie und Unfallchirurgie
Deutsche Gesellschaft für Orthopädie und Orthopädische Chirurgie
Deutsche Gesellschaft für Unfallchirurgie
Berufsverband für Orthopädie und Unfallchirurgie
Deutscher Kongress für Orthopädie und Unfallchirurgie (DKOU 2025)
Abstracts | DWG | Timing von operativen Eingriffen an der degenerativen LWS
Berlin
20251028
20251031
BS46-4644
TextObjectives and questions: Since their release, large-language models (LLMs) such as ChatGPT-4 Pro, Copilot Pro 16.03, Gemini Advanced 1.5 Pro, and Claude 3.5 Sonnet have demonstrated the ability to pass medical licensing exams and generate differential diagnoses. However, their accountability, capability, and trustworthiness in medical applications remain topics of debate. This study aimed to evaluate the effectiveness of different LLMs in answering common medical questions related to spinal surgery and providing recommendations for further diagnostics and treatment.Material and methods: The study was conducted at a certified Level 1 spine center. A set of ten fictional medical questions and realistic clinical case scenarios was developed. The selected LLMs were prompted to respond to these cases from two perspectives: that of an expert in spine surgery and that of a patient. Responses were assessed for clinical completeness, correctness, and adaptability by two board-certified orthopedic surgeons using a five-point grading scale. Statistical analysis was performed to compare the results.Results: The LLMs achieved high scores (>4) in most evaluated categories. Correctness and completeness were slightly higher than adaptability across all models. ChatGPT and Claude provided more detailed responses, using more sentences compared to Gemini and Copilot. No significant differences were found between the LLMs (p > 0.05) across the three outcome measures. Similarly, no significant differences were observed between responses tailored for medical experts versus patients (p > 0.05). However, responses for patients contained significantly shorter words on average, as measured by syllables per words.Discussion and conclusions: Current LLMs provide reliable and well-structured information for both medical professionals and patients regarding spine surgery. As technology advances, the role of LLMs in supporting medical staff and educating patients is expected to expand further.
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