ArticleJournal of multidisciplinary healthcare2025
Assessing AI in Various Elements of Enhanced Recovery After Surgery (ERAS)-Guided Ankle Fracture Treatment: A Comparative Analysis with Expert Agreement.
Article in Journal of multidisciplinary healthcare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aimed to assess and compare the performance of ChatGPT and iFlytek Spark, two AI-powered large language models (LLMs), in generating clinical recommendations aligned with expert consensus on Enhanced Recovery After Surgery (ERAS)-guided ankle fracture treatment. This study aims to determine the applicability and reliability of AI in supporting ERAS protocols for optimized patient outcomes. Methods: A qualitative comparative analysis was conducted using 35 structured clinical questions derived from the Expert Consensus on Optimizing Ankle Fracture Treatment Protocols under ERAS Principles. Questions covered preoperative preparation, intraoperative management, postoperative pain control and rehabilitation, and complication management. Responses from ChatGPT and iFlytek Spark were independently evaluated by two experienced trauma orthopedic specialists based on clinical relevance, consistency with expert consensus, and depth of reasoning. Results: ChatGPT demonstrated higher alignment with expert consensus (29/35 questions, 82.9%), particularly in comprehensive perioperative recommendations, detailed medical rationales, and structured treatment plans. However, discrepancies were noted in intraoperative blood pressure management and preoperative antiemetic selection. iFlytek Spark aligned with expert consensus in 22/35 questions (62.9%), but responses were often more generalized, less clinically detailed, and occasionally inconsistent with best practices. Agreement between ChatGPT and iFlytek Spark was observed in 23/35 questions (65.7%), with ChatGPT generally exhibiting greater specificity, timeliness, and precision in its recommendations. Conclusion: AI-powered LLMs, particularly ChatGPT, show promise in supporting clinical decision-making for ERAS-guided ankle fracture management. While ChatGPT provided more accurate and contextually relevant responses, inconsistencies with expert consensus highlight the need for further refinement, validation, and clinical integration. iFlytek Spark's lower conformity suggests potential differences in training data and underlying algorithms, underscoring the variability in AI-generated medical advice. To optimize AI's role in orthopedic care, future research should focus on enhancing AI alignment with medical guidelines, improving model transparency, and integrating physician oversight to ensure safe and effective clinical applications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.