ArticleJournal of medical systems2026
Assessment of ChatGPT-5 as an Artificial Intelligence Tool for Exploring Emerging Dimensions of Clinical Simulation: A Proof-of-concept Study.
Article in Journal of medical systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Autonomous Assessment of Medical Consent Forms: Development and Preliminary Feasibility Demonstration of the Universal Health Communication Index (UHCI).Healthcare (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) and large language models (LLMs) such as ChatGPT-5 are increasingly applied in medical education. However, their potential role in clinical simulation remains largely unexplored. This descriptive proof-of-concept study aimed to examine ChatGPT-5's ability to synthesize and generate educational content related to clinical simulation, focusing on the coherence, factual accuracy, and understandability of its outputs. Seven exploratory questions covering conceptual, historical, and technological aspects of clinical simulation were submitted to ChatGPT-5. Each query was regenerated three times to assess consistency. Responses were independently evaluated by multiple reviewers using a five-point Likert scale for content quality and accuracy, and the Patient Education Materials Assessment Tool (PEMAT) for understandability. Authenticity of AI-generated references was verified through PubMed and Google Scholar. ChatGPT-5 produced coherent and organized responses reflecting major milestones and trends in clinical simulation. Approximately 80% of cited references were verifiable, while some inconsistencies indicated residual fabrication. The average agreement score for accuracy and coherence was 4 ("agree"), suggesting generally acceptable quality. PEMAT analysis showed that content was structured and clear but occasionally used complex terminology, limiting accessibility. Within the exploratory scope of this proof-of-concept study, ChatGPT-5 demonstrated potential as a supportive tool for synthesizing information about clinical simulation. Nonetheless, interpretive depth, citation reliability, and pedagogical adaptation require further refinement. Future research should assess the integration of LLMs into immersive simulation environments under robust ethical and educational frameworks.
Indexed as
Identifiers
41507587What 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.