ArticleCureus2026
Adherence of Free-Tier Large Language Models to the 2024 European Society of Cardiology (ESC) Guidelines for the Management of Elevated Blood Pressure and Hypertension: A Comparative Study.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Hypertension remains the leading modifiable risk factor for cardiovascular disease and premature death worldwide. In 2024, the European Society of Cardiology (ESC) released updated guidelines for the management of elevated blood pressure and hypertension. Concurrently, the integration of artificial intelligence into healthcare has accelerated, with large language models (LLMs) becoming accessible tools for information retrieval. Objective This study aims to evaluate and compare the accuracy and adherence of three popular free-tier LLMs (ChatGPT-5.2, Gemini 3 Flash, and Claude 4.5 Sonnet) in responding to questions based strictly on the 2024 ESC Guidelines. Methods We conducted a comparative cross-sectional study in January 2026 to evaluate the performance of three LLMs. The primary source of ground truth was the 2024 ESC Guidelines. A dataset of 40 specific questions was generated, covering key domains including diagnosis, treatment targets, lifestyle modifications, and comorbidities. Questions comprised both factual queries and clinical case reports. Responses were categorized by a qualified physician as correct, inaccurate, or incorrect based strictly on guidelines. Statistical analysis was performed using the Fisher-Freeman-Halton exact test to evaluate differences in performance. Results The overall accuracy across all models was high, with no statistically significant differences in performance observed (p>0.99). Claude 4.5 Sonnet achieved the highest numerical accuracy, providing correct responses to 33 out of 40 questions (82.5%). ChatGPT-5.2 and Gemini 3 Flash achieved identical correctness rates of 80.0% (32 out of 40 correct answers). A qualitative analysis revealed a distinct tendency toward overly aggressive management in complex clinical scenarios, suggesting a "safety bias" where models default to intensive intervention rather than nuanced guideline steps. Conclusions The evaluated free-tier LLMs demonstrated comparable and high proficiency in interpreting the 2024 ESC Guidelines. Despite this potential, the study identified a recurrent safety bias manifesting as a tendency toward over-medicalization. While these models serve as promising auxiliary tools for medical education, verification of AI-generated advice against official guideline documents remains essential.
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.