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Hypertension is asymptomatic and also referred to as the "silent killer" until it progresses to a severe stage or causes target organ disease. Patients may experience symptoms stemming from the strain on blood vessels and tissues in various organs or the heart's increased workload.Physical exams might show no abnormalities other than high blood pressure. Signs of vascular damage, when present, correspond to the organs supplied by the affected vessels, leading to target organ damage. For...
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Hypertension is a widespread, long-term medical condition where blood pressure in the arteries remains elevated. It is characterized by systolic blood pressure readings of 130 mm Hg or above or diastolic blood pressure (DBP) readings of 80 mm Hg or higher. Unmanaged hypertension poses significant health risks, making the distinction between primary (or essential) hypertension and secondary hypertension crucial, as their management and implications vary.Primary HypertensionPrimary hypertension,...
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Accurate blood pressure assessment is crucial for diagnosing and managing various health conditions. To ensure the reliability of these measurements, healthcare professionals must adhere to standardized pre-procedural guidelines. These guidelines enhance patient safety and improve the overall quality of healthcare. The following steps are essential for obtaining accurate and consistent blood pressure readings, from using the appropriate tools to ensuring effective communication with the...
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Performance of Large Language Models in Analyzing Common Hypertension Scenarios.

Jaleh Zand1, Jing Miao1, Musab S Hommos2

  • 1Division of Nephrology and Hypertension (J.Z., J.M., G.L.S., S.J.T., W.C., V.D.G., Z.M.Z.), Mayo Clinic, Rochester, MN.

Hypertension (Dallas, Tex. : 1979)
|November 3, 2025
PubMed
Summary

Large language models (LLMs) show potential for aiding hypertension management, with GPT-4 demonstrating the highest accuracy and safety among tested models. However, current LLMs are not yet superior to expert recommendations, necessitating human oversight in clinical applications.

Keywords:
artificial intelligenceclinical decision-makinghypertensionlarge language modelsprimary health care

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems
  • Cardiovascular Disease Management

Background:

  • Hypertension is a leading cause of cardiovascular mortality with suboptimal control rates.
  • Large language models (LLMs) offer potential for improving hypertension management by assisting clinical decision-making.
  • The reliability of LLMs for guideline-driven medical tasks requires thorough evaluation.

Purpose of the Study:

  • To assess the accuracy and safety of hypertension management recommendations generated by three distinct LLMs.
  • To compare LLM performance against expert-generated recommendations for hypertension care.
  • To determine the guideline concordance and reliability of LLM-based clinical advice.

Main Methods:

  • Fifty-one clinical vignettes were developed for hypertension management scenarios.
  • Responses were generated by three LLMs (GPT-4, Gemini, MedLM) and a hypertension expert.
  • Blinded reviewers evaluated responses for accuracy, safety, and source identification.

Main Results:

  • GPT-4 achieved the highest accuracy (83%) and safety (86%) among LLMs, yet remained below expert performance (92% accuracy, 93% safety).
  • Gemini (64% accuracy, 73% safety) and MedLM (35% accuracy, 39% safety) showed significantly lower performance.
  • GPT-4 exhibited greater guideline concordance (46%) compared to other LLMs but lagged behind expert recommendations (68%).

Conclusions:

  • GPT-4 shows promise for supporting hypertension management due to its closer alignment with expert decisions.
  • Current LLM capabilities in hypertension management are inferior to those of human experts.
  • Continuous human-in-the-loop supervision is crucial for the safe and effective deployment of LLMs in clinical settings.