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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Published on: December 6, 2024

Comparative performance of large language models on cardiovascular certification simulation exam.

Eesha Nachnani¹, Kashish Goel², Alexander E Sullivan²

¹University School of Nashville, Nashville, TN.

American Heart Journal

|January 14, 2026

Summary

Artificial intelligence (AI) is increasingly used in medicine. A study found that only ChatGPT-4.0, among tested AI models, performed comparably to humans on a cardiovascular medicine exam.

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Cardiovascular Medicine

Background:

Artificial intelligence (AI) demonstrates growing capabilities in medical practice.
AI tools are being evaluated for their potential to assist in clinical decision-making and examinations.

Purpose of the Study:

To assess the performance of leading large-language models (LLMs) on a cardiovascular medicine board-style examination.
To compare the efficacy of different AI platforms in a specialized medical domain.

Main Methods:

Three popular LLMs (ChatGPT-4.0, Gemini, Bing AI) were evaluated.
Performance was measured against a cardiovascular medicine board-style exam, with scores compared to human participants.

Main Results:

ChatGPT-4.0 achieved a score comparable to human participants on the cardiovascular exam.
Gemini and Bing AI did not reach similar performance levels in this specialized medical assessment.

Conclusions:

ChatGPT-4.0 shows significant potential for medical knowledge assessment in cardiology.
Further research is needed to evaluate the broader clinical applicability and limitations of AI in specialized medical fields.