ArticleBritish journal of clinical pharmacology2026
Evaluating and leveraging large language models in clinical pharmacology and therapeutics assessment: From exam takers to exam shapers.
Article in British journal of clinical pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- AI-Assisted Angoff Standard Setting for Multiple-Choice Examinations in Medical Education: Comparative Study.JMIR medical education · 2026Article
- Evaluating and leveraging large language models in clinical pharmacology and therapeutics assessment: From exam takers to exam shapers.British journal of clinical pharmacology · 2026Article
- Using assessment in pharmacology to drive learning in the right direction.British journal of clinical pharmacology · 2026Article
- Performance of large language models on the Turkish Pharmacy Specialty Examination: a comparative analysis of accuracy, confidence, and readability.Scientific reports · 2026Article
- Ambiguity Detection in Medical Exams via Large Language Models: Retrospective Cross-Sectional Pilot Study.JMIR medical education · 2026Article
- Reimagining pharmacology education.Pharmacological reviews · 2026Review
- Benchmark evaluation of deepseek AI models in antibacterial clinical decision-making for infectious diseases.BMC medical informatics and decision making · 2026Article
- Assessing multiple-choice question quality in internal medicine: a comparative analysis of three large language models against expert consensus.Frontiers in medicine · 2026Article
- When AI models take the exam: large language models vs medical students on multiple-choice course exams.Medical education online · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsIn medical education, the ability of large language models (LLMs) to match human performance raises questions about their potential as educational tools. This study evaluates LLMs' performance on Clinical Pharmacology and Therapeutics (CPT) exams, comparing their results to medical students and exploring their ability to identify poorly formulated multiple-choice questions (MCQs).
methodsChatGPT-4 Omni, Gemini Advanced, Le Chat and DeepSeek R1 were tested on local CPT exams (third year of bachelor's degree, first/second year of master's degree) and the European Prescribing Exam (EuroPE
resultsLLMs achieved comparable or superior results to medical students across all levels. For local exams, LLMs outperformed M1 students and matched L3 and M2 students. In EuroPE
conclusionLLMs demonstrate capabilities comparable to or exceeding medical students in CPT exams. Their ability to flag potentially flawed MCQs highlights their value not only as educational tools but also as quality control instruments in exam preparation.
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.