ReviewClinical and translational science2023
Artificial intelligence in pharmacology research and practice.
Review in Clinical and translational science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- Designing equitable and inclusive assessments in pharmacology: Insights from education practices in medical sciences.British journal of clinical pharmacology · 2026Review
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Reimagining pharmacology education.Pharmacological reviews · 2026Review
- Evaluating Large Language Models for Food Supplement Development: A Case Study in Glycemic Control.Nutrients · 2026Article
- Advancing the development of deep learning and machine learning models for oral drugs through diverse descriptor classes: a focus on pharmacokinetic parameters (Vdss and PPB).Molecular diversity · 2026Article
- Pharmacy Students' Perceptions and Use of Artificial Intelligence Tools in Oman: A Cross-Sectional Survey.Cureus · 2025Article
- Exploring the experiences and perceptions of nursing students in utilizing artificial intelligence: a descriptive phenomenological study.BMC nursing · 2025Article
- Aligning Large Language Models with Humans: A Comprehensive Survey of ChatGPT's Aptitude in Pharmacology.Drugs · 2025Article
- Neuropsychopharmacology in the era of artificial intelligence and biomolecule prediction software.NPP - digital psychiatry and neuroscience · 2025Review
- Navigating Recent Changes in Dosing Information: Dynamics of FDA-Approved Monoclonal Antibodies in Post-Marketing Realities.Clinical and translational science · 2025Review
- AI in Clinical Trials and Drug Development: Challenges and Potential Advancements.Current drug discovery technologies · 2025Review
- Article
- Ethics for artificial intelligence use in clinical pharmacology.Indian journal of pharmacology · 2024Article
- IUPHAR Review: New strategies for medications to treat substance use disorders.Pharmacological research · 2024Review
- Article
- A comprehensive review of artificial intelligence for pharmacology research.Frontiers in genetics · 2024Review
- Revolutionizing pharmacokinetics: the dawn of AI-powered analysis.Journal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, Societe canadienne des sciences pharmaceutiques · 2024Article
- Will artificial intelligence chatbots replace clinical pharmacologists? An exploratory study in clinical practice.European journal of clinical pharmacology · 2023Article
- Review
- Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development.International journal of molecular sciences · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, the use of artificial intelligence (AI) in health care has risen steadily, including a wide range of applications in the field of pharmacology. AI is now used throughout the entire continuum of pharmacology research and clinical practice and from early drug discovery to real-world datamining. The types of AI models used range from unsupervised clustering of drugs or patients aimed at identifying potential drug compounds or suitable patient populations, to supervised machine learning approaches to improve therapeutic drug monitoring. Additionally, natural language processing is increasingly used to mine electronic health records to obtain real-world data. In this mini-review, we discuss the basics of AI followed by an outline of its application in pharmacology research and clinical practice.
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.