ReviewClinical and translational science2025
Agents for Change: Artificial Intelligent Workflows for Quantitative Clinical Pharmacology and Translational Sciences.
Review in Clinical and translational science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Sustainability in Pharmaceutical Research and Development: A Perspective on Opportunities for Clinical Pharmacology and Translational Sciences.Clinical pharmacology and therapeutics · 2026Article
- Expanding Quantitative Medicines' Reach: Systems and Methods to Enhance R&D Productivity and Decision-Making in the Agentic-Era.Clinical and translational science · 2026Article
- From Executor to Orchestrator: The Pharmacology Scientist in the Age of Agentic AI.Clinical pharmacology and therapeutics · 2026Review
- Automating the Management of Extraspinal Findings in Magnetic Resonance Imaging Spine Studies Using a Privacy-Preserving Large Language Model: Retrospective Validation Study.Journal of medical Internet research · 2026Article
- Empowering clinical trial design with agentic intelligence and real-world data.Nature communications · 2026Article
- AI-Driven Discovery and Design of Antimicrobial Peptides: Progress, Challenges, and Opportunities.Probiotics and antimicrobial proteins · 2026Review
- An agentic AI system for automated pharmacogenomic recommendation generation.NPJ digital medicine · 2026Article
- Retrieval Augmented Generation (RAG) for Evaluating Regulatory Compliance of Drug Information and Clinical Trial Protocols.CPT: pharmacometrics & systems pharmacology · 2026Article
- Artificial Intelligence agents for biological research: a survey.Briefings in bioinformatics · 2026Article
- Artificial intelligence-assisted photodynamic diagnosis and photodynamic therapy against cancer.Frontiers in oncology · 2026Review
- DiscoVerse: multi-agent pharmaceutical co-scientist for traceable drug discovery and reverse translation.Frontiers in artificial intelligence · 2026Article
- AI for NONMEM Coding in Pharmacometrics Research and Education: Shortcut or Pitfall?CPT: pharmacometrics & systems pharmacology · 2025Article
- Artificial Intelligence in Clinical and Translational Science: From Bench Insights to Bedside Impact.Clinical and translational science · 2025Article
- Pharmacometrics in the Age of Large Language Models: A Vision of the Future.Pharmaceutics · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is making a significant impact across various industries, including healthcare, where it is driving innovation and increasing efficiency. In the fields of Quantitative Clinical Pharmacology (QCP) and Translational Sciences (TS), AI offers the potential to transform traditional practices through the use of agentic workflows-systems with different levels of autonomy where specialized AI agents work together to perform complex tasks, while keeping "human in the loop." These workflows can simplify processes, such as data collection, analysis, modeling, and simulation, leading to greater efficiency and consistency. This review explores how these AI-powered agentic workflows can help in addressing some of the current challenges in QCP and TS by streamlining pharmacokinetic and pharmacodynamic analyses, optimizing clinical trial designs, and advancing precision medicine. By integrating domain-specific tools while maintaining data privacy and regulatory standards, well-designed agentic workflows empower scientists to automate routine tasks and make more informed decisions. Herein, we showcase practical examples of AI agents in existing platforms that support QCP and biomedical research and offer recommendations for overcoming potential challenges involved in implementing these innovative workflows. Looking ahead, fostering collaborative efforts, embracing open-source initiatives, and establishing robust regulatory frameworks will be key to unlocking the full potential of agentic workflows in advancing QCP and TS. These efforts hold the promise of speeding up research outcomes and improving the efficiency of drug development and patient care.
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.