Agents for change: artificial intelligent workflows for quantitative clinical pharmacology and translational sciences : review
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 tradi...
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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
March 2025
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| In: |
Clinical and translational science
Year: 2025, Jahrgang: 18, Heft: 3, Pages: 1-12 |
| ISSN: | 1752-8062 |
| DOI: | 10.1111/cts.70188 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1111/cts.70188 Verlag, kostenfrei, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1111/cts.70188 
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| Verfasserangaben: | Mohamed H. Shahin, Srijib Goswami, Sebastian Lobentanzer, Brian W. Corrigan |
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| 520 | |a 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. | ||
| 650 | 4 | |a agentic workflows | |
| 650 | 4 | |a AI agents | |
| 650 | 4 | |a artificial intelligence | |
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| 700 | 1 | |a Corrigan, Brian W. |e VerfasserIn |4 aut | |
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