AI-driven applications in clinical pharmacology and translational science: Insights from the ASCPT 2024 aI preconference
Artificial intelligence (AI) is driving innovation in clinical pharmacology and translational science with tools to advance drug development, clinical trials, and patient care. This review summarizes the key takeaways from the AI preconference at the American Society for Clinical Pharmacology and Th...
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| Hauptverfasser: | , , , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
Apr 2025
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| In: |
Clinical and translational science
Year: 2025, Jahrgang: 18, Heft: 4, Pages: 1-13 |
| ISSN: | 1752-8062 |
| DOI: | 10.1111/cts.70203 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1111/cts.70203 Verlag, kostenfrei, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1111/cts.70203 
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| Verfasserangaben: | Mohamed H. Shahin, Prashant Desai, Nadia Terranova, Yuanfang Guan, Tomáš Helikar, Sebastian Lobentanzer, Qi Liu, James Lu, Subha Madhavan, Gary Mo, Flora T. Musuamba, Jagdeep T. Podichetty, Jie Shen, Lei Xie, Mathew Wiens, Cynthia J. Musante |
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| 520 | |a Artificial intelligence (AI) is driving innovation in clinical pharmacology and translational science with tools to advance drug development, clinical trials, and patient care. This review summarizes the key takeaways from the AI preconference at the American Society for Clinical Pharmacology and Therapeutics (ASCPT) 2024 Annual Meeting in Colorado Springs, where experts from academia, industry, and regulatory bodies discussed how AI is streamlining drug discovery, dosing strategies, outcome assessment, and patient care. The theme of the preconference was centered around how AI can empower clinical pharmacologists and translational researchers to make informed decisions and translate research findings into practice. The preconference also looked at the impact of large language models in biomedical research and how these tools are democratizing data analysis and empowering researchers. The application of explainable AI in predicting drug efficacy and safety, and the ethical considerations that should be applied when integrating AI into clinical and biomedical research were also touched upon. By sharing these diverse perspectives and real-world examples, this review shows how AI can be used in clinical pharmacology and translational science to bring efficiency and accelerate drug discovery and development to address patients' unmet clinical needs. | ||
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