2023 ACCP Annual Meeting Invited Keynote
Cynthia J. (CJ) Musante, PhD, Vice President, Scientific Research & Global Head of Quantitative Systems Pharmacology, Pfizer Worldwide Research, Development & Medical Clinical Pharmacology & Bioanalytics
Presentation Title: Benefit vs Risk: What’s the Optimal Dose of Artificial Intelligence in Clinical Pharmacology?
ACCP is pleased to announce that its 2023 Invited Keynote is Cynthia J. (CJ) Musante, PhD, Vice President of Scientific Research and the Global Head of Quantitative Systems Pharmacology (QSP), Clinical Pharmacology & Bioanalytics, at Pfizer Inc. Dr. Musante received her PhD in Applied Mathematics from North Carolina State Univ and has over twenty years of experience in QSP modeling. At Pfizer, her group is responsible for developing and applying systems models and disease platforms across the portfolio to enhance the robustness and quality of decision making at the program and therapeutic-strategy level. Dr. Musante is an advocate for model-informed drug development, both internally and externally. She is a frequent organizer and invited speaker at national and international conferences and currently serves as President of the Int’l Society of Pharmacometrics.
Dr. Musante will present 'Benefit vs Risk: What's the Optimal Dose of Artificial Intelligence in Clinical Pharmacology?" on Monday, September 11, 2023 at 10:00 AM PT. Artificial intelligence (AI) is revolutionizing various domains of healthcare and its potential in clinical pharmacology is both promising and challenging. The integration of AI and machine learning (AI/ML) in pharmacological research and clinical practice has the potential to enhance data-driven decision making, accelerate drug discovery and development and improve patient outcomes through personalized medicine approaches. However, determining the optimal integration or “dose” of AI/ML in clinical pharmacology requires careful consideration of the benefits it brings, along with its associated risks and ethical considerations. The current landscape and future potential of AI/ML applications will be examined, ranging from drug discovery and development to individualized dosing and adverse event prediction. While AI offers unprecedented opportunities, it is important to acknowledge and address the risks and challenges associated with its implementation. Ethical considerations, data availability, quality & privacy and algorithmic biases are among the critical factors that demand careful attention. This talk will highlight the need for transparency, robust validation and explainability in AI-driven systems to ensure trust, reliability and accountability in clinical decision-making processes. Striking the right balance between human expertise and AI-driven recommendations becomes paramount, as clinical pharmacologists navigate the complexities of incorporating AI/ML into their research and practice. By weighing the potential benefits against the inherent dangers, this talk aims to provide attendees with practical insights to inform the safe and effective use of AI/ML, fostering a future where AI-powered clinical pharmacology maximizes outcomes while minimizing risks.