AI for Biomedical Research (el. knyga) (skaityta knyga) | knygos.lt

Aprašymas

Artificial intelligence is transforming every aspect of biomedical research practice. Researchers face a fragmented landscape of tools, evolving ethical requirements, emerging regulations, and rapidly shifting best practices. The first of two volumes, this book provides a comprehensive treatment of artificial intelligence across the complete research lifecycle, from initial ideation through analysis, quality assurance, dissemination, and long-term impact.

Key Features

• The only resource covering AI applications across the entire research journey, from initial idea through long-term impact.
• Systematically applies four major AI governance frameworks (RUAIH, FUTURE-AI, NIST AI RMF, and GMLP) throughout both volumes, showing researchers how to operationalize abstract principles in daily practice.
• Includes detailed analysis of the EU AI Act implementation timeline (February 2025 through August 2027) with specific guidance for research applications. Helps researchers and institutions understand high-risk classifications, research exemptions, and compliance strategies before requirements take effect.
• Moves beyond hype and fear to deliver a balanced, practical approach to human-AI collaboration. Each chapter includes "Capabilities and Limitations" analysis, ethical considerations, and implementation strategies that acknowledge both AI's transformative potential and its persistent limitations.
• The book will feature concrete examples demonstrating implementation principles in practice. Each chapter concludes with practical implementation strategies, reflection questions, and guidance applicable to institutions of varying sizes and resources.
• Volume 1 explores how AI can improve essential research activities, including designing studies, securing funding, navigating compliance and ethics reviews, recruiting participants, managing operations, collecting data, and analyzing results.
• Volume 2 continues the exploration of quality assurance, dissemination, peer review, impact, and the necessary institutional changes to responsibly implement AI capabilities.