Exploring the ethical issues posed by AI and big data technologies in drug development.

Area of Science:

• Biomedicine
• Computer Science
• Pharmacology

Background:

• Artificial Intelligence (AI) and Big Data are transforming global industries, including drug development.
• Traditional drug development is lengthy and costly, often taking a decade and billions of dollars.
• AI integration promises to significantly accelerate and enhance the drug discovery and development process.

Purpose of the Study:

• To systematically analyze technological breakthroughs of AI and Big Data in drug development.
• To identify and discuss potential risks and ethical issues associated with AI in drug discovery.
• To explore governance pathways for responsible innovation in AI-driven drug development.

Main Methods:

• Leveraging big data analytics and deep learning techniques for R&D.
• Analyzing AI applications in compound screening, efficacy prediction, and clinical trial design.
• Examining ethical considerations such as data privacy and algorithmic transparency.

Main Results:

• AI significantly improves R&D efficiency and accuracy in drug discovery.
• Drug development cycles can be compressed to two years or less using AI.
• Ethical challenges including data privacy and algorithmic transparency emerge with AI adoption.

Conclusions:

• AI and Big Data are reconstructing the drug development paradigm.
• Addressing ethical concerns is crucial for building a responsible innovation ecosystem.
• Strengthening safety and ethical guidelines is essential for the AI-driven efficiency revolution in medicine.