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

TL;DR

This paper examines how AI and big data are transforming drug development and the ethical challenges they introduce.

Contribution

It systematically analyzes the ethical issues and proposes governance strategies for responsible innovation in AI-driven drug development.

Findings

AI and big data significantly reduce drug development time and costs.

Ethical concerns include data privacy and algorithmic transparency.

A responsible innovation ecosystem is needed to address these challenges.

Abstract

The rapid development and wide application of Artificial Intelligence (AI) and Big Data technologies have profoundly changed the way industries around the world operate, from finance, transportation, education to media, the integration of the two not only improves the efficiency of the industry, but also optimizes the quality of service and decision-making process to a large extent. In the era of deep integration of Biomedicine and AI, AI and Big Data technology are reconstructing the paradigm of drug development with unprecedented intensity. The long cycle of traditional drug development, which takes a decade and billions of dollars in investment, is being compressed to 2 years or even less under the drive of AI. Through big data analytics and deep learning techniques, AI can greatly improve R&D efficiency and accuracy in a variety of aspects such as compound screening, efficacy prediction, and clinical (pre) experiment design. However, the use of AI and big data in drug discovery and development also raises corresponding ethical issues, such as data privacy protection and algorithmic transparency. This article will systematically analyze the technological breakthroughs, potential risks, and governance paths of AI and big data in drug development. It will explore how to strengthen the bottom-line of safety and ethics in the Efficiency Revolution and build a responsible innovation ecosystem.