Directorate for Science, Technology and Innovation


Artificial Intelligence in Science

Challenges, Opportunities and the Future of Research

The rapid advances of artificial intelligence (AI) in recent years have led to numerous creative applications in science. Accelerating the productivity of science could be the most economically and socially valuable of all the uses of AI. Utilising AI to accelerate scientific productivity will support the ability of OECD countries to grow, innovate and meet global challenges, from climate change to new contagions. This publication is aimed at a broad readership, including policy makers, the public, and stakeholders in all areas of science. It is written in non-technical language and gathers the perspectives of prominent researchers and practitioners. The book examines various topics, including the current, emerging, and potential future uses of AI in science, where progress is needed to better serve scientific advancements, and changes in scientific productivity. Additionally, it explores measures to expedite the integration of AI into research in developing countries. A distinctive contribution is the book’s examination of policies for AI in science. Policy makers and actors across research systems can do much to deepen AI’s use in science, magnifying its positive effects, while adapting to the fast-changing implications of AI for research governance.

Published on June 26, 2023


Executive summary
Artificial intelligence in science: Overview and policy proposals
Is science getting harder?8 chapters available
Are ideas getting harder to find? A short review of the evidence
The end of Moore’s Law? Innovation in computer systems continues at a high pace
Is technological progress in US agriculture slowing?
Eroom’s Law and the decline in the productivity of biopharmaceutical R&D
Is there a slowdown in research productivity? Evidence from China and Germany
Declining R&D efficiency: Evidence from Japan
Quantifying the “cognitive extent” of science and how it has changed over time and across countries
What can bibliometrics contribute to understanding research productivity?
Artificial intelligence in science today10 chapters available
How can artificial intelligence help scientists? A (non-exhaustive) overview
A framework for evaluating the AI-driven automation of science
Using machine learning to verify scientific claims
Robot scientists: From Adam to Eve to Genesis
From knowledge discovery to knowledge creation: How can literature-based discovery accelerate progress in science?
Advancing the productivity of science with citizen science and artificial intelligence
What can artificial intelligence do for physics?
AI in drug discovery
Data-driven innovation in clinical pharmaceutical research
Applying AI to real-world health-care settings and the life sciences: Tackling data privacy, security and policy challenges with federated learning
The near future: challenges and ways forward8 chapters available
Artificial intelligence in scientific discovery: Challenges and opportunities
Machine reading: Successes, challenges and implications for science
Interpretability: Should – and can – we understand the reasoning of machine-learning systems?
Combining collective and machine intelligence at the knowledge frontier
Elicit: Language models as research tools
Democratising artificial intelligence to accelerate scientific discovery
Is there a narrowing of AI research?
Lessons from shortcomings in machine learning for medical imaging
Artificial intelligence in science: Implications for public policy5 chapters available
Artificial intelligence for science and engineering: A priority for public investment in research and development
The importance of knowledge bases for artificial intelligence in science
High-performance computing leadership to enable advances in artificial intelligence and a thriving compute ecosystem
Improving reproducibility of artificial intelligence research to increase trust and productivity
AI and scientific productivity: Considering policy and governance challenges
Artificial intelligence, science and developing countries3 chapters available
Artificial intelligence and development projects: A case study in funding mechanisms to optimise research excellence in sub-Saharan Africa
Artificial intelligence for science in Africa
Artificial intelligence, developing-country science and bilateral co‑operation
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