IDEAS home Printed from https://ideas.repec.org/p/ipt/iptwpa/jrc143482.html

The Role of Artificial Intelligence in Scientific Research

Author

Listed:

Abstract

Artificial Intelligence (AI) is fundamentally transforming the scientific process across all stages, from hypothesis generation and experimental design to data analysis, peer review and dissemination of results. In many research fields, such as the examined protein structure prediction, materials discovery and computational humanities, AI accelerates discovery, fosters interdisciplinary collaboration and enhances reproducibility, while improving access to advanced analytical and computational capabilities. These developments align with the European Union (EU)’s vision to make AI tools and infrastructure more accessible, strengthening research in areas of strategic importance such as climate change, health, and clean technologies. However, this progress introduces new challenges, including concerns about algorithmic bias, the proliferation of hallucinations and fabricated data, and the potential erosion of critical thinking skills. AI Adoption remains uneven across scientific domains, and addressing these risks requires robust governance, transparency and alignment with open-science principles. This report, accompanying the publication of the European Strategy for AI in Science, provide scientific evidence to support policymakers in maximising AI’s benefits for EU’s research excellence, innovation and competitiveness, while ensuring its deployment in science remains ethical, inclusive and aligned with European values.

Suggested Citation

  • Purificato Erasmo & Bili Danai & Jungnickel Robert & Ruiz Serra Victoria & Fabiani Josefina & Abendroth Dias Kulani & Fernandez Llorca David & Gomez Emilia, 2025. "The Role of Artificial Intelligence in Scientific Research," JRC Research Reports JRC143482, Joint Research Centre.
    • Handle: RePEc:ipt:iptwpa:jrc143482
    as

    Download full text from publisher

    File URL: https://publications.jrc.ec.europa.eu/repository/handle/JRC143482
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ewen Callaway, 2024. "Chemistry Nobel goes to developers of AlphaFold AI that predicts protein structures," Nature, Nature, vol. 634(8034), pages 525-526, October.
    2. Cristina Cornelio & Sanjeeb Dash & Vernon Austel & Tyler R. Josephson & Joao Goncalves & Kenneth L. Clarkson & Nimrod Megiddo & Bachir El Khadir & Lior Horesh, 2023. "Combining data and theory for derivable scientific discovery with AI-Descartes," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    3. Jason W. Burton & Ezequiel Lopez-Lopez & Shahar Hechtlinger & Zoe Rahwan & Samuel Aeschbach & Michiel A. Bakker & Joshua A. Becker & Aleks Berditchevskaia & Julian Berger & Levin Brinkmann & Lucie Fle, 2024. "How large language models can reshape collective intelligence," Nature Human Behaviour, Nature, vol. 8(9), pages 1643-1655, September.
    4. Yannis Assael & Thea Sommerschield & Brendan Shillingford & Mahyar Bordbar & John Pavlopoulos & Marita Chatzipanagiotou & Ion Androutsopoulos & Jonathan Prag & Nando Freitas, 2022. "Restoring and attributing ancient texts using deep neural networks," Nature, Nature, vol. 603(7900), pages 280-283, March.
    5. Florian Wätzold & Bartosz Popiela & Jonas Mayer, 2024. "Methodology for AI-Based Search Strategy of Scientific Papers: Exemplary Search for Hybrid and Battery Electric Vehicles in the Semantic Scholar Database," Publications, MDPI, vol. 12(4), pages 1-16, December.
    6. Chen, Zhiyuan & Zhang, Jie, 2019. "Types of patents and driving forces behind the patent growth in China," Economic Modelling, Elsevier, vol. 80(C), pages 294-302.
    7. Nathan J. Szymanski & Bernardus Rendy & Yuxing Fei & Rishi E. Kumar & Tanjin He & David Milsted & Matthew J. McDermott & Max Gallant & Ekin Dogus Cubuk & Amil Merchant & Haegyeom Kim & Anubhav Jain & , 2023. "An autonomous laboratory for the accelerated synthesis of novel materials," Nature, Nature, vol. 624(7990), pages 86-91, December.
    8. Brooks Hanson & Shelley Stall & Joel Cutcher-Gershenfeld & Kristina Vrouwenvelder & Christopher Wirz & Yuhan (Douglas) Rao & Ge Peng, 2023. "Garbage in, garbage out: mitigating risks and maximizing benefits of AI in research," Nature, Nature, vol. 623(7985), pages 28-31, November.
    9. Guan Wang & Rebecca Frederick & Jinglong Duan & William B. L. Wong & Verica Rupar & Weihua Li & Quan Bai, 2025. "Detecting misinformation through framing theory: the frame element-based model," Journal of Computational Social Science, Springer, vol. 8(3), pages 1-25, August.
    10. Martin Steinegger & Johannes Söding, 2018. "Clustering huge protein sequence sets in linear time," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    11. John Dagdelen & Alexander Dunn & Sanghoon Lee & Nicholas Walker & Andrew S. Rosen & Gerbrand Ceder & Kristin A. Persson & Anubhav Jain, 2024. "Structured information extraction from scientific text with large language models," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    12. Shijie Wu & Ozan Irsoy & Steven Lu & Vadim Dabravolski & Mark Dredze & Sebastian Gehrmann & Prabhanjan Kambadur & David Rosenberg & Gideon Mann, 2023. "BloombergGPT: A Large Language Model for Finance," Papers 2303.17564, arXiv.org, revised Dec 2023.
    13. Jeon, June & Kim, Lanu & Park, Jaehyuk, 2025. "The ethics of generative AI in social science research: A qualitative approach for institutionally grounded AI research ethics," Technology in Society, Elsevier, vol. 81(C).
    14. Tong Bao & Yi Zhao & Jin Mao & Chengzhi Zhang, 2025. "Examining linguistic shifts in academic writing before and after the launch of ChatGPT: a study on preprint papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(7), pages 3597-3627, July.
    15. Stefano Bianchini & Moritz Muller & Pierre Pelletier, 2023. "Drivers and Barriers of AI Adoption and Use in Scientific Research," Papers 2312.09843, arXiv.org, revised Feb 2024.
    16. Arvind Narayanan & Sayash Kapoor, 2025. "Why an overreliance on AI-driven modelling is bad for science," Nature, Nature, vol. 640(8058), pages 312-314, April.
    17. Jeffrey A. Ruffolo & Lee-Shin Chu & Sai Pooja Mahajan & Jeffrey J. Gray, 2023. "Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    18. Sivasubramanian Manikandan & Rangarajan Sindhu Kaviya & Dhamodharan Hemnath Shreeharan & Ramasamy Subbaiya & Sundaram Vickram & Natchimuthu Karmegam & Woong Kim & Muthusamy Govarthanan, 2025. "Artificial intelligence‐driven sustainability: Enhancing carbon capture for sustainable development goals– A review," Sustainable Development, John Wiley & Sons, Ltd., vol. 33(2), pages 2004-2029, April.
    19. Anastasiia V. Sadybekov & Vsevolod Katritch, 2023. "Computational approaches streamlining drug discovery," Nature, Nature, vol. 616(7958), pages 673-685, April.
    20. Victor Chernozhukov & Christian Hansen & Nathan Kallus & Martin Spindler & Vasilis Syrgkanis, 2024. "Applied Causal Inference Powered by ML and AI," Papers 2403.02467, arXiv.org.
    21. Xianhua Zhang & Lu Lin & Qihua Wang, 2025. "Updatable Estimation in Generalized Linear Models with Missing Data," Statistical Papers, Springer, vol. 66(1), pages 1-26, January.
    22. Luis M. Antunes & Keith T. Butler & Ricardo Grau-Crespo, 2024. "Crystal structure generation with autoregressive large language modeling," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    23. Guido Schryen & Mauricio Marrone & Jiaqi Yang, 2025. "Exploring the scope of generative AI in literature review development," Electronic Markets, Springer;IIM University of St. Gallen, vol. 35(1), pages 1-26, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohammed-Khalil Ghali & Cecil Pang & Oscar Molina & Carlos Gershenson-Garcia & Daehan Won, 2025. "Forecasting Commodity Price Shocks Using Temporal and Semantic Fusion of Prices Signals and Agentic Generative AI Extracted Economic News," Papers 2508.06497, arXiv.org.
    2. Ching-Nam Hang & Pei-Duo Yu & Roberto Morabito & Chee-Wei Tan, 2024. "Large Language Models Meet Next-Generation Networking Technologies: A Review," Future Internet, MDPI, vol. 16(10), pages 1-29, October.
    3. Zhangsheng Liu & Liuqingqing Yang & Liqin Fan, 2021. "Induced Effect of Environmental Regulation on Green Innovation: Evidence from the Increasing-Block Pricing Scheme," IJERPH, MDPI, vol. 18(5), pages 1-15, March.
    4. Nilmani Singh & Stephan Lane & Tianhao Yu & Jingxia Lu & Adrianna Ramos & Haiyang Cui & Huimin Zhao, 2025. "A generalized platform for artificial intelligence-powered autonomous enzyme engineering," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    5. Liu, Weiwei & Song, Yifan & Bi, Kexin, 2021. "Exploring the patent collaboration network of China's wind energy industry: A study based on patent data from CNIPA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    6. Xia Li & Hanghang Zheng & Xiao Chen & Hong Liu & Mao Mao, 2025. "Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring," Papers 2501.10677, arXiv.org, revised Jan 2025.
    7. Amanda Balasooriya & Darshana Sedera, 2025. "Top Management Challenges in Using Artificial Intelligence for Sustainable Development Goals: An Exploratory Case Study of an Australian Agribusiness," Sustainability, MDPI, vol. 17(15), pages 1-19, July.
    8. Lezhi Li & Ting-Yu Chang & Hai Wang, 2023. "Multimodal Gen-AI for Fundamental Investment Research," Papers 2401.06164, arXiv.org.
    9. Junyao Li & Xuyang Zhao & Qingyuan Fan & Yanping Long & Ronghui Liu & Jixian Zhai & Qing Pan & Yi Li, 2025. "Empowering low-crosstalk, dynamic-decision random access of DNA storage via 384-multiplexed nanopore signatures," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    10. Boeing, Philipp & Mueller, Elisabeth, 2019. "Measuring China's patent quality: Development and validation of ISR indices," China Economic Review, Elsevier, vol. 57(C).
    11. Peicong Lin & Yumeng Yan & Huanyu Tao & Sheng-You Huang, 2023. "Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    12. Thanos Konstantinidis & Giorgos Iacovides & Mingxue Xu & Tony G. Constantinides & Danilo Mandic, 2024. "FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications," Papers 2403.12285, arXiv.org.
    13. Mingyang Wang & Shuai Li & Jike Wang & Odin Zhang & Hongyan Du & Dejun Jiang & Zhenxing Wu & Yafeng Deng & Yu Kang & Peichen Pan & Dan Li & Xiaorui Wang & Xiaojun Yao & Tingjun Hou & Chang-Yu Hsieh, 2024. "ClickGen: Directed exploration of synthesizable chemical space via modular reactions and reinforcement learning," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    14. Wen Yue, 2022. "Foreign direct investment and the innovation performance of local enterprises," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 9(1), pages 1-9, December.
    15. Yijia Xiao & Edward Sun & Tong Chen & Fang Wu & Di Luo & Wei Wang, 2025. "Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning," Papers 2509.11420, arXiv.org.
    16. Wang, Zixuan & Chen, Zijian & Wang, Boyuan & Wu, Chuang & Zhou, Chao & Peng, Yang & Zhang, Xinyu & Ni, Zongming & Chung, Chi-yung & Chan, Ching-chuen & Yang, Jian & Zhao, Haitao, 2025. "Digital manufacturing of perovskite materials and solar cells," Applied Energy, Elsevier, vol. 377(PB).
    17. Meyer, Julian Anton, 2025. "Success factors and development areas for the implementation of Generative AI in companies," Junior Management Science (JUMS), Junior Management Science e. V., vol. 10(1), pages 1-23.
    18. Frank Xing, 2024. "Designing Heterogeneous LLM Agents for Financial Sentiment Analysis," Papers 2401.05799, arXiv.org.
    19. Daniel de Cerqueira Lima e Penalva Santos & Juliana Berendt Ihler & Laene Oliveira Soares & Yana Amaral Alves & Cristina Gomez Souza & Ronney Arismel Mancebo Boloy, 2022. "Global Trends and Opportunities in Hybrid Microgrid Systems Using Renewable Energies," International Journal of Energy Economics and Policy, Econjournals, vol. 12(4), pages 263-273, July.
    20. Zhiyuan Chen & Minjie Deng & Min Fang, 2022. "Financing Innovation with Innovation," Working Papers 002004, University of Florida, Department of Economics.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ipt:iptwpa:jrc143482. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Publication Officer (email available below). General contact details of provider: https://edirc.repec.org/data/ipjrces.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.