Artificial intelligence tools expand scientists’ impact but contract science’s focus
Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60 (2023).
Google Scholar
Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. USA 79, 2554–2558 (1982).
Google Scholar
Hopfield, J. J. Neurons with graded response have collective computational properties like those of two-state neurons. Proc. Natl Acad. Sci. USA 81, 3088–3092 (1984).
Google Scholar
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Google Scholar
Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. Commun. ACM, 60, 84–90 (2012).
Hinton, G. E. & Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006).
Google Scholar
Hinton, G. E. Training products of experts by minimizing contrastive divergence. Neural Comput. 14, 1771–1800 (2002).
Google Scholar
Kuhlman, B. et al. Design of a novel globular protein fold with atomic-level accuracy. Science 302, 1364–1368 (2003).
Google Scholar
Jumper, J. et al. Highly accurate protein structure prediction with alphafold. Nature 596, 583–589 (2021).
Google Scholar
Gao, J. & Wang, D. Quantifying the use and potential benefits of artificial intelligence in scientific research. Nat. Human Behav. 8, 2281–2292 (2024).
Evans, J. A. Electronic publication and the narrowing of science and scholarship. Science 321, 395–399 (2008).
Google Scholar
Adıgüzel, T., Kaya, M. H. & Cansu, F. K. Revolutionizing education with AI: exploring the transformative potential of ChatGPT. Contemp. Educat. Technol. 15, ep429 (2023).
Akgun, S. & Greenhow, C. Artificial intelligence in education: addressing ethical challenges in K-12 settings. AI Ethics 2, 431–440 (2022).
Google Scholar
Meskó, B. & Topol, E. J. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. npj Digital Med. 6, 120 (2023).
Google Scholar
Loh, H. W. et al. Application of explainable artificial intelligence for healthcare: a systematic review of the last decade (2011–2022). Comput. Methods Prog. Biomed. 226, 107161 (2022).
Ahmed, I., Jeon, G. & Piccialli, F. From artificial intelligence to explainable artificial intelligence in industry 4.0: a survey on what, how, and where. IEEE Trans. Indust. Inform. 18, 5031–5042 (2022).
Google Scholar
Varadi, M. et al. Alphafold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucl. Acids Res. 50, D439–D444 (2022).
Google Scholar
Degrave, J. et al. Magnetic control of tokamak plasmas through deep reinforcement learning. Nature 602, 414–419 (2022).
Google Scholar
Fawzi, A. et al. Discovering faster matrix multiplication algorithms with reinforcement learning. Nature 610, 47–53 (2022).
Google Scholar
Boiko, D. A., MacKnight, R., Kline, B. & Gomes, G. Autonomous chemical research with large language models. Nature 624, 570–578 (2023).
Google Scholar
Stokel-Walker, C. & Van Noorden, R. What ChatGPT and generative AI mean for science. Nature 614, 214–216 (2023).
Google Scholar
Gilson, A. et al. How does ChatGPT perform on the United States medical licensing examination? The implications of large language models for medical education and knowledge assessment. JMIR Med. Educat. 9, e45312 (2023).
Google Scholar
Salimi, A. & Saheb, H. Large language models in ophthalmology scientific writing: ethical considerations blurred lines or not at all? Am. J. Ophthalmol. 254, 177–181 (2023).
Google Scholar
Liang, W. et al. Mapping the increasing use of LLMs in scientific papers. In Proc. 1st Conference on Language Modeling (COLM, USA, 2024).
Hwang, T. et al. Can ChatGPT assist authors with abstract writing in medical journals? Evaluating the quality of scientific abstracts generated by ChatGPT and original abstracts. PLoS ONE 19, e0297701 (2024).
Google Scholar
Kobak, D., González-Márquez, R., Horvát, E.-Á. & Lause, J. Delving into LLM-assisted writing in biomedical publications through excess vocabulary. Sci. Adv. 11, eadt3813 (2025).
Google Scholar
Wojtowicz, Z. & DeDeo, S. Undermining Mental Proof: How AI Can Make Cooperation Harder by Making Thinking Easier Vol. 39, 1592–1600 (2025).
Frank, M. R., Wang, D., Cebrian, M. & Rahwan, I. The evolution of citation graphs in artificial intelligence research. Nat. Mach. Intell. 1, 79–85 (2019).
Google Scholar
OpenAlex (OpenAlex, 2025); https://openalex.org/.
Clarivate (Web of Science, 2025); https://www.webofscience.com.
Mongeon, P. & Paul-Hus, A. The journal coverage of web of science and scopus: a comparative analysis. Scientometrics 106, 213–228 (2016).
Google Scholar
Devlin, J. et al. BERT: pre-training of deep bidirectional transformers for language understanding. In Proc. 57th Annual Meeting of the Association for Computational Linguistics 4171–4186 (ACL, Italy, 2019).
Wolf, T. et al. Transformers: State-of-the-art natural language processing. In Proc. 58th Annual Meeting of the Association for Computational Linguistics 38–45 (ACL, 2020).
Beltagy, I., Lo, K. & Cohan, A. SciBERT: a pretrained language model for scientific text. In Proc. 57th Annual Meeting of the Association for Computational Linguistics 3613–3618 (ACL, Italy, 2019).
Cohan, A., Feldman, S., Beltagy, I., Downey, D. & Weld, D. S. SPECTER: document-level representation learning using citation-informed transformers. In Proc. 58th Annual Meeting of the Association for Computational Linguistics 2270–2282 (ACL, 2020).
Singh, A., D’Arcy, M., Cohan, A., Downey, D. & Feldman, S. SciRepEval: a multi-format benchmark for scientific document representations. In Proc. 61st Annual Meeting of the Association for Computational Linguistics 5548–5566 (ACL, Canada, 2023).
Landis, J. R. & Koch, G. G. The measurement of observer agreement for categorical data. Biometrics 33, 159–174 (1977).
Fleiss, J. L. Measuring nominal scale agreement among many raters. Psychol. Bull. 76, 378 (1971).
Google Scholar
Chu, J. S. & Evans, J. A. Slowed canonical progress in large fields of science. Proc. Natl Acad. Sci. USA 118, e2021636118 (2021).
Google Scholar
Journal Citation Reports (Clarivate, 2021); https://jcr.clarivate.com/jcr/home.
Ioannidis, J. P., Boyack, K. W. & Klavans, R. Estimates of the continuously publishing core in the scientific workforce. PloS ONE 9, e101698 (2014).
Google Scholar
Kendall, D. G. Birth-and-death processes, and the theory of carcinogenesis. Biometrika 47, 13–21 (1960).
Google Scholar
Fortunato, S. et al. Science of science. Science 359, eaao0185 (2018).
Google Scholar
Milojević, S. Quantifying the cognitive extent of science. J. Informetrics 9, 962–973 (2015).
Google Scholar
McMahan, P. & Evans, J. Ambiguity and engagement. Am. J. Sociol. 124, 860–912 (2018).
Google Scholar
Merton, R. K. The matthew effect in science: the reward and communication systems of science are considered. Science 159, 56–63 (1968).
Google Scholar
Borger, J. G. et al. Artificial intelligence takes center stage: exploring the capabilities and implications of chatgpt and other AI-assisted technologies in scientific research and education. Immunol. Cell Biol. 101, 923–935 (2023).
Google Scholar
Lawrence, N. D. & Montgomery, J. Accelerating AI for science: open data science for science. Royal Soc. Open Sci. 11, 231130 (2024).
Google Scholar
King, R. D. et al. The automation of science. Science 324, 85–89 (2009).
Google Scholar
Burger, B. et al. A mobile robotic chemist. Nature 583, 237–241 (2020).
Google Scholar
Krauss, A. Debunking Revolutionary Paradigm Shifts: Evidence of Cumulative Scientific Progress Across Science Vol. 480, 20240141 (The Royal Society, 2024).
Microsoft Academic Graph (Microsoft, 2015); https://www.microsoft.com/en-us/research/project/microsoft-academic-graph.
Open Academic Graph (Aminer, 2020); https://old.aminer.cn/oag-2-1.
Porter, A. & Rafols, I. Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics 81, 719–745 (2009).
Google Scholar
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).
Google Scholar
LeCun, Y. et al. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1, 541–551 (1989).
Google Scholar
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual Learning for image recognition. In CVPR’16: Proc. 2016 IEEE conference on computer vision and pattern recognition 770–778 (2016).
Face, H. Bert for Sequence Classification (Hugging Face, 2025); https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertForSequenceClassification.
Sekara, V. et al. The chaperone effect in scientific publishing. Proc. Natl Acad. Sci. USA 115, 12603–12607 (2018).
Google Scholar
Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In KDD’16: Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (2016).
Hill, R. et al. The pivot penalty in research. Nature 642, 999–1006 (2025).
Milojević, S., Radicchi, F. & Walsh, J. P. Changing demographics of scientific careers: the rise of the temporary workforce. Proc. Natl Acad. Sci. USA 115, 12616–12623 (2018).
Google Scholar
Xu, F., Wu, L. & Evans, J. Flat teams drive scientific innovation. Proc. Natl Acad. Sci. USA 119, e2200927119 (2022).
Google Scholar
Lin, Y., Frey, C. B. & Wu, L. Remote collaboration fuses fewer breakthrough ideas. Nature 623, 987–991 (2023).
Google Scholar
Kingman, J. F. C. Poisson Processes Vol. 3 (Clarendon, 1992).
Meisling, T. Discrete-time queuing theory. Operat. Res. 6, 96–105 (1958).
Google Scholar
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