Accelerating Science with Human+AI Review
Abstract
AbstractThis issue of NEJM AI features the first two articles published through our accelerated human+AI review process. In this editorial, we describe the invitation-only "Fast Track" process used to review these studies, both of which are randomized controlled trials of ambient AI scribes. We emphasize the central role of human judgment and values while using AI to accelerate science.
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AI agents that generate paper reviews are now deployed at scale, including at AAAI-26 on all 22,977 main-track submissions and in NEJM AI's Fast Track process.
“AI agents that generate paper reviews, which we refer to as AI reviewers [Liu andShah, 2023, Kuznetsov et al., 2024], are one response now deployed at scale: the AI conference AAAI-26 ran them on all 22,977 main-track submissions [Biswas et al., 2026], and the medical journal NEJM AI has built them into its”
From On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists (2026) · cites this paper as Manrai et al., 2025
Cites
Liang, W., Zhang, Y., Cao, H., Wang, B., Ding, D., Yang, X., Vodrahalli, K., He, S., Smith, D., Yin, Y., et al. (2023) · arXiv · doi:10.48550/arxiv.2310.01783 · Cited by 243 · see citing works
Ambient AI Scribes in Clinical Practice: A Randomized Trial
Lukac, P. J., Turner, W. B., Vangala, S., Chin, A. T., Khalili, J., Shih, Y. T., Sarkisian, C. A., Cheng, E. M., & Mafi, J. N. (2025) · NEJM AI · doi:10.1056/aioa2501000 · Cited by 158 · see citing works
Medical Artificial Intelligence and Human Values
Yu, K.-H., Healey, E., Leong, T.-Y., Kohane, I. S., & Manrai, A. K. (2024) · New England Journal of Medicine · doi:10.1056/nejmra2214183 · Cited by 149 · see citing works
Self-citationLLM Evaluators Recognize and Favor Their Own Generations
Panickssery, A., Bowman, S., & Feng, S. (2024) · Advances in Neural Information Processing Systems 37 · doi:10.52202/079017-2197 · Cited by 96 · see citing works
Afshar, M., Ryan Baumann, M., Resnik, F., Hintzke, J., Gravel Sullivan, A., Wills, G., Lemmon, K., Dambach, J., Mrotek, L. A., Quinn, M., et al. (2025) · NEJM AI · doi:10.1056/aioa2500945 · Cited by 59 · see citing works
Why We Support and Encourage the Use of Large Language Models in NEJM AI Submissions
Koller, D., Beam, A. L., Manrai, A. K., Ashley, E. A., Liu, X., Gichoya, J. W., Holmes, C. C., Zou, J., Dagan, N., Wong, T. Y., et al. (2023) · NEJM AI · doi:10.1056/aie2300128 · Cited by 57 · see citing works
Self-citationCognitive Biases and Artificial Intelligence
Wang, J., & Redelmeier, D. A. (2024) · NEJM AI · doi:10.1056/aics2400639 · Cited by 43 · see citing works
Cognitive Bias in Large Language Models: Implications for Research and Practice
Zwaan, L. (2024) · NEJM AI · doi:10.1056/aie2400961 · Cited by 17 · see citing works
Why Medicine Must Become a Knowledge-Processing Discipline
Kohane, I. S., Beam, A. L., & Manrai, A. K. (2024) · NEJM AI · doi:10.1056/aip2400582 · Cited by 5 · see citing works
Self-citationHuman-in-the-Loop AI Summaries of NEJM AI Grand Rounds
Manrai, A. K., & Beam, A. L. (2024) · NEJM AI · doi:10.1056/aie2400581 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
When and how to disclose AI use in academic publishing: AMEE Guide No.192
Cleland, J., Driessen, E. W., Masters, K., Lingard, L., & Maggio, L. A. (2025) · Medical Teacher · doi:10.1080/0142159x.2025.2607513 · Cited by 44 · see citing works
Introduction to generative artificial intelligence tools for academic article writing
Lim, J. S. (2026) · Science Editing · doi:10.6087/kcse.393 · Cited by 6 · see citing works
Using AI to improve peer review and research integrity in scientific journals
Bauchner, H., & Rivara, F. P. (2026) · Health Affairs Scholar · doi:10.1093/haschl/qxag028 · Cited by 6 · see citing works
How do authors want to use AI for review?
Lemberger, T., Mastboim, N. S., & Rechavi, O. (2026) · EMBO Reports · doi:10.1038/s44319-026-00725-4 · Cited by 3 · see citing works
When and how to disclose AI use in academic peer review
Masters, K., & Cleland, J. (2026) · Medical Teacher · doi:10.1080/0142159x.2025.2609741 · Cited by 3 · see citing works
Large Language Models in Scientific Publishing: Policy Landscape for Authors, Reviewers, and Editors
Park, S. H. (2026) · Korean Journal of Radiology · doi:10.3348/kjr.2026.0166 · Cited by 2 · see citing works
Kim, S., Yoon, D., Gashteovski, K., Suk, J., Baek, J., Aggarwal, P., Wu, I., Zaverkin, V., Petkoski, S., Schrider, D. R., et al. (2026) · arXiv · doi:10.48550/arxiv.2605.20668 · Cited by 1 · see citing works
Declare the use or non-use of artificial intelligence in peer-review: A relic of the past?
Rozencwajg, S., & Benhamou, D. (2026) · Anaesthesia Critical Care & Pain Medicine · doi:10.1016/j.accpm.2026.101812 · Cited by 1 · see citing works
Guellec, B. L., Bentegeac, R., Boyer, L., Celi, L. A., Boussat, B., & Hamroun, A. (2026) · Journal of Epidemiology and Population Health · doi:10.1016/j.jeph.2026.203798 · Cited by 1 · see citing works
ReviewBench: An Extensible Framework for Benchmarking Human and AI Manuscript Review
Khalil, N. N., Reed, T., & Ciccozzi, M. R. (2026) · doi:10.64898/2026.04.17.719279
“AI tools for manuscript review have also emerged [13, 14, 15, 16, 17], ranging from frontier large language models (LLMs) that have already been applied directly to peer review [18] to tool-augmented multi-agent systems with search capabilities [14, 19], such as Reviewer3.com ( R3).”
Artificial intelligence and journals: A perspective
Jani, N. J. (2025) · Journal of Integrated Standardized Homoeopathy · doi:10.25259/jish_203_2025
Knäusl, B., Solberg, T. D., Thorwarth, D., & Muren, L. P. (2026) · Physics and Imaging in Radiation Oncology · doi:10.1016/j.phro.2026.100920
Pereira, A. N. A. (2026) · Revista Portuguesa de Imunoalergologia · doi:10.32932/rpia.2026.03.178
The next unit of science: Is the scientific paper due to be replaced?
Requarth, T. (2026) · The Transmitter · doi:10.53053/iznq5416
Kim, S.-J. (2026) · Science Editing · doi:10.6087/kcse.400
The Challenges of Scientific Publishing: An Editor‐In‐Chief's Perspective
Zimmer, L., & Bousquet, P. (2026) · Fundamental and Clinical Pharmacology · doi:10.1111/fcp.70105
Jain, V., Roy, M., Regmi, A., Poduval, M., Kambhampati, S. B. S., & Jain, V. K. (2026) · Indian Journal of Orthopaedics · doi:10.1007/s43465-026-01834-x
Meidert, A. S. (2026) · British Journal of Anaesthesia · doi:10.1016/j.bja.2026.07.053
Aligning Editorial Review With the Pace of Language Models: Five Proposals for Oncology Publishing
Cantón, E. D. (2026) · JCO Clinical Cancer Informatics · doi:10.1200/cci-26-00245
Artificial intelligence in endoscopy: a call for joint leadership between ESGE and Endoscopy
Mori, Y., Hassan, C., Bergman, J. J. G. H. M., & and ESGE–Endoscopy AI Working Group (2026) · Endoscopy · doi:10.1055/a-2954-8396
Generative AI in scientific publishing: better for writing or for reviewing?
Mayol, J. M. (2026) · British journal of surgery · doi:10.1093/bjs/znag138
- Authors
- Manrai, A. K., Ouyang, D., Hogan, J. W., & Kohane, I. S.
- Affiliations
- Harvard University (US)
- Kaiser Permanente (US)
- Brown University (US)
- Venue
- NEJM AI
- Publisher
- Massachusetts Medical Society
- Published
- 2025-11-26
- Volume
- 2
- Issue
- 12
- Pages
- AIe2501175-AIe2501175
- ISSN
- 2836-9386 (ISSN-L)
- OpenAlex
- W4416717364
- Cited by
- 17
- References
- 10
- Citations by year
- 2026: 15 · 2025: 2
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NEJM AI (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Decision Sciences › Information Systems and Management › Interdisciplinary Research and Collaboration
- Keywords
- AI-assisted peer review, scholarly publishing, manuscript review, artificial intelligence in science
- MeSH terms
- Artificial Intelligence, Humans, Biomedical Research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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