AI-assisted prescreening of biomedical research proposals: ethical considerations and the pilot case of “la Caixa” Foundation
Open accessAbstract
The “la Caixa” Foundation has been experimenting with artificial intelligence (AI)-assisted decision-making geared toward alleviating the administrative burden associated with the evaluation pipeline of its flagship funding program, piloting an algorithm to detect immature project proposals before they reach the peer review stage, and suggest their removal from the selection process to a human overseer. In this article, we explore existing uses of AI by publishers and research funding organizations to automate their selection pipelines, in addition to analyzing the conditions under which the focal case corresponds to a responsible use of AI and the extent to which these conditions are met by the current implementation, highlighting challenges and areas of improvement.
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After La Caixa's triage, proposals judged weak by all three (LLM and two reviewers) are rejected without a full review.
“After this, proposals judged weak by all three (LLM and two reviewers) are rejected without a full review”
From Can Large Language Models Evaluate Grant Proposal Quality? Revisiting the Wennerås and Wold Peer Review Data (2026) · cites this paper as Carbonell Cortés et al., 2024
The paper says proposals flagged by the models are checked by two eligibility reviewers; if either reviewer has reasonable doubt, the proposal is sent to peer review (e49-8). This supports removal without full peer review when the reviewers agree. However, §4.2 says the automated decision requires agreement among three NLP models, not one LLM, so the claim misstates who the three decision-makers are. (LLM, conf=0.88)
Experimental — accuracy not yet measuredLa Caixa employs large language models to triage proposals, with the lowest scoring proposals being sent to human experts for quick reviews.
“La Caixa employs Large Language Models (LLMs) to triage proposals, with the lowest scoring proposals being sent to human experts for quick reviews.”
From Can Large Language Models Evaluate Grant Proposal Quality? Revisiting the Wennerås and Wold Peer Review Data (2026) · cites this paper as Carbonell Cortés et al., 2024
Section 4.2 says three NLP models flag proposals unanimously classified in the bottom score class. Page e49-8 says two human eligibility reviewers assess every flagged proposal, and Section 5.2 describes their evaluation as superficial, supporting the quick-review characterization. The paper identifies the deployed models as BioLinkBERT-Base and BioELECTRA-Base variants, but does not identify them as large language models; its discussion of LLMs in Section 1.2 does not establish that La Caixa uses them for this process. (LLM, conf=0.85)
Experimental — accuracy not yet measuredLa Caixa's use of LLMs has led to a small reduction in the number of reviews needed.
“This has led to a small reduction in the number of reviews needed (Carbonell Cortés et al., 2024).”
From Can Large Language Models Evaluate Grant Proposal Quality? Revisiting the Wennerås and Wold Peer Review Data (2026) · cites this paper as Carbonell Cortés et al., 2024
Section 4.1 (p. e49-8) reports that the AI-assisted process removed 63 of 493 proposals from peer review in the HR23 call, supporting a reduction in reviews needed. But Section 4.2 identifies the prescreening tools as three specific NLP models, not as LLMs, so the claim's description of the technology is not established by this source. (LLM, conf=0.83)
Experimental — accuracy not yet measuredOne funder now uses artificial intelligence for reviewing.
“one now uses it for reviewing.”
From Can Large Language Models Evaluate Grant Proposal Quality? Revisiting the Wennerås and Wold Peer Review Data (2026) · cites this paper as Carbonell Cortés et al., 2024
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Cited by
Sorted by citations
Sandström, U., & Thelwall, M. (2026) · Journal of Data and Information Science · doi:10.1515/jdis-2026-0048 · Cited by 1 · see citing works
- Authors
- Carbonell Cortés, C., Parra-Rojas, C., Pérez-Lozano, A., Arcara, F., Vargas-Sánchez, S., Fernández-Montenegro, R., Casado-Marín, D., Rondelli, B., & López-Verdeguer, I.
- Affiliations
- SIRIS Academic (ES)
- Venue
- Data & Policy
- Publisher
- Cambridge University Press (CUP)
- Published
- 2024
- Volume
- 6
- ISSN
- 2632-3249 (ISSN-L)
- OpenAlex
- W4403700580
- Cited by
- 15
- References
- 9
- Citations by year
- 2026: 10 · 2025: 4 · 2024: 1
- FWCI
- 6.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Data & Policy (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Zenodo (CERN European Organization for Nuclear Research) (cc-by)
- Funders
- Wellcome Trust
- Russian Science Foundation
- Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Decision Sciences › Information Systems and Management › Research, Science, and Academia
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Keywords
- AI-assisted decision-making, research funding, grant proposal evaluation, peer review, responsible AI use, research ethics, administrative burden, algorithmic bias, human oversight
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