AI Consensus Validation in Biomedical Research: A Review, Conceptual Framework,and Future Directions

Abstract

Introduction: The exponential growth of biomedical research has strained traditional peer review, creating delays and inconsistencies. Artificial intelligence (AI), particularly large language models (LLMs), offers potential for scalable, reproducible assessment of scientific content. Current tools remain siloed and lack a framework to leverage agreement among diverse models as a validation signal. Methods: We conducted a narrative and conceptual review of literature (2021 onward) from PubMed, arXiv, and IEEE Xplore, focusing on AI-assisted peer review, LLM evaluation, and validation frameworks. We propose AI Consensus Validation (AICV), where agreement among diverse LLMs serves as an early indicator of clarity, novelty, relevance, and conceptual soundness. Results: Our review identifies key gaps in the current landscape, including epistemic homogeneity, fragmentation, and opacity. AICV shifts the paradigm from single-model prediction to multi-model consensus, using convergence as a marker of epistemic robustness. We present the operational workflow of AICV and illustrate its application as a proof-of-concept with three biomedical abstracts, demonstrating its capacity to differentiate submissions based on convergence patterns. Discussion: AICV could be integrated into journal pre-screening, grant triage, and researcher feedback. Challenges include model biases, explainability gaps, and suppressing disruptive ideas. We outline a phased validation roadmap to translate AICV from concept to a trusted tool. Conclusion: AICV represents a promising middle path between fully manual peer review and opaque automation. Leveraging epistemic diversity across LLMs may accelerate biomedical innovation while preserving rigor and creativity if implemented with attention to bias, transparency, and human oversight.

Received: 26 January 2026 | Revised: 17 March 2026 | Accepted: 21 April 2026

Conflicts of Interest

The author declares that he has no conflicts of interest to this work.

Data Availability Statement

This article is a review of previously published literature. No new data were generated or analyzed in this study. All information supporting the findings of this review is available within the cited
references and in Appendix A.

Author Contribution Statement

Tan Aik Kah: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.