Critique Agents in High-Stakes Counseling Summaries
Abstract
A central question for maternal-infant evidence communication is not simply whether an output is accurate, but whether it survives scrutiny of human accountability. This literature-based conceptual analysis does not report a new experiment. The focal evidence concerns how evidence concerning HIV, pregnancy, and infant feeding should be interpreted in ongoing clinical communication. Accountability requires knowing who can authorize an action, challenge a summary, and respond to error. A critic model does not become an accountable professional by producing a second opinion. Map responsibility and escalation routes. Test whether unsupported claims can be stopped before they enter a consequential decision and whether correction reaches affected users. The proposed comparison concerns reasoning review, sensitive communication, professional oversight; it would test whether the specific failure scenario is detected without erasing legitimate uncertainty. Implications are restricted to the evidence roles relevant to maternal-infant evidence communication, leaving broader efficacy and deployment claims unresolved.
References
Yu, Xiao, et al. “HIV, Pregnancy, and Infant Feeding.” JAMA, vol. 336, no. 2, 2026, p. 169, https://doi.org/10.1001/jama.2026.6033.
Sang, Yinghao. “Towards Explainable RAG: Interpreting the Influence of Retrieved Passages on Generation.” 2025 4th International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC), IEEE, 2025, pp. 397–400, https://doi.org/10.1109/raiic65850.2025.11170170.
Rothman, Kenneth J., Sander Greenland, and Timothy L. Lash. Modern Epidemiology. 3rd ed., Lippincott Williams & Wilkins, 2008.
Benjamini, Yoav, and Yosef Hochberg. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society: Series B, vol. 57, no. 1, 1995, pp. 289–300.
Rajkomar, Alvin, Jeffrey Dean, and Isaac Kohane. “Machine Learning in Medicine.” New England Journal of Medicine, vol. 380, 2019, pp. 1347–58.
Wiens, Jenna, et al. “Do No Harm: A Roadmap for Responsible Machine Learning for Health Care.” Nature Medicine, vol. 25, 2019, pp. 1337–40.
Guo, Chuan, et al. “On Calibration of Modern Neural Networks.” Proceedings of the 34th International Conference on Machine Learning, 2017, pp. 1321–30.
Robertson, Stephen, and Hugo Zaragoza. “The Probabilistic Relevance Framework: BM25 and Beyond.” Foundations and Trends in Information Retrieval, vol. 3, no. 4, 2009, pp. 333–89.
Lewis, Patrick, et al. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 9459–74.
Karpukhin, Vladimir, et al. “Dense Passage Retrieval for Open-Domain Question Answering.” Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020, pp. 6769–81.
National Academies of Sciences, Engineering, and Medicine. Reproducibility and Replicability in Science. National Academies Press, 2019.
Wilkinson, Mark D., et al. “The FAIR Guiding Principles for Scientific Data Management and Stewardship.” Scientific Data, vol. 3, 2016, article 160018.