# Retrieval, not hallucinations, will be the limiting factor for LLM-based clinical AI tools

arXiv: https://arxiv.org/abs/2607.24793 · HTML: https://mlsanity.com/paper/2607.24793/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 29 Jun 2026.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Dense Text Retrieval based on Pretrained Language Models: A Survey](https://mlsanity.com/paper/2211.14876/) (2022)
- [Don't Use LLMs to Make Relevance Judgments](https://mlsanity.com/paper/2409.15133/) (2024)
- [The Great Nugget Recall: Automating Fact Extraction and RAG Evaluation with Large Language Models](https://mlsanity.com/paper/2504.15068/) (2025)
- [On the Evaluation of Machine-Generated Reports](https://mlsanity.com/paper/2405.00982/) (2024)
- [Searching for Scientific Evidence in a Pandemic: An Overview of TREC-COVID](https://mlsanity.com/paper/2104.09632/) (2021)
- [Refocusing on Relevance: Personalization in NLG](https://mlsanity.com/paper/2109.05140/) (2021)
- [LLM-Evaluation Tropes: Perspectives on the Validity of LLM-Evaluations](https://mlsanity.com/paper/2504.19076/) (2025)
- [Auto-ARGUE: LLM-Based Report Generation Evaluation](https://mlsanity.com/paper/2509.26184/) (2025)

## Abstract (arXiv)

Discussions around large language model (LLM) errors in clinical artificial intelligence (AI) generally center around precision errors like hallucinations. This perspective, targeting both clinicians and AI researchers, seeks to shift that discussion to recall errors, particularly in retrieval of patient-level data needed for many clinical AI tools. The perspective outlines types of errors and mitigation strategies, describes research directions in LLMs and retrieval, and provides an overview of retrieval evaluation.