arXiv — NLP / Computation & Language · · 3 min read

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

Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.

Computer Science > Information Retrieval

arXiv:2607.24793 (cs)
[Submitted on 29 Jun 2026]

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

View a PDF of the paper titled Retrieval, not hallucinations, will be the limiting factor for LLM-based clinical AI tools, by Kirk Roberts and Steven Bedrick and Kurt Miller and William R. Hersh and Hongfang Liu
View PDF HTML (experimental)
Abstract: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.
Comments: Perspective piece
Subjects: Information Retrieval (cs.IR); Computation and Language (cs.CL)
Cite as: arXiv:2607.24793 [cs.IR]
  (or arXiv:2607.24793v1 [cs.IR] for this version)
  https://doi.org/10.48550/arXiv.2607.24793
arXiv-issued DOI via DataCite

Submission history

From: Kirk Roberts [view email]
[v1] Mon, 29 Jun 2026 02:31:16 UTC (220 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled Retrieval, not hallucinations, will be the limiting factor for LLM-based clinical AI tools, by Kirk Roberts and Steven Bedrick and Kurt Miller and William R. Hersh and Hongfang Liu
  • View PDF
  • HTML (experimental)
  • TeX Source

Additional Features

Current browse context:

cs.IR
< prev   |   next >
Change to browse by:

References & Citations

Loading...

BibTeX formatted citation

loading...
Data provided by:

Bookmark

BibSonomy Reddit
Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos

Demos

Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers

Recommenders and Search Tools

Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Discussion (0)

Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.

Sign in →

No comments yet. Sign in and be the first to say something.

More from arXiv — NLP / Computation & Language