Evidence map›Paper›PMID 42396645›Full record

ArticleJournal of hospital medicine2026

The 5R's of large language model-assisted diagnosis: A practical framework for hospitalists.

Peter Barish, Andrew D Auerbach, Sumant R Ranji

Abstract read
In one paragraph

Article in Journal of hospital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Peter BarishDivision of Hospital Medicine, University of California San Francisco, San Francisco, California, USA.
Andrew D AuerbachDivision of Hospital Medicine, University of California San Francisco, San Francisco, California, USA.
Sumant R RanjiDivision of Hospital Medicine, San Francisco General Hospital, University of California San Francisco, San Francisco, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diagnostic error remains a major patient safety challenge in hospital medicine. Large language models (LLMs) are increasingly used by clinicians to aid in diagnosis, yet most lack a structured approach for doing so safely and effectively. In this piece, we propose a practical, clinician-centered framework for LLM-assisted diagnosis. The 5R's Framework promotes a human-in-the-loop approach that harnesses LLMs to reduce diagnostic error while preserving critical reasoning skills.

Indexed as

Diagnostic ErrorsHospitalistsLarge Language ModelsHumans

Identifiers

PMID42396645
PMCPMC13636112

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.