Evidence map›Paper›PMID 41239178›Full record

ArticleEmergency radiology2026

Large language models as cost-conscious decision aids in emergency medicine: protocol support for imaging in lower back pain.

Siona Prasad, Arya S Rao, Mario V Russo, Soham Ghoshal, Elizabeth Roux, Chau Vo, John Kim, Joshua A Hirsch, Michael H Lev, Rajiv Gupta and 7 more

Abstract read
PubMed Publisher
In one paragraph

Article in Emergency radiology, 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

17 authors.

Siona Prasad *Harvard Medical School, Boston, MA, USA.
Arya S Rao *Harvard Medical School, Boston, MA, USA.
Mario V RussoHarvard Medical School, Boston, MA, USA.
Soham GhoshalHarvard Medical School, Boston, MA, USA.
Elizabeth RouxHarvard Medical School, Boston, MA, USA.
Chau VoHarvard Medical School, Boston, MA, USA.
John KimHarvard Medical School, Boston, MA, USA.
Joshua A HirschHarvard Medical School, Boston, MA, USA.
Michael H LevHarvard Medical School, Boston, MA, USA.
Rajiv GuptaHarvard Medical School, Boston, MA, USA.
William H MarksMedically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), Mass General Brigham, Boston, MA, USA.
Amine KorchiRéseau Radiologique Romand, Région de Genève, Genève, Suisse.
Adam LandmanBrown University Health, Providence, USA.
Bernardo C BizzoHarvard Medical School, Boston, MA, USA.
Ali S RajaHarvard Medical School, Boston, MA, USA.
Keith J DreyerHarvard Medical School, Boston, MA, USA.
Marc D SucciHarvard Medical School, Boston, MA, USA. msucci@mgh.harvard.edu.ORCID http://orcid.org/0000-0002-1518-3984

Funding

Medical Scientist Training ProgramT32GM144273 · NIGMS · HARVARD MEDICAL SCHOOL · PI David Shumway Jones, Jacqueline A. Lees · 2022 to 2026
$14.7M
NIGMS NIH HHS T32 GM144273NIGMS NIH HHS T32GM144273
6 · The paper itself

Abstract

backgroundAppropriate utilization of imaging in the emergency department (ED) remains an important determinant of health care expenditures and patient throughput. LLMs have demonstrated potential as clinical decision support tools, and may aid in cost-conscious imaging triage in the ED. We aim to evaluate the effectiveness of LLMs in providing accurate, cost-conscious imaging recommendations for ED patients with lower back pain.

methods422 patients presented between December 2017 and June 2018 to the ED of a ~ 1000-bed major urban academic medical center with a chief complaint of lower back pain and received a lumbar spine MRI. The primary outcomes were Hoy et al. (Best Pract Res Clin Rheumatol 24(6):769-781, 2010) alignment of Generative Pre-Trained Transformer 4 (GPT-4)-generated imaging recommendations with ACR criteria, by raw accuracy and Cohen's κ, and Hoy et al. (Arthritis Rheum 64(6):2028-2037, 2012) professional service resource utilization quantified in work relative value units (wRVUs) under real-world clinical decisions, GPT-4 recommendations, and hypothetical 100% ACR adherence scenarios. GPT-4 was compared with real-world clinical decisions for imaging of lower back pain based on ED triage notes. Resource utilization was analyzed to assess potential savings from GPT-4 recommendations.

resultsGPT-4 model generated ACR-concordant recommendations for 72.0% (304/422) of cases and demonstrated significant alignment with ACR criteria as measured by Cohen's κ (0.42,95% CI: [0.35,0.48], p < 0.05). Actual resource utilization was 629 wRVUs. GPT-4 would have used 481.74 wRVUs. 100% adherence to the ACR criteria would have used 481.86 wRVUs.

conclusionsOur results support LLMs as possible tools for cost-conscious radiologic decision making in ED back pain evaluation.

Indexed as

Decision Support TechniquesEmergency Service, HospitalLow Back PainMagnetic Resonance ImagingAdultFemaleHumansLarge Language ModelsMaleMiddle AgedArtificial intelligenceClinical decision supportCost-Conscious recommendationsEmergency departmentImaging triageLLMsLower back painResource utilization

Identifiers

PMID41239178

What OpenQuestion 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 OpenQuestion graph.