Evidence map›Paper›PMID 40901830›Full record

ArticlePloS one2025

Development, optimization, and preliminary evaluation of a novel artificial intelligence tool to promote patient health literacy in radiology reports: The Rads-Lit tool.

Rushabh H Doshi, Kanhai Amin, Shin Mei Chan, Manroop Kaur, Simar S Bajaj, Pavan Khosla, Veer T Kothari, Ali Mozayan, Irena Tocino, Sophie Chheang

Abstract readEvaluation Study
In one paragraph

Article in PloS one, 2025. 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

10 authors.

Rushabh H DoshiYale School of Medicine, New Haven, Connecticut, United States of America.ORCID https://orcid.org/0000-0002-2692-8195
Kanhai AminYale College, New Haven, Connecticut, United States of America.
Shin Mei ChanUCSF Department of Radiology & Biomedical Imaging, San Francisco, California, United States of America.
Manroop KaurDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, United States of America.
Simar S BajajHarvard College, Cambridge, Massachusetts, United States of America.
Pavan KhoslaYale School of Medicine, New Haven, Connecticut, United States of America.ORCID https://orcid.org/0000-0002-6966-6084
Veer T KothariYale School of Medicine, New Haven, Connecticut, United States of America.
Ali MozayanDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, United States of America.
Irena TocinoDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, United States of America.
Sophie ChheangDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Radiology reports are an integral part of patient medical records; however, these reports often contain complex medical terminology that are difficult for patients to comprehend, potentially leading to anxiety, misunderstanding, and misinterpretation. The development of user-friendly instruments to improve understanding is thus critically important to enhance health literacy and empower patients. In this study, we introduce a novel artificial intelligence (AI) interface, the Rads-Lit Tool, which can simplify radiology reports for patients using natural language processing (NLP) techniques. This manuscript presents the development process, methodology, and results of the Rads-Lit Tool, demonstrating its potential to simplify radiology reports across various examination types and complexity levels. Our findings highlight that patient-facing AI-driven tools can enhance patient health literacy and foster improved patient-provider communication in radiology.

Indexed as

Health LiteracyNatural Language ProcessingRadiologyCommunicationHumansProof of Concept StudyUser-Computer Interface

Identifiers

PMID40901830
PMCPMC12407389

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.