Evidence map›Paper›PMID 36914733›Full record

ArticleScientific reports2023

Medical image captioning via generative pretrained transformers.

Alexander Selivanov, Oleg Y Rogov, Daniil Chesakov, Artem Shelmanov, Irina Fedulova, Dmitry V Dylov

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Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

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  6. Hallucination-Aware Multimodal Benchmark for Gastrointestinal Image Analysis with Large Vision-Language Models.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026
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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

6 authors.

Alexander Selivanov *Skolkovo Institute of Science and Technology, Bolshoy blvd., 30/1, Moscow, 121205, Russia.
Oleg Y Rogov *Skolkovo Institute of Science and Technology, Bolshoy blvd., 30/1, Moscow, 121205, Russia.
Daniil ChesakovSkolkovo Institute of Science and Technology, Bolshoy blvd., 30/1, Moscow, 121205, Russia.
Artem ShelmanovSkolkovo Institute of Science and Technology, Bolshoy blvd., 30/1, Moscow, 121205, Russia.
Irina FedulovaPhilips (Russia), Skolkovo Technopark 42, Building 1, Bolshoi Boulevard, Moscow, 121205, Russia.
Dmitry V DylovSkolkovo Institute of Science and Technology, Bolshoy blvd., 30/1, Moscow, 121205, Russia. d.dylov@skoltech.ru.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The proposed model for automatic clinical image caption generation combines the analysis of radiological scans with structured patient information from the textual records. It uses two language models, the Show-Attend-Tell and the GPT-3, to generate comprehensive and descriptive radiology records. The generated textual summary contains essential information about pathologies found, their location, along with the 2D heatmaps that localize each pathology on the scans. The model has been tested on two medical datasets, the Open-I, MIMIC-CXR, and the general-purpose MS-COCO, and the results measured with natural language assessment metrics demonstrated its efficient applicability to chest X-ray image captioning.

Indexed as

BenchmarkingRadiologyElectric Power SuppliesHumansLanguageThorax

Identifiers

PMID36914733
PMCPMC10010644

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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.