ArticleJMIR AI2026
Clinical Evidence Linkage From the American Society of Clinical Oncology 2024 Conference Poster Images Using Generative AI: Exploratory Observational Study.
Article in JMIR AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly-stage clinical findings often appear only as conference posters circulated on social media. Because posters rarely carry structured metadata, their citations are invisible to bibliometric and alternative metric tools, limiting real-time research discovery.
objectiveThis study aimed to determine whether a large language model can accurately extract citation data from clinical conference poster images shared on X (formerly known as Twitter) and link those data to the Dimensions and Altmetric databases.
methodsPoster images associated with the 2024 American Society of Clinical Oncology conference were searched using the terms "#ASCO24," "#ASCO2024," and the conference name. Images ≥100 kB that contained the word "poster" in the post text were retained. A prompt-engineered Gemini 2.0 Flash model classified images, summarized posters, and extracted structured citation elements (eg, authors, titles, and digital object identifiers [DOIs]) in JSON format. A hierarchical linkage algorithm matched extracted elements against Dimensions records, prioritizing persistent identifiers and then title-journal-author composites. Manual validation was performed on a random 20% sample.
resultsWe searched within 115,714 posts and 16,574 images, of which 651 (3.9%) met the inclusion criteria, and we obtained 1117 potential citations. The algorithm linked 63.4% (708/1117) of the citations to 616 unique research outputs (n=580, 94.2% journal articles; n=36, 5.8% clinical trial registrations). Manual review of 135 randomly sampled citations confirmed correct linkage in 124 (91.9%) cases. DOI-based matching was mostly flawless; most errors occurred where only partial bibliographic details were available. The linked dataset enabled rapid profiling of topical foci (eg, lung and breast cancer) and identification of the most frequently referenced institutions and clinical trials in shared posters.
conclusionsThis study presents a novel artificial intelligence-driven methodology for enhancing research discovery and attention analysis from nontraditional clinical scholarly outputs. The American Society of Clinical Oncology was used as an example, but this methodology could be used for any conference and clinical poster.
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Registered trials
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.