# Clinical Evidence Linkage From the American Society of Clinical Oncology 2024 Conference Poster Images Using Generative AI: Exploratory Observational Study

## TL;DR

This study shows how AI can extract and link citation data from clinical conference posters shared on social media, improving research discovery and analysis.

## Contribution

A novel AI method is introduced to extract and link citations from clinical conference posters, enhancing research visibility and analysis.

## Key findings

- An AI model successfully extracted and linked 63.4% of citations from ASCO 2024 posters to research databases.
- Manual validation confirmed 91.9% accuracy in a random sample of extracted citations.
- The method enabled rapid profiling of research topics and identification of key institutions and trials from posters.

## Abstract

Early-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.

This 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.

Poster 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.

We 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.

This 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.