Generative artificial intelligence (AI) has rapidly transformed image and visual processing, progressing from early generative adversarial networks to advanced multimodal models such as DALL-E and diffusion-based techniques. Recent studies highlight advancements in text-to-image generation and semantic image synthesis [1], showcasing improved realism and contextual understanding. The application of AI in visual communication design [2] and generative visual intelligence [3] further demonstrates AI’s expanding role in creative fields. As generative models continue to evolve, their impact extends beyond art and entertainment into scientific visualization and human-computer interaction.

Altmetric and Dimensions are two powerful research analytics platforms that provide insights into scholarly impact and research discovery. Altmetric specifically tracks the online attention a research output receives. It aggregates mentions from a wide variety of nontraditional sources, including social media (such as X, formerly known as Twitter, the platform used in this study), public policy documents, mainstream news outlets, podcasts, blogs, and others. This provides a real-time gauge of how research is being discussed and shared among both academic and public audiences, reflecting its societal or “alternative” impact [4]. Dimensions, on the other hand, is a comprehensive research database that integrates publications, grants, patents, clinical trials, and policy documents, enabling in-depth bibliometric analysis and research discovery [5]. Unlike traditional citation-based metrics, these platforms offer a broader perspective on research influence, making them an essential complement for researchers, institutions, and policymakers.

Extracting citations from research conference posters presents unique challenges due to their unstructured format, multimodal content, and limited metadata. Recent advances in scholarly document processing and bibliographic reference parsing have aimed to improve citation extraction from various scientific sources. Studies on neural network models for scholarly document processing [6] and automated bibliographic reference parsers provide insights into leveraging AI for structured citation extraction. Additionally, research on semantic entity extraction from academic databases [7] highlights challenges related to data acquisition and accuracy. These findings suggest that, while existing methods improve citation extraction in formal publications, further work is needed to adapt them for conference posters.

The primary goal of this study was to test whether it is possible to extract citations from conference poster pictures using a large language model (LLM). The secondary objective was to explore what extracted data can be used to link the poster citations to Dimensions and Altmetric data.

To examine whether generative AI can extract information from conference posters, we used the Google Cloud Vertex AI environment and tested several LLMs. Through trial and error and comparison of the quality of the output, we used the Gemini 2.0 Flash (Google; experimental) version for this study. The prompt used is presented in Textbox 1.

Figure 1 shows an example of a poster image with the locations from which information was extracted. The JSON format information returned by the prompt is provided in Textbox 2.

For the purposes of this study, we selected poster images posted on X associated with the American Society of Clinical Oncology (ASCO) 2024 conference. We used the Tweepy Python library [8] to do this using the following search terms: “American Society of Clinical Oncology Annual Meeting 2024” OR #ASCO24 OR #ASCO2024

Due to the high metadata availability of ASCO conferences, we also decided to include a smaller, nonclinical conference to double-check accuracy and citation linkage. For this subanalysis, we included all International Conference on Science, Technology, and Innovation Indicators (STI) conferences from 2018 to 2024 (Multimedia Appendix 1).

For the image to be considered for inclusion, it had to be at least 100 kB and mention the word “poster” in the body of the post to optimize the identification of posts that contained a poster image to input into the LLM.

To be able to do this at scale, we used the following toolkits: Python (Python Software Foundation); Jupyter Notebooks; and Google Cloud Vertex AI, Cloud Storage, and BigQuery. The pipeline is described in Figure 2.

From the AI response containing the JSON values, we extracted and linked data to our Dimensions database using an algorithm that considered the following information:

Title matching involved 2 calculations. The first was match percentage to identify the textual overlap between citation and publication titles while accounting for differences in word counts. First, titles were tokenized to calculate word counts and matching words. A match percentage was derived by dividing matching words by the citation title word count. To address noise from large word count discrepancies, an adjustment penalized cases with significant differences, weighting matches in which word counts aligned more closely.

The second calculation was title score=match percentage×number of words. This was also calculated to differentiate strong matches in low-count titles that could be present in many different publications due to commonality.

The primary and simplest method for matching were the persistent identifiers. The next option was the title matching algorithm, where only matches with an adjusted match percentage above 70% were considered for use, with those with a percentage of >93% used on its own and those with a percentage between 70% and 90% used in conjunction with the above indicators. Table 1 describes the algorithm used by priority order.

In the case of multiple Dimensions publication matches, we used the highest match percentage or title score. After all these algorithms had run, we retrieved all the Dimensions IDs linked to DOI, PubMed identifier (PMID), clinical trial registration, title, and journal. For the final ID matching decision, we applied the following rules (see the example in Table 2). If multiple IDs were retrieved, we selected the most frequent ID as the final ID. If only 1 ID was retrieved, that was the final ID. If multiple IDs were retrieved but not repeated, we used the following priority list: (1) retrieved DOI, (2) retrieved PMID, (3) retrieved clinical trial ID, (4) retrieved title ID, and (5) retrieved journal ID.

To test the accuracy of the citation extraction and linkage, we performed a random check of examples, first, for whether the citation was correct regarding the image information. If this information was false, we checked whether it was the fault of the AI extraction (eg, extracting the wrong DOI and hallucinating an identifier) and whether it was the fault of the matching algorithm (eg, when the AI extracted the information correctly but we could not match it to our Dimensions and Altmetric data).

To ensure that we had at least 100 manually confirmed citations, we randomly selected 150 examples from our results dataset. For our subanalysis, due to the small sample size, all citations were manually confirmed (Multimedia Appendix 1).

Due to the observational nature of publicly available data, this study was exempt from ethics approval and informed consent. The example image of the poster in Figure 1 is from the main author of this manuscript and is shared with his permission.

This study included, in total, 115,714 X posts from the ASCO 2024 conference, including 23,548 (20.4%) original posts, 4044 (3.5%) quoted posts, and 88,122 (76.2%) reposts. Of these 115,714 posts, 18,218 (15.7%) included at least one attachment, with 16,574 (14.3%) being labeled as a photo or image. After applying our eligibility criteria (image of >100 kB and text of the post including the word “poster”), we narrowed this down to 793 images to run our AI model. Of these 793 images, a further 94 (11.9%) were excluded from the analysis as the quality was too low to be readable by the AI, and 130 (16.4%) were considered posters by the AI, all confirmed manually (Figure 2). The final dataset included 651 readable poster images, with 1117 potential citations identified by our AI model.

In total, we managed to link 708 poster citations (n=332, 46.9% coming from the main poster information and n=376, 53.1% from the reference section or content of the poster, with an average of 1.66, SD 2.58 and median of 1, IQR 0-3 references per poster) to their respective publication information (616 unique publications) using different matching methods as described in Figure 3.

We were unable to link over one-third (409/1117, 36.6%) of the potential citations to their respective Dimensions publication information due to incomplete information or to the information retrieving multiple studies, as in the following examples:

Our subanalysis results can be found in Multimedia Appendix 1.

Of the 708 matches, 150 (21.2%) were randomly selected for manual curation and confirmation of accuracy. Of these, after excluding the posters for which we were unable to confirm a match (not of sufficient quality to be readable by the human eye), we manually reviewed 135 poster citations, of which 124 (91.9%) were correct matches and 11 (8.1%) were incorrect matches. A breakdown by matching method can be found in Table 3.

Our subanalysis of the STI conferences yielded similar accuracy results (Multimedia Appendix 1).

A total of 616 unique research outputs (n=580, 94.2% articles and n=36, 5.8% clinical trial registrations) were matched, with a few being mentioned in more than one conference poster (Table 4).

The 616 research outputs linked as per Dimensions document classification [5] included 361 (58.6%) conference abstracts, 143 (23.2%) research articles, 39 (6.3%) review articles, 36 (5.8%) clinical trial registrations, 13 (2.1%) letters to the editor, 10 (1.6%) unknown, 4 (0.6%) other types of journal content, 4 (0.6%) correction or erratum notes, 3 (0.5%) editorials, 1 (0.2%) conference paper, 1 (0.2%) other type of conference content, and 1 (0.2%) reference work. Interestingly, some of the main posters were then published (either as an abstract or full publication) and achieved a significant level of attention (according to the Altmetric score), as shown in Table 5.

By linking the poster citations to Dimensions publication and clinical trial information, it is possible to conduct several analyses and extract interesting signals, for example, using research categories. In Figure 4, we used the International Cancer Research Partnership cancer type taxonomy to be able to quickly identify the main cancer types mentioned in the poster citations.

We then used the authors’ affiliations to calculate the most frequently referenced institutions in shared posters (this could include the poster itself or inside reference), with the following top three for research articles: (1) Dana-Farber Cancer Institute (35 poster citations), (2) the University of Texas MD Anderson Cancer Center (30 poster citations), and (3) Memorial Sloan Kettering Cancer Center (24 poster citations).

The following were the top three for clinical trials: (1) SWOG Cancer Research Network (4 poster citations), (2) University of North Carolina Lineberger Comprehensive Cancer Center (3 poster citations), and (3) National Cancer Institute (3 poster citations).

Focusing on clinical trials, by linking cited clinical trials to their respective Dimensions information, we were able to explore some interesting information and signals on the status of these trials; for example, in Figures 5 and 6, we outline the active years and overall status and phase for all 36 matched clinical trial registrations.

The final dataset included 651 images with readable posters and 1117 potential citations. These images came from 240 X profiles and 347 unique X posts, ranging from a minimum of 1 to a maximum of 4 images per post.

Most of the included posts (330/347, 95.1%) were original posts, with most (318/347, 91.6%) originating during the ASCO 2024 conference period (May 31, 2024, to June 4, 2024), as shown in Figure 7.

The top three profiles sharing posters were the following: (1) DFCI_BreastOnc (14 posts with 16 poster images), (2) CSCancerCenter (7 posts with 7 poster images), and (3) MDAndersonNews (6 posts with 6 poster images).

This study aimed to explore the use of an LLM to extract both the main citations as well as references (where available) from poster images. It also explored how were different strategies good at trying to link the free-text extracted poster citation data to the Dimensions and Altmetric databases. As the ASCO conferences are well indexed, mostly in the Journal of Clinical Oncology, we decided to also include a subanalysis of a smaller, nonclinical conference that matched similar results. The LLM and algorithm can be tested on any poster at any conference.

This exploratory study demonstrated that it is indeed possible to extract structured citation data from poster images using LLMs and relatively easy to link to the Dimensions and Altmetric databases in cases in which the DOI or other persistent identifiers are present; however, it became significantly more challenging for references without these identifiers, and we tried to find creative solutions using the available extracted data points. Our random sampling accuracy testing highlighted that the LLM was particularly good at extracting the correct information from both the main poster and references (or other poster content citations), with the former usually being easier to link to the publication through the title and authors. When an identifier was provided in the references section, it was also quite straightforward to link it to the respective publication; however, it was more challenging to do so when only limited information was provided, for example, just the first author’s last name with “et al” and the journal (with the year in parentheses) as it could provide erroneous linkage to multiple publications.

For most posters (124/135, 91.9%), the LLM extracted the correct information, and using our Dimensions database, we managed to successfully link most of the citations with complete data provided in the poster. In some cases, we even managed to link a poster to both its respective clinical trial registration [28] and publication [29]. Interestingly, during the manual random sample, in a lot of cases in which a picture was taken of several posters and people at the same time, the AI still managed to extract enough partial information to be able to link to the publication data. According to our random sampling analysis, most of the errors were not from the AI extraction but from our matching algorithm, and several future improvements were identified. First, journal+year+volume is not sufficient and should only be used as a last resort. Many of the errors encountered were in this category, and despite this being already one of the most recent matching methods within our algorithm, we should consider other, more recent methods before this one. Second, even when using identifiers, we should consider other methods of matching in conjunction with them. In some cases, the AI seemed to hallucinate a DOI that matched the wrong publication. In other cases, the DOI was wrong (we noticed this particularly in older studies).

Our matching algorithm was relatively basic and needs further improvement as it was built through trial and error until an optimal level of accuracy was achieved using the selected method. Albeit limited, this algorithm was sufficient to prove our hypothesis successfully. In future studies and applications of this technology, we will consider other matching methods such as using multiple types of author information, sponsors, funders, organizations, and the content of the poster itself (which we can try to match to an abstract, for example). However, to the best of our knowledge, this is the first study to successfully extract poster image information and link it to a research repository. Despite our hypothesis’s success, poster citations should be used thoughtfully to avoid citation duplication (eg, if the conference poster abstract is published online). Therefore, it is our view that these poster citations may not be suitable to be used as formal citations but should perhaps be considered as attention mentions instead (a debatable opinion). As the images were shared through an X post, we believe that the correct way of presenting this information would be as a research attention (similar to when someone posts a publication link or DOI), and this would be a novel way to track conference attention.

Alternative metrics are becoming increasingly important, and tools such as Altmetric, PlumX, and others are increasing their capabilities in capturing different types of research attention [30]. Conference posters can contain novel, important information that often goes unpublished or share early insights on studies (eg, interim clinical trial results) months or years before the final publication [31]. We used the ASCO 2024 conference example to highlight that, due to the agile nature of health care practice (see the COVID-19 pandemic as an example), having access to timely poster citation information may be important for clinical and strategic decisions.

By linking poster citations to their respective research publication or clinical trial information, it is possible to extract interesting insights and signals to explore at scale. In this study, we used a narrow, limiting example as a proof of concept; however, future research will test this at scale with multiple conferences through the years in several fields (including new ones such as humanities, social sciences, and engineering), testing practical, economical, and computational feasibility of poster or image citation extraction.

We noticed in some rare cases that, if a poster was about a systematic review that referenced other publications in the content, the LLM often assumed that to be a poster mention. While not the desired behavior, it might extend the capabilities of this model and the number of publications identified as cited in that poster (which is, in fact, correct and in accordance with our study objectives).

ASCO posters are routinely published by the Journal of Clinical Oncology and issued a DOI, and therefore, the metadata are easy to obtain. Many other important conferences do not publish material with DOIs and would be considerably harder to match. To test this, we used a smaller nonclinical conference as a subanalysis (Multimedia Appendix 1) that yielded similar results.

During the random sampling accuracy testing, we noted that, when the quality of the poster was poor but passable (according to the is_readable variable returned by the AI), it was hard for the human eye to confirm its accuracy, and therefore, the poster was not included in the random sampling analysis. Future work should include thresholds of quality for accuracy checking. We also did not perform a sample size calculation for the random sampling analysis, and the 150-sample check was an arbitrary number that we felt was sufficient to be representative of our data together with the STI conference subanalysis, in which we conducted the accuracy check in all citations due to the small sample.

Another limitation is that we did not fully explore linking methods between the data retrieved from the image and our datasets. There might be other creative ways to link more publications to our (and other) data. For example, in this study, we struggled to match publications in which only the first author, year, and journal were shared, and there might be other clever ways to match these in our dataset (by increasing context using general poster information and perhaps another AI model to help select the correct publication within a list). Another example is when the LLM hallucinated while extracting a persistent identifier (eg, DOI or PMID) and other information was provided. A fallback method could be added to the algorithm to identify a nonmatch from the identifier and prioritize other matching methods instead. Future work will involve improving our algorithm to maximize poster citation matching.

This study also opens the door to other image citation extraction algorithms, for example, of conference presentations (or available online presentation slides in which research mention and citation is likely) or presentation video analysis and citation extraction using AI.

The objective of this study was to confirm the hypothesis that AI can be used to extract citations from poster images. Our study concluded that it is not only possible but also straightforward to do it in a scalable way, with most of the effort lying in accurately connecting these citations to the correct publication data through different methods. This study opens the door for future use of AI on image data extraction to collect scholarly mentions and citations from novel sources, as well as other relevant clinical data from conference posters.