ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2026
Assessing Multimodal AI for Visual Information Extraction of Pharmacology.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 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.
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Authors and funding
4 authors.
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Abstract
While Americans are using herbal dietary supplements (natural products) more than ever, the consumption of natural products with prescription drugs can lead to harmful interactions. Pharmacovigilance of natural products depends on careful expert review and interpretation of a wide variety of evidence. In prior work, we demonstrated the value of knowledge graph (NP-KG) for assisting with natural product safety investigations. However, scaling the NP-KG from 33 natural products to the thousands on the market requires computer-assisted data extraction, particularly from visual elements (figures or tables) of pharmacology literature. We evaluated the accuracy and resilience of 3 open-weights and 7 closed-weights multimodal models by performing visual information extraction from select tables and images. The best performing models could accurately extract 90% of tabular data and 45% of data reported figures with a modified relative error rate of 0.05. Image resolution and information density were primary hindrances to better extraction performance.
Identifiers
42317821PMC13274268What Socratic holds
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 Socratic graph.