ArticleMissouri medicine
Pulmonary Embolism Education: Role of Generative Artificial Intelligence Models.
Article in Missouri medicine. 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
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.
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.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The growing use of generative artificial intelligence (AI) in the public sphere allows for a greater degree of disseminating information worldwide. For patients, there is a growing body of literature exploring how the generative artificial intelligence models can be used in improving the health literacy of patients, especially in cases of acute pulmonary embolism, where patients require deep, concise understanding of there disease and management. This study measured the readability of the generative responses created by publicly available AI models, and found that ChatGPT, Google Gemini, and Microsoft CoPilot do not currently meet the United States readability recommendations. Given the growing use of these models, future investigation on the longitudinal readability measures may help profile how these generative AI models adapt in their deep learning processes.
Indexed as
Identifiers
39697584PMC11651256What OpenQuestion 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 OpenQuestion graph.