ReviewJournal of lipid and atherosclerosis2025
Application of Generative Artificial Intelligence in Dyslipidemia Care.
Review in Journal of lipid and atherosclerosis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- The Risks to Patients Associated with Using Artificial Intelligence Tools in Pharmacy Practice: A Scoping Review.Pharmacy (Basel, Switzerland) · 2026Review
- Small dense LDL: An underestimated driver of atherosclerosis (Review).Molecular medicine reports · 2025Review
- Technologies, opportunities, challenges, and future directions for integrating generative artificial intelligence into medical education: a narrative review.Ewha medical journal · 2025Article
- Prompt engineering for single-best-answer multiple-choice questions in licensing examinations: a narrative review with a case study involving the Korean Medical Licensing Examination.Journal of educational evaluation for health professions · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dyslipidemia dramatically increases the risk of cardiovascular diseases, necessitating appropriate treatment techniques. Generative AI (GenAI), an advanced AI technology that can generate diverse content by learning from vast datasets, provides promising new opportunities to address this challenge. GenAI-powered frequently asked questions systems and chatbots offer continuous, personalized support by addressing lifestyle modifications and medication adherence, which is crucial for patients with dyslipidemia. These tools also help to promote health literacy by making information more accessible and reliable. GenAI helps healthcare providers construct clinical case scenarios, training materials, and evaluation tools, which supports professional development and evidence-based practice. Multimodal GenAI technology analyzes food images and nutritional content to deliver personalized dietary recommendations tailored to each patient's condition, improving long-term nutritional management for those with dyslipidemia. Moreover, using GenAI for image generation enhances the visual quality of educational materials for both patients and professionals, allowing healthcare providers to create real-time, customized visual aids. To apply successfully, healthcare providers must develop GenAI-related abilities, such as prompt engineering and critical evaluation of GenAI-generated data.
Indexed as
Identifiers
What 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.