ReviewJMIR AI2026
Enhancing Patients' Informed Consent Through AI: Systematic Review.
Review in JMIR AI, 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
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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Informed consent is a cornerstone of medical ethics, ensuring that patients understand the risks, benefits, and alternatives of procedures before making health care decisions. However, challenges such as complex medical language, time constraints, and variations in patient literacy often hinder comprehension. Recent advancements in AI offer new opportunities to improve the informed consent process. Objective: This systematic review aims to assess AI's effectiveness in enhancing patient understanding and decision-making during the informed consent process. Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a comprehensive literature search was conducted in PubMed, Embase, and the Cochrane Library to identify studies published in the past 5 years on AI's role in informed consent. Subsequently, the reference lists of selected papers were manually reviewed to include any additional relevant studies. Descriptive and statistical analyses were conducted to evaluate AI's effectiveness, along with tests of homogeneity to assess the feasibility of a meta-analysis. Results: A total of 33 studies published between 2020 and 2025 were included, categorized into 3 domains: AI-generated patient education (n=18, 54.5%), AI-generated consent documentation (n=10, 30.3%), and AI-assisted consent acquisition (n=5, 15.2%). Large language models demonstrated high accuracy, though readability consistently fell below the recommended eighth-grade level. The best-performing model, Copilot, achieved a Flesch-Kincaid Grade Level of 10.59 (±1.22). AI-generated documents improved Flesch Reading Ease Scores by 44% to 122% and reduced required comprehension grade levels by 10% to 47%, and GPT-4 produced significantly more comprehensive consent forms than both Bard Gemini Advanced and human-written documents ( Conclusions: AI has the potential to improve the informed consent process; however, further research is needed to address ethical concerns and ensure its effective, patient-centered integration into clinical practice.
Indexed as
Identifiers
What 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.