Informed consent is a core ethical and legal requirement in oral and maxillofacial surgery (OMFS). Traditional consent documents often lack adequate readability and comprehensive coverage of ethical and safety elements. Large language models (LLMs) may assist in drafting consent forms; however, their alignment with clinician expectations in OMFS remains insufficiently studied.

This in silico descriptive evaluation study assessed AI-generated informed consent drafts using the QUEST (Quality, Utility, Ethics, Safety, and Transparency) framework. Consent drafts were generated for four representative OMFS procedures: third molar extraction, dental implant placement, open reduction and internal fixation of mandibular fracture, and soft tissue biopsy. One draft was produced per procedure. Two experienced oral and maxillofacial surgeons independently rated each draft across QUEST domains using a 5-point Likert scale (1 = poor, 3 = acceptable, 5 = excellent). Readability was evaluated using standard grade-level metrics. Inter-rater reliability was assessed using Cohen’s kappa (κ), with significance set at p < 0.05.

Four AI-generated consent drafts were evaluated. Readability scores ranged from grade 6.0 to 8.0 (mean 6.85 ± 0.85). Inter-rater reliability was substantial (κ = 0.78; p < 0.001). Among QUEST domains, Quality achieved the highest scores (mean 4.1 ± 0.4), while Ethics, Safety, and Transparency demonstrated comparatively lower ratings.

AI-generated informed consent drafts showed acceptable readability and procedural descriptions but demonstrated limitations in ethical, safety, and transparency domains. These findings suggest that LLMs may assist in consent drafting; however, clinician oversight and further validation are required before routine clinical adoption.