Evidence map›Paper›PMID 42011474›Full record

ArticleCureus2026

Comparative Evaluation of Gemini 3.0- and ChatGPT 5.0-Generated Regional Language Informed Consent Forms in Ophthalmology: A Dual-Rater Study in Hindi and Kannada.

Deepsekhar Das, Bahubali Shetti, Venkatesh Antalmarad, Anshum Choudhary, Kathyayini G Thodupunoori, Sumit Grover, Atindra Narayan

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Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Deepsekhar DasOphthalmology, All India Institute of Medical Sciences, New Delhi, New Delhi, IND.
Bahubali ShettiOphthalmology, All India Institute of Medical Sciences, New Delhi, New Delhi, IND.
Venkatesh AntalmaradOphthalmology, All India Institute of Medical Sciences, New Delhi, New Delhi, IND.
Anshum ChoudharyOphthalmology, All India Institute of Medical Sciences, New Delhi, New Delhi, IND.
Kathyayini G ThodupunooriOphthalmology, All India Institute of Medical Sciences, New Delhi, New Delhi, IND.
Sumit GroverOphthalmology, All India Institute of Medical Sciences, New Delhi, New Delhi, IND.
Atindra NarayanMedicine, All India Institute of Medical Sciences, New Delhi, New Delhi, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate the accuracy, linguistic quality, clinical completeness, and real-world applicability of informed consent forms generated in Indian regional languages (Hindi and Kannada) by two large language model-based chatbots for common ophthalmic surgical procedures.

methodsIn this comparative, blinded observational study, two chatbots (Gemini 3.0 (Google, California, USA) and ChatGPT 5.0 (OpenAI, California, USA)) were prompted to generate informed consent documents in Hindi and Kannada for five ophthalmic scenarios: cataract surgery, traumatic corneal perforation repair, therapeutic penetrating keratoplasty, orbitotomy, and squint surgery. Outputs were independently assessed by four ophthalmologist raters (two for each language) using a 10-point scoring system based on correctness, completeness, language and readability, clinical relevance, and real-world applicability. Descriptive statistics were calculated. Paired t-tests were used to compare chatbot performance, effect sizes (Cohen's d) were estimated, and inter-rater reliability was assessed using intraclass correlation coefficients (ICCs).

resultsAcross both languages, Gemini 3.0 demonstrated more consistent performance and higher combined mean scores. In the Hindi cohort, combined mean scores were comparable between Gemini 3.0 (7.85) and ChatGPT 5.0 (8.00), with significant rater-dependent preference variability. In contrast, in the Kannada cohort, Gemini 3.0 significantly outperformed ChatGPT 5.0 (8.8 vs 7.7, p<0.01), with large to extremely large effect sizes (Cohen's d: 1.23-3.8). Inter-rater reliability was moderate to good for Gemini 3.0 (ICC: 0.62-0.71) and lower for ChatGPT 5.0 (ICC: 0.38-0.59). ChatGPT 5.0 exhibited frequent grammatical and terminological inaccuracies, particularly in Kannada, affecting clinical usability.

conclusionLarge language models can generate clinically usable informed consent forms in Indian regional languages; however, performance varies significantly between models. Gemini 3.0 demonstrated superior linguistic accuracy, consistency, and clinical suitability. Language-specific validation and mandatory human oversight are essential before clinical implementation.

Indexed as

ai generated consentchatgpt 5.0gemini 3.0informed consentlarge language modelophthalmology

Identifiers

PMID42011474
PMCPMC13092168

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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.