Evidence map›Paper›PMID 39419585›Full record

ArticleBMJ open ophthalmology2024

Artificial intelligence chatbots as sources of patient education material for cataract surgery: ChatGPT-4 versus Google Bard.

Matthew Azzopardi, Benjamin Ng, Abison Logeswaran, Constantinos Loizou, Ryan Chin Taw Cheong, Prasanth Gireesh, Darren Shu Jeng Ting, Yu Jeat Chong

Abstract readComparative Study
In one paragraph

Article in BMJ open ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Matthew AzzopardiRoyal Free London NHS Foundation Trust, London, UK.ORCID 0000-0002-8571-2503
Benjamin NgUniversity of Oxford Christ Church, Oxford, UK.
Abison LogeswaranMoorfields Eye Hospital NHS Foundation Trust, London, UK.
Constantinos LoizouModality Partnership, Birmingham, UK.
Ryan Chin Taw CheongRoyal National ENT and Eastman Dental Hospitals, London, UK.
Prasanth GireeshLeela Eye Clinic, Chennai, Tamilnadu, India.
Darren Shu Jeng TingAcademic Unit of Ophthalmology, University of Birmingham Institute of Inflammation and Ageing, Birmingham, UK.ORCID 0000-0003-1081-1141
Yu Jeat ChongSingapore Eye Research Institute, Singapore yujeat.chong07@alumni.imperial.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo conduct a head-to-head comparative analysis of cataract surgery patient education material generated by Chat Generative Pre-trained Transformer (ChatGPT-4) and Google Bard. METHODS AND ANALYSIS: 98 frequently asked questions on cataract surgery in English were taken in November 2023 from 5 trustworthy online patient information resources. 59 of these were curated (20 augmented for clarity and 39 duplicates excluded) and categorised into 3 domains: condition (n=15), preparation for surgery (n=21) and recovery after surgery (n=23). They were formulated into input prompts with 'prompt engineering'. Using the Patient Education Materials Assessment Tool-Printable (PEMAT-P) Auto-Scoring Form, four ophthalmologists independently graded ChatGPT-4 and Google Bard responses. The readability of responses was evaluated using a Flesch-Kincaid calculator. Responses were also subjectively examined for any inaccurate or harmful information.

resultsGoogle Bard had a higher mean overall Flesch-Kincaid Level (8.02) compared with ChatGPT-4 (5.75) (p<0.001), also noted across all three domains. ChatGPT-4 had a higher overall PEMAT-P understandability score (85.8%) in comparison to Google Bard (80.9%) (p<0.001), which was also noted in the 'preparation for cataract surgery' (85.2% vs 75.7%; p<0.001) and 'recovery after cataract surgery' (86.5% vs 82.3%; p=0.004) domains. There was no statistically significant difference in overall (42.5% vs 44.2%; p=0.344) or individual domain actionability scores (p>0.10). None of the generated material contained dangerous information.

conclusionIn comparison to Google Bard, ChatGPT-4 fared better overall, scoring higher on the PEMAT-P understandability scale and exhibiting more faithfulness to the prompt engineering instruction. Since input prompts might vary from real-world patient searches, follow-up studies with patient participation are required.

Indexed as

Artificial IntelligenceCataract ExtractionPatient Education as TopicComprehensionHumansInternetCataractMedical EducationTreatment Surgery

Identifiers

PMID39419585
PMCPMC11487885

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Registered trials

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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.