Evidence map›Paper›PMID 42732302›Full record

ArticleJournal of medical education and curricular development

A Pilot Study of a CustomGPT for the Royal College of Ophthalmologists Curriculum 2024.

William Purcell, Vikas Chadha, Devina Gogi, Vernon Long, Aabgina Shafi, Yashin Ramkissoon

Abstract read
In one paragraph

Article in Journal of medical education and curricular development. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

William PurcellDepartment of Ophthalmology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom.ORCID https://orcid.org/0009-0007-2961-542X
Vikas ChadhaTennent Institute of Ophthalmology, Gartnavel General Hospital, Glasgow, Scotland, United Kingdom.
Devina GogiDepartment of Ophthalmology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom.
Vernon LongDepartment of Ophthalmology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom.
Aabgina ShafiDepartment of Ophthalmology, Pinderfields Hospital, Wakefield, United Kingdom.
Yashin RamkissoonDepartment of Ophthalmology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Large language models (LLMs) employ transformer architectures to generate contextually appropriate responses and are increasingly applied in medical education. To support implementation of the Royal College of Ophthalmologists (RCOphth) Ophthalmic Specialist Training (OST) Curriculum 2024, the Yorkshire & Humber School of Ophthalmology developed a CustomGPT trained exclusively on official RCOphth curriculum documents. This study evaluated its usability, effectiveness, and accuracy. Methods: A pilot study invited ophthalmology trainees (ST1-ST7, Fellows) and trainers (Consultants, Supervisors, Tutors, Programme Directors) to use the CustomGPT over a three-month period (April to June 2025). Quantitative feedback (Likert scales) assessed relevance, ease of use, and satisfaction, while qualitative feedback explored perceived value and limitations. Accuracy was evaluated by comparing responses from the CustomGPT and the RCOphth Training Committee (TC) Chair to ten authentic curriculum queries. Results: Twenty-eight participants (14 trainees, 14 trainers) completed the survey; only 21% were regular AI users. Mean scores were high for relevance (4.21/5), ease of use (4.38/5), and satisfaction (4.10/5), with 86% reporting time savings and 75% rating their likelihood of recommending the tool as 8/10 or higher. Qualitative data highlighted concise, accurate, and well-structured responses but noted occasional technical issues and rigid interpretation of complex queries. Comparative analysis showed strong alignment with the TC Chair, though the GPT sometimes demonstrated greater precision in referencing but less flexibility in interpretation. Conclusions: A curriculum-specific GPT can provide rapid, accurate guidance, reducing ambiguity and improving efficiency. When integrated into training platforms, such tools can enhance curriculum accessibility in postgraduate medical education.

Indexed as

Large language modelOphthalmic Specialist TrainingRoyal College of Ophthalmologists

Identifiers

PMID42732302
PMCPMC13570001

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.