Evidence map›Paper›PMID 42023401›Full record

ArticleClinical ophthalmology (Auckland, N.Z.)2026

Evaluation of Routine Clinical Deployment of an Autonomous Artificial Intelligence Assistant for Cataract Follow-Up in the National Health Service.

Aisling Higham, Anna Maria Sideri, Filip Tarcoveanu, Ernest J Lim, Lisette H G Bijma, Syeda S Imtiyaz, Louisa Katie Stacey, Sarah Khavandi, Nick de Pennington, James Thomas and 6 more

Abstract read
In one paragraph

Article in Clinical ophthalmology (Auckland, N.Z.), 2026. 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

16 authors.

Aisling HighamDepartment of Ophthalmology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.ORCID 0000-0002-8901-0007
Anna Maria SideriDepartment of Ophthalmology, King's College Hospital NHS Foundation Trust, London, UK.
Filip TarcoveanuDepartment of Ophthalmology, Stoke Mandeville Hospital, Buckinghamshire Healthcare NHS Trust, Aylesbury, UK.
Ernest J LimUfonia Limited, Oxford, UK.
Lisette H G BijmaUfonia Limited, Oxford, UK.
Syeda S ImtiyazDepartment of Ophthalmology, Stoke Mandeville Hospital, Buckinghamshire Healthcare NHS Trust, Aylesbury, UK.
Louisa Katie StaceyUfonia Limited, Oxford, UK.
Sarah KhavandiUfonia Limited, Oxford, UK.
Nick de PenningtonUfonia Limited, Oxford, UK.ORCID 0000-0003-4536-4978
James ThomasUfonia Limited, Oxford, UK.
Michael AdamsDepartment of Ophthalmology, Stoke Mandeville Hospital, Buckinghamshire Healthcare NHS Trust, Aylesbury, UK.
Sarah J MalingDepartment of Ophthalmology, Stoke Mandeville Hospital, Buckinghamshire Healthcare NHS Trust, Aylesbury, UK.
Kikkeri S ArunDepartment of Ophthalmology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Rebecca TurnerDepartment of Ophthalmology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Christopher KingDepartment of Ophthalmology, Stoke Mandeville Hospital, Buckinghamshire Healthcare NHS Trust, Aylesbury, UK.
Kanmin XueDepartment of Ophthalmology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To understand the efficiency, safety, and patient acceptability of using an artificial intelligence-based conversational agent Dora R2 (Ufonia Limited, UK) in the cataract pathway. Patients and Methods: This mixed methods cohort-based service evaluation included both prospective and retrospective data collection from two UK public hospitals: Oxford University Hospitals National Health Service (NHS) Foundation Trust and Buckinghamshire Healthcare NHS Trust. Patients undergoing cataract surgery of mixed complexity were included. All patients who had postoperative calls with Dora R2 between October 2022 and April 2023 were included. Dora R2 calls patients three weeks after routine cataract surgery to assess symptoms and answer patient queries. Patient demographics and clinical outcomes were reviewed, and statistical analyses were performed to identify any differences based on age, gender and ethnicity. Results: Of 1580 eligible patients, 1269 (78%) completed the Dora R2 call. About 767 (63%) had "no clinical concerns" identified by Dora. The median patient age was 77 years, with 84% identifying as white. There were no significant differences in call outcomes based on demographic factors (at 5% significance level). The Net Promoter Score for patient acceptability was 47, indicating high satisfaction. Regarding safety, only 0.3% of patients required unplanned management changes within two weeks of a Dora call with a "no concerns identified" outcome. Conclusion: Dora R2 effectively supports postoperative follow-up for cataract surgery, demonstrating high efficiency, safety, and patient acceptability. The technology successfully supports clinicians in identifying uncomplicated cases, reduces the need for clinician-led consultations, and does not exacerbate digital inequalities, showing promise for broader implementation.

Indexed as

cataract surgerydigital health equitypost operative follow uptelemedicine

Identifiers

PMID42023401
PMCPMC13098554

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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.