Considering the missing science of retraining and maintenance in medical artificial intelligence, using ophthalmology as an exemplar.

Ariel Yuhan Ong, Robbert R Struyven, Alastair K Denniston, David A Merle, Justin Engelmann, Hyunmin Kim, Yukun Zhou, Pearse A Keane, Ines Lains

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Review in NPJ digital medicine, 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

9 authors.

Ariel Yuhan OngInstitute of Ophthalmology, University College London, London, UK. ariel.ong@nhs.net.

Robbert R StruyvenInstitute of Ophthalmology, University College London, London, UK.

Alastair K DennistonNIHR Birmingham Biomedical Research Centre, Birmingham, UK.

David A MerleInstitute of Ophthalmology, University College London, London, UK.

Justin EngelmannInstitute of Ophthalmology, University College London, London, UK.

Hyunmin KimInstitute of Ophthalmology, University College London, London, UK.

Yukun ZhouInstitute of Ophthalmology, University College London, London, UK.

Pearse A KeaneInstitute of Ophthalmology, University College London, London, UK. p.keane@ucl.ac.uk.

Ines LainsDepartment of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA. ines_lains@meei.harvard.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Considerations around model retraining are standard practice in industry and non-healthcare sectors; however, this is much less well explored in medical artificial intelligence (AI). The problem is not only that models often fail to generalise, but that academia in particular does not have a systematic science of retraining to address this gap. This matters for building trustworthy models capable of making a lasting impact, rather than compounding as research waste. In this Perspective, we highlight three common challenges that constrain model retraining in medicine, and argue that academia must evolve beyond a focus on developing proofs-of-concept and world-first innovations to also recognise model retraining as scholarship. Drawing from case examples in ophthalmology, we call on stakeholders to consider not just how we build AI models, but how we should retrain, maintain, and share them.

Identifiers

PMID41741715

DOI10.1038/s41746-026-02463-2

PMCPMC13046786

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LicenceCC BY

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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.