SynthesisJournal of patient-reported outcomes2026
Artificial intelligence and patient reported outcomes in ophthalmology.
Synthesis in Journal of patient-reported outcomes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeArtificial intelligence (AI) has the potential to revolutionise the delivery of ophthalmic healthcare worldwide. Whether AI is making a meaningful difference or is acceptable for patients, however, remains unclear. Patient reported outcomes (PROs) allow researchers to answer these questions and smooth the path to clinical deployment. This review aims to investigate how PROs are being applied to the development and evaluation of ophthalmic AI technology and explore any underlying reasons why PROs may be currently underutilised.
methodA systematic search of electronic databases for studies and clinical trials applying PROs in the development and evaluation of ophthalmic AI was conducted from date of inception to February 2025.
resultsFifty articles applied a PRO to ophthalmic AI, from which 14 interventional studies and 24 unique validated PROs were identified. There was a rapid year-on-year increase in PRO utilisation beginning in 2020 until 2024. PROs were concentrated in economically advanced countries, were generic (58%) rather than disease-specific (40%), and most often were used as evaluator metrics (50%), or input (44%) for the AI model. Few articles investigated consumer-ready technologies (12%).
conclusionLow research priority, the nascent state of AI in ophthalmology, and lack of high quality accumulated PROs data were identified as possible barriers to realising the full potential of PROs in ophthalmic AI. Investment into the development of robust validated PROs and the inclusion of PROs in routine data collection may catalyse the development of AI technologies capable of making the greatest meaningful difference to patients’ quality of life.
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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.