ReviewCancers2026
Uveal Melanoma Ground Truth Labeling in Machine Learning.
Review in Cancers, 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
6 authors.
Funding
Abstract
BACKGROUND/
objectivesUveal melanoma (UM) is the most common primary intraocular malignant tumor among adults and has a high risk of metastasis. Recently, artificial intelligence (AI) tools have been developed to support the management of UM across different clinical tasks. The definition of ground truth, the reference standard that models use in training and development, greatly influences the performance and clinical relevance of the models. Currently, there is limited consensus regarding which ground truth methods are most appropriate for each clinical application. This review aims to evaluate the advantages and limitations of available ground truth options in UM and proposes task-specific recommendations based on clinical utility, feasibility, and cost.
methodsA narrative review of the existing literature was conducted to identify and evaluate commonly used ground truth methods for UM AI applications based on factors such as time, cost, invasiveness, and required level of expertise.
resultsEach ground truth method offers distinct benefits and drawbacks in relation to biological precision, invasiveness, availability, cost, and turnaround time. No single ground truth is universally optimal across all applications. Instead, the ideal choice depends on the intended clinical task, and practical alternatives exist to mitigate the constraints that result from limited time and institutional resources.
conclusionsThe selection of ground truth for AI models in UM should be chosen based on the specific clinical task to balance predictive relevance with feasibility of implementation. The adoption of task-specific ground truth standards may improve the development of clinically meaningful AI tools and facilitate their integration into real-world practice.
Indexed as
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What OpenQuestion holds
Registered trials
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.