ReviewClinical oncology (Royal College of Radiologists (Great Britain))2025
Sex-Based Bias in Artificial Intelligence-Based Segmentation Models in Clinical Oncology.
Review in Clinical oncology (Royal College of Radiologists (Great Britain)), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patients.Briefings in bioinformatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Artificial intelligence (AI) advancements have accelerated applications of imaging in clinical oncology, especially in revolutionizing the safe and accurate delivery of state-of-the-art imaging-guided radiotherapy techniques. However, concerns are growing over the potential for sex-related bias and the omission of female-specific data in multi-organ segmentation algorithm development pipelines. Opportunities exist for addressing sex-specific data as a source of bias, and improving sex inclusion to adequately inform the development of AI-based technologies to ensure their fairness, generalizability and equitable distribution. The goal of this review is to discuss the importance of biological sex for AI-based multi-organ image segmentation in routine clinical and radiation oncology; sources of sex-based bias in data generation, model building and implementation and recommendations to ensure AI equity in this rapidly evolving domain.
Indexed as
Identifiers
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.