ArticleJournal of biomedical informatics2024
Recommendations to promote fairness and inclusion in biomedical AI research and clinical use.
Article in Journal of biomedical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- PREFER-IT: A transdisciplinary co-created framework to realise inclusive health AI.PLOS digital health · 2026Article
- Integrating Artificial Intelligence with Wearable Sensors for Advanced Health Monitoring and Diagnosis.Biosensors · 2026Review
- Review
- The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.Computer methods and programs in biomedicine · 2025Review
- Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patients.Briefings in bioinformatics · 2025Article
- Expertise Is the Special Sauce That Makes Using AI an Essential Tool in Academics.Oncology nursing forum · 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
4 authors.
Funding
Abstract
objectiveUnderstanding and quantifying biases when designing and implementing actionable approaches to increase fairness and inclusion is critical for artificial intelligence (AI) in biomedical applications.
methodsIn this Special Communication, we discuss how bias is introduced at different stages of the development and use of AI applications in biomedical sciences and health care. We describe various AI applications and their implications for fairness and inclusion in sections on 1) Bias in Data Source Landscapes, 2) Algorithmic Fairness, 3) Uncertainty in AI Predictions, 4) Explainable AI for Fairness and Equity, and 5) Sociological/Ethnographic Issues in Data and Results Representation.
resultsWe provide recommendations to address biases when developing and using AI in clinical applications.
conclusionThese recommendations can be applied to informatics research and practice to foster more equitable and inclusive health care systems and research discoveries.
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.