ReviewPatterns (New York, N.Y.)2022
In medicine, how do we machine learn anything real?
Review in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled 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.
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
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations.The Lancet. Digital health · 2025Pooled it
- Responsible AI for Predicting Delayed Hospital Discharge Among Older Adults: Development and Evaluation Study for Balancing Accuracy, Equity, and Explainability.JMIR medical informatics · 2026Article
- Artificial Intelligence in Medicine: Barriers, Solutions, and Strategies.HCA healthcare journal of medicine · 2026Article
- Predictors of frontline doublet or triplet regimen initiation in transplant-ineligible newly diagnosed multiple myeloma.Future science OA · 2025Article
- Integration of label-free surface enhanced Raman spectroscopy (SERS) of extracellular vesicles (EVs) with Raman tagged labels to enhance ovarian cancer diagnostics.Biosensors & bioelectronics · 2025Article
- Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap.NPJ digital medicine · 2025Review
- Large Language Models lack essential metacognition for reliable medical reasoning.Nature communications · 2025Article
- Evaluating Algorithmic Bias in 30-Day Hospital Readmission Models: Retrospective Analysis.Journal of medical Internet research · 2024Article
- Drop the shortcuts: image augmentation improves fairness and decreases AI detection of race and other demographics from medical images.EBioMedicine · 2024Article
- Using Machine Learning to Predict Unplanned Hospital Utilization and Chemotherapy Management From Patient-Reported Outcome Measures.JCO clinical cancer informatics · 2024Article
- Pathophysiological Features in Electronic Medical Records Sustain Model Performance under Temporal Dataset Shift.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Presentation matters for AI-generated clinical advice.Nature human behaviour · 2023Article
- Using Visual Patient to Show Vital Sign Predictions, a Computer-Based Mixed Quantitative and Qualitative Simulation Study.Diagnostics (Basel, Switzerland) · 2023Article
- Symptoms for early diagnosis of chronic kidney disease in children - a machine learning-based score.European journal of pediatrics · 2023Article
- Article
- Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics.Nanoscale advances · 2023Review
- "AI for all" is a matter of social justice.AI and ethics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning has traditionally operated in a space where data and labels are assumed to be anchored in objective truths. Unfortunately, much evidence suggests that the "embodied" data acquired from and about human bodies does not create systems that function as desired. The complexity of health care data can be linked to a long history of discrimination, and research in this space forbids naive applications. To improve health care, machine learning models must strive to recognize, reduce, or remove such biases from the start. We aim to enumerate many examples to demonstrate the depth and breadth of biases that exist and that have been present throughout the history of medicine. We hope that outrage over algorithms automating biases will lead to changes in the underlying practices that generated such data, leading to reduced health disparities.
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.