ReviewNPJ digital medicine2022
Machine learning for medical imaging: methodological failures and recommendations for the future.
Review in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 265 papers, 9 of them syntheses 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
265 citing papers in PubMed, 9 syntheses or guidelines pooled it, 550 citations in OpenAlex.
- Predictive performance of CT-based artificial intelligence for predicting variceal bleeding in portal hypertension: a systematic review and meta-analysis.Abdominal radiology (New York) · 2026Pooled it
- Diagnostic Performance of Machine Learning for Systemic Lupus Erythematosus: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Generative large language models in the clinical management of Alzheimer's disease and mild cognitive impairment.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Pooled it
- Performance of Deep Learning in Classifying Age-Related Macular Degeneration From Images: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Performance of Radiomics-based machine learning and deep learning-based methods in the prediction of tumor grade in meningioma: a systematic review and meta-analysis.Neurosurgical review · 2025Pooled it
- The effects of applying artificial intelligence to triage in the emergency department: A systematic review of prospective studies.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Pooled it
- A systematic review of the hybrid machine learning models for brain tumour segmentation and detection in medical images.Frontiers in artificial intelligence · 2025Pooled it
- Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review.PloS one · 2024Pooled it
- Side-level versus patient-level evaluation in four-view mammography classification: a comprehensive benchmark on the CMMD dataset.Future science OA · 2026Article
- The evaluation crisis in artificial intelligence-driven computational biology: Pitfalls and solutions.Quantitative biology (Beijing, China) · 2026Article
- The Performance Ceiling of LIDC-IDRI Is Label-Defined Rather than Image-Defined: A Computable Bound and a Purposive Audit of Published Studies.Journal of imaging informatics in medicine · 2026Article
- Uncertainty-aware deep learning in FDG-PET: implications for generalizability in clinical practice.Radiological physics and technology · 2026Article
- Artificial Intelligence in Neuromuscular Diseases: Opportunities for a Data-Scarce Field.Neurology and therapy · 2026Review
- Deep Active Learning for Lung Disease Severity Classification from Chest X-rays: Learning with Less Data in the Presence of Class Imbalance.Journal of imaging informatics in medicine · 2026Article
- Comparative Evaluation of EfficientNet-B3, Vision Transformers, and RETFound for Seven-Category Retinopathy Fundus Image Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Multicohort Evaluation of Acquisition Confounding in Single-Site Lung-CT Malignancy Classification with Frozen Foundation-Model Features.Journal of imaging informatics in medicine · 2026Article
- The RATAB Framework: An Interpretable AI-Driven Radiographic Risk Stratification Model for Suspected Lung Lesions.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in Cerebral Small Vessel Disease Imaging: A Study-Level Cross-Sectional Analysis of Validation Status and Clinical Applicability.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence for Cerebral Aneurysm Management: Integrating Imaging, Hemodynamics, and Clinical Decision Support.Journal of imaging informatics in medicine · 2026Review
205 more citing papers are in PubMed but not listed here.
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 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Research in computer analysis of medical images bears many promises to improve patients' health. However, a number of systematic challenges are slowing down the progress of the field, from limitations of the data, such as biases, to research incentives, such as optimizing for publication. In this paper we review roadblocks to developing and assessing methods. Building our analysis on evidence from the literature and data challenges, we show that at every step, potential biases can creep in. On a positive note, we also discuss on-going efforts to counteract these problems. Finally we provide recommendations on how to further address these problems in the future.
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.