ArticleCritical care (London, England)2024
Should AI models be explainable to clinicians?
Article in Critical care (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 4 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
90 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine Learning-Based Prognostic Models for Functional Outcomes in Spinal Cord Injury: Systematic Review.JMIR medical informatics · 2026Pooled it
- Research on machine learning-based clinical prediction models: a bibliometric analysis.Frontiers in oncology · 2026Pooled it
- AI-driven techniques for detection and mitigation of SARS-CoV-2 spread: a review, taxonomy, and trends.Clinical and experimental medicine · 2025Pooled it
- Stage-stratified benefits of AI-radiomics PET in early Alzheimer's disease: a systematic review and meta-analysis.Frontiers in neurologyPooled it
- Are asynchronous or synchronous clinical decision support more likely to change provider behavior? A case study in dementia.Journal of the American Medical Informatics Association : JAMIA · 2026Trial
- Drivers of Artificial Intelligence (AI) Adoption in Supporting Chronic Disease Management in Primary Care: A Scoping Review.Nursing reports (Pavia, Italy) · 2026Review
- The Role of Machine Learning and Artificial Intelligence in Enhancing Critical Care Nursing Practice: A Scoping Review.Nursing in critical care · 2026Article
- Cone-beam computed tomography-based radiomic analysis of architectural phenotypes in jaw cysts and tumors using interpretable artificial intelligence models.World journal of radiology · 2026Article
- Automating the Management of Extraspinal Findings in Magnetic Resonance Imaging Spine Studies Using a Privacy-Preserving Large Language Model: Retrospective Validation Study.Journal of medical Internet research · 2026Article
- Toward Precision Oral Medicine in Pemphigus Vulgaris: A Conceptual AI Framework for Rituximab Response Prediction.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Graph Neural Networks in Neuroimaging: Current Status and Biostatistical Considerations for Clinical Deployment.Annals of biomedical engineering · 2026Review
- Article
- Beyond EuroSCORE II: is artificial intelligence ready to redefine risk stratification in cardiothoracic surgery?Annals of medicine and surgery (2012) · 2026Article
- Hippocampal subfield radiomics improves identification of in-hospital aggressive behavior in schizophrenia: an interpretable machine-learning study.BMC psychiatry · 2026Article
- PHO-Agents: A Large Language Model-Powered Multi-Agent System for Predicting Health Outcomes.medRxiv : the preprint server for health sciences · 2026Article
- Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.Journal of imaging · 2026Review
- Tongue muscle strength and resting posture across sagittal skeletal patterns in adolescents: a cross-sectional study using mediation analysis and decision tree.BMC oral health · 2026Article
- Development and internal validation of a clinical prediction model for postoperative urinary tract infection in older surgical patients: a retrospective cohort study.BMC geriatrics · 2026Article
- Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology.Journal of imaging informatics in medicine · 2026Review
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
30 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
5 authors.
Funding
Abstract
In the high-stakes realm of critical care, where daily decisions are crucial and clear communication is paramount, comprehending the rationale behind Artificial Intelligence (AI)-driven decisions appears essential. While AI has the potential to improve decision-making, its complexity can hinder comprehension and adherence to its recommendations. "Explainable AI" (XAI) aims to bridge this gap, enhancing confidence among patients and doctors. It also helps to meet regulatory transparency requirements, offers actionable insights, and promotes fairness and safety. Yet, defining explainability and standardising assessments are ongoing challenges and balancing performance and explainability can be needed, even if XAI is a growing field.
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.