ReviewDigital health
Re-focusing explainability in medicine.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 3 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
32 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Wearable Technology and Machine Learning for Prediction of Performance-Based and Patient-Reported Outcome Measures: A Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- The ethical requirement of explainability for AI-DSS in healthcare: a systematic review of reasons.BMC medical ethics · 2024Pooled it
- Machine-learning versus traditional approaches for atherosclerotic cardiovascular risk prognostication in primary prevention cohorts: a systematic review and meta-analysis.European heart journal. Quality of care & clinical outcomes · 2023Pooled it
- Artificial intelligence predictive analytics in heart failure: results of the pilot phase of a pragmatic randomized clinical trial.Journal of the American Medical Informatics Association : JAMIA · 2024Trial
- Introduction to using symbolic regression for interpretable machine learning in healthcare.Communications medicine · 2026Review
- AI Perceptions, Professional Identity, and AI-Supported Clinical Decisions Among Medical Students and Clinicians: Cross-Sectional Survey and Quasi-Randomized Vignette Study.JMIR medical education · 2026Article
- Development and evaluation of an explainable machine learning selection pipeline for predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.Journal of translational medicine · 2026Article
- Explainability in AI-enabled medical neurotechnology: a scoping review.Journal of neuroengineering and rehabilitation · 2026Article
- Artificial intelligence for predicting and preventing adverse pregnancy outcomes addressing bias and clinical translation.Frontiers in digital health · 2026Review
- Dual-modality CAD for breast cancer screening: dealing with discordant diagnosis between mammography and tomography.Frontiers in oncology · 2026Article
- Machine learning models explanations as interpretations of evidence: a theoretical framework of explainability and its implications on high-stakes biomedical decision-making.BMC medical research methodology · 2025Article
- Ethical challenges in biomarker research and precision medicine - a qualitative study in dermatology.BMC medical ethics · 2025Article
- Machine Learning-Based Prediction of Long-Term Outcomes in Patients With Chronic Total Occlusion of the Coronary Artery.Korean circulation journal · 2025Article
- Trust, Trustworthiness, and the Future of Medical AI: Outcomes of an Interdisciplinary Expert Workshop.Journal of medical Internet research · 2025Article
- PON-P3: Accurate Prediction of Pathogenicity of Amino Acid Substitutions.International journal of molecular sciences · 2025Article
- Finding Consensus on Trust in AI in Health Care: Recommendations From a Panel of International Experts.Journal of medical Internet research · 2025Article
- Predicting Atrial Fibrillation Relapse Using Bayesian Networks: Explainable AI Approach.JMIR cardio · 2025Article
- AI Interventions to Alleviate Healthcare Shortages and Enhance Work Conditions in Critical Care: Qualitative Analysis.Journal of medical Internet research · 2025Article
- Sleep Apnea Events Recognition Based on Polysomnographic Recordings: A Large-Scale Multi-Channel Machine Learning approach.IEEE open journal of engineering in medicine and biology · 2025Article
- Machine learning innovations in CPR: a comprehensive survey on enhanced resuscitation techniques.Artificial intelligence review · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Using artificial intelligence to improve patient care is a cutting-edge methodology, but its implementation in clinical routine has been limited due to significant concerns about understanding its behavior. One major barrier is the explainability dilemma and how much explanation is required to use artificial intelligence safely in healthcare. A key issue is the lack of consensus on the definition of explainability by experts, regulators, and healthcare professionals, resulting in a wide variety of terminology and expectations. This paper aims to fill the gap by defining minimal explainability standards to serve the views and needs of essential stakeholders in healthcare. In that sense, we propose to define minimal explainability criteria that can support doctors' understanding, meet patients' needs, and fulfill legal requirements. Therefore, explainability need not to be exhaustive but sufficient for doctors and patients to comprehend the artificial intelligence models' clinical implications and be integrated safely into clinical practice. Thus, minimally acceptable standards for explainability are context-dependent and should respond to the specific need and potential risks of each clinical scenario for a responsible and ethical implementation of artificial intelligence.
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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.