ArticleJournal of the American Medical Informatics Association : JAMIA2020
Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 3 of them syntheses that pooled it.
What it found
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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, 3 syntheses or guidelines pooled it.
- Reporting Quality of AI Intervention in Randomized Controlled Trials in Primary Care: Systematic Review and Meta-Epidemiological Study.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence applied to magnetic resonance imaging reliably detects the presence, but not the location, of meniscus tears: a systematic review and meta-analysis.European radiology · 2024Pooled it
- Systematic review of current natural language processing methods and applications in cardiology.Heart (British Cardiac Society) · 2022Pooled it
- Evaluating the Influence of a Risk Calculator on Physician Risk Perception and Decision-Making in IPMN Surveillance: A Randomized Trial.Annals of surgical oncology · 2026Trial
- AI-Assisted Cardiovascular Risk Assessment by General Practitioners in Resource-Constrained Indonesian Settings Using a Conceptual Prototype: Randomized Controlled Study.Journal of medical Internet research · 2025Trial
- Patient trust in the use of machine learning-based clinical decision support systems in psychiatric services: A randomized survey experiment.European psychiatry : the journal of the Association of European Psychiatrists · 2024Trial
- RE-LIG: A Faithfulness-Driven Layer Integrated Gradients Framework for Explainable Medical Visual Question Answering.Journal of imaging informatics in medicine · 2026Article
- LANTERN-XGB: An Interpretable Multi-Modal Machine Learning for Improving Clinical Decision-Making in Lung Cancer.International journal of molecular sciences · 2026Article
- "Black box" artificial intelligence for mortality prediction: a mixed-methods study of palliative care team, patient, and caregiver perspectives.Annals of palliative medicine · 2026Article
- The Application of artificial intelligence in periprosthetic joint infection.Journal of advanced research · 2026Review
- Evidence-based action plan for integrating artificial intelligence in an academic medical centre-a multidisciplinary approach.PloS one · 2026Article
- Between map and maze: reframing trust in healthcare AI.AI & society · 2026Review
- A prompt framework for enhancing LLM-based explainability of medical machine learning models: an intensive care unit application.BMC medical informatics and decision making · 2025Article
- Predicting arrhythmia recurrence post-ablation in atrial fibrillation using explainable machine learning.Communications medicine · 2025Article
- Testing a Novel Design Framework for Patient-Facing Machine Learning-Based Predictions of Heart Failure Decompensation.JACC. Advances · 2025Article
- Current perspectives and challenges of using artificial intelligence in immunodeficiencies.The Journal of allergy and clinical immunology · 2025Review
- Artificial intelligence in emergency department triage: a scoping review on workload reduction and patient safety enhancement.Journal of Korean biological nursing science · 2025Review
- So You've Got a High AUC, Now What? An Overview of Important Considerations when Bringing Machine-Learning Models from Computer to Bedside.Medical decision making : an international journal of the Society for Medical Decision Making · 2025Review
- Exploring doctors' perspectives on precision medicine and AI in colorectal cancer: opportunities and challenges for the doctor-patient relationship.BMC medical informatics and decision making · 2025Article
- Clinician-informed XAI evaluation checklist with metrics (CLIX-M) for AI-powered clinical decision support systems.NPJ digital medicine · 2025Review
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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveImplementation of machine learning (ML) may be limited by patients' right to "meaningful information about the logic involved" when ML influences healthcare decisions. Given the complexity of healthcare decisions, it is likely that ML outputs will need to be understood and trusted by physicians, and then explained to patients. We therefore investigated the association between physician understanding of ML outputs, their ability to explain these to patients, and their willingness to trust the ML outputs, using various ML explainability methods. MATERIALS AND
methodsWe designed a survey for physicians with a diagnostic dilemma that could be resolved by an ML risk calculator. Physicians were asked to rate their understanding, explainability, and trust in response to 3 different ML outputs. One ML output had no explanation of its logic (the control) and 2 ML outputs used different model-agnostic explainability methods. The relationships among understanding, explainability, and trust were assessed using Cochran-Mantel-Haenszel tests of association.
resultsThe survey was sent to 1315 physicians, and 170 (13%) provided completed surveys. There were significant associations between physician understanding and explainability (P < .001), between physician understanding and trust (P < .001), and between explainability and trust (P < .001). ML outputs that used model-agnostic explainability methods were preferred by 88% of physicians when compared with the control condition; however, no particular ML explainability method had a greater influence on intended physician behavior.
conclusionsPhysician understanding, explainability, and trust in ML risk calculators are related. Physicians preferred ML outputs accompanied by model-agnostic explanations but the explainability method did not alter intended physician behavior.
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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.