ArticleNPJ digital medicine2023
Solving the explainable AI conundrum by bridging clinicians' needs and developers' goals.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 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.
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Who cites it
72 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Task-Technology Fit of Artificial Intelligence-based clinical decision support systems: a review of qualitative studies.BMC medical informatics and decision making · 2025Pooled it
- Application of artificial intelligence in echocardiography from 2009 to 2024: a bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Current state of research and future developments of artificial intelligence in pain diagnosis and treatment.Journal of translational medicine · 2026Review
- The Role of Machine Learning and Artificial Intelligence in Enhancing Critical Care Nursing Practice: A Scoping Review.Nursing in critical care · 2026Article
- Ethical Considerations on Artificial Intelligence, Health and Health Care. All That Glisters Is Not Gold.European journal of clinical investigation · 2026Review
- Machine learning and AI for cancer research and care: a review of applications, limitations, and future directions.Journal of the Egyptian National Cancer Institute · 2026Review
- Article
- Toward trustworthy healthcare AI: designing academic research for translation readiness.Molecular systems biology · 2026Review
- Advancing Human-Centered AI in Clinical Decision Support: Sociocognitive Human-in-the-Loop Study in HIV Care.Journal of medical Internet research · 2026Article
- Application of a deep learning method based on CBCT panoramic reconstruction images for differentiating operationally defined cystic and neoplastic jaw lesions from osteomyelitis: a single-center retrospective study.Scientific reports · 2026Article
- Temporal Convolutional Neural Network Analysis of Magnetocardiography Signals for Detection of Pulmonary Hypertension.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Peripheral Artery Disease: A Science Advisory From the American Heart Association.Circulation. Population health and outcomes · 2026Review
- Healthcare professionals show high AI enthusiasm but limited knowledge: A cross-sectional study.PLOS digital health · 2026Article
- Implementation of an AI-Based Clinical Decision Support System Predicting In-Hospital Cardiac Arrest in General Wards: A Multicenter Staggered-Implementation Study in Secondary Hospitals in Korea.Diagnostics (Basel, Switzerland) · 2026Article
- Multicenter machine learning model for assessing the impact of malignancy on in-hospital mortality in heart failure patients: a clinical decision support system with interpretable artificial intelligence.Scientific reports · 2026Article
- Examining developer perspectives on medical AI regulatory frameworks.NPJ digital medicine · 2026Article
- Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals' perspectives.PLOS digital health · 2026Article
- Early emergency department decision support for heart failure hospitalization using triage-level unstructured and structured data: a retrospective cohort study.BMC medical informatics and decision making · 2026Article
- Electrical risk score and electrocardiographic markers in acute cholecystitis: a prospective cross-sectional study using explainable machine learning.BMC cardiovascular disorders · 2026Article
12 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
8 authors.
Funding
Abstract
Explainable artificial intelligence (XAI) has emerged as a promising solution for addressing the implementation challenges of AI/ML in healthcare. However, little is known about how developers and clinicians interpret XAI and what conflicting goals and requirements they may have. This paper presents the findings of a longitudinal multi-method study involving 112 developers and clinicians co-designing an XAI solution for a clinical decision support system. Our study identifies three key differences between developer and clinician mental models of XAI, including opposing goals (model interpretability vs. clinical plausibility), different sources of truth (data vs. patient), and the role of exploring new vs. exploiting old knowledge. Based on our findings, we propose design solutions that can help address the XAI conundrum in healthcare, including the use of causal inference models, personalized explanations, and ambidexterity between exploration and exploitation mindsets. Our study highlights the importance of considering the perspectives of both developers and clinicians in the design of XAI systems and provides practical recommendations for improving the effectiveness and usability of XAI in healthcare.
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.