Explainability for artificial intelligence in healthcare
A multidisciplinary perspective
Dados Bibliográficos
| ID | 23318986 |
|---|---|
| Autores | Julia Amann (0000-0003-2155-5286, ETH Zurich, autor correspondente), Alessandro Blasimme (0000-0001-5908-2002, ETH Zurich), Effy Vayena (0000-0003-1303-5467, ETH Zurich), Dietmar Frey (0000-0001-5407-2331, Charité - Universitätsmedizin Berlin), Vince I Madai (0000-0002-8552-6954, Birmingham City University) |
| Ano | 2020 |
| Volume | 20 |
| Fascículo | 1 |
| Páginas | 310-310 |
| Data de publicação | 2020-12-01 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | BMC Medical Informatics and Decision Making (JOURNAL) |
| Identificadores do periódico | ISSN: 1472-6947 • E-ISSN: 1472-6947 |
| Editora | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1186/s12911-020-01332-6 |
| PMID | 33256715 |
| OpenAlex | W3109650690 |
| Idioma | EN |
| Citações recebidas | 74 |
| Referências citadas | 37 |
Autonomy · Beneficence · Bioethics · Economic Justice · Engineering ethics · Health care · Knowledge management · Multidisciplinary approach · Perspective (graphical) · Political science · Artificial Intelligence · Artificial Intelligence in Healthcare and Education · Computer Science · Engineering · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI · Health Informatics · Law · Psychology
The Patient Beyond the Dataset
Transparency tinkering
Ethical considerations in the use of patient medical records for research
Healthcare workers' adoption of and satisfaction with artificial intelligence
Interpreting Black-Box Models
Transforming Mortality Prediction
Ethical and social dynamics in artificial intelligence and society
Evaluating machine learning-enabled and multimodal data-driven exercise prescriptions for mental health
Characteristics of Artificial Intelligence Clinical Trials in the Field of Healthcare
Recentering responsible and explainable artificial intelligence research on patients
Artificial Intelligence Applicability in the Pharmaceutical Industry
Explainable AI in osteosarcoma image analysis
The speculative future of conversational AI for neurocognitive disorder screening
The democratization dilemma
Panta Rh-AI
Physicians’ ethical concerns about artificial intelligence in medicine
AI-driven transformation of precision medicine
Research integrity and data ethics in AI-driven integrated healthcare
Global patterns and inequalities in healthy aging
A dialectical lens for AI and medical humanities
Leveraging a machine learning model to predict hospital readmission risk
Uncloaking the black-box
Analysis of the anti-scalping mechanism of hospital appointment registration based on Bayesian theory
A machine learning approach with SHAP interpretability for classifying drug craving levels
The use of artificial intelligence for delivery of essential health services across WHO regions
Decolonising global health by decolonising academic publishing
Karl Jaspers and artificial neural nets
Mapping the landscape of ethical considerations in explainable AI research
Algorithmic gaze and subject occlusion
Explainability, Public Reason, and Medical Artificial Intelligence
Personalized explanations for clinician-AI interaction in breast imaging diagnosis by adapting communication to expertise levels
Who needs explanation and when? Juggling explainable AI and user epistemic uncertainty
Do stakeholder needs differ? - Designing stakeholder-tailored Explainable Artificial Intelligence (XAI) interfaces
Risk prediction algorithms and clinical judgment
Assessing the impact of information on patient attitudes toward artificial intelligence-based clinical decision support (AI/CDS)
Responsibility and decision-making authority in using clinical decision support systems
Agree to disagree
Evidence, ethics and the promise of artificial intelligence in psychiatry
Artificial Intelligence and Understanding in Medicine
Ethical and Legal Challenges of Partially and Fully Autonomous AI in Healthcare
New challenges in medical law. Patient–doctor relationship, informed consent and medical civil liability in the era of AI
Unlocking the black box
Exploring the drivers of XAI-enhanced clinical decision support systems adoption
Navigating AI unpredictability
When is black-box AI justifiable to use in healthcare
Reluctant Republic
Between promise and practice
Convergence of Diverse Expertise
One blind spot of the explainability debate
AI and shared decision-making
From moral panic to pragmatic governance
Explaining explainable AI for healthcare
“Just” accuracy? Procedural fairness demands explainability in AI-based medical resource allocations
Ethical and epistemic implications of artificial intelligence in medicine
A counterintuitive approach to explainable AI in healthcare
AI, Epidemiology and Public Health in the Covid Pandemic
Inteligência artificial e suas implicações éticas e legais
Artificial intelligence and its ethical and legal implications
La inteligencia artificial y sus implicaciones éticas y legales
Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education
Factors affecting attitude and intention to adopt artificial intelligence for sustainable triage
Urban Innovation Dilemmas
Critical Engagement
Trust, Explainability and AI
Between Hope and Skepticism
Who decides what is healthy? Algorithmic classification and medical normativity
Nurses’ experiences regarding role boundaries in collaborative AI-Assisted nursing
Sincerity as ethical alignment to reconstruct the moral foundation of AI ethics
Ethical issues raised by artificial intelligence and big data in population health
Ethical assessments and mitigation strategies for biases in AI-systems used during the Covid-19 pandemic
Information, collaboration, regulation
Algorithmic care and consent
Prediction and explainability in AI
First, Trust Needs to Develop
Decision aids for people facing health treatment or screening decisions
Principles alone cannot guarantee ethical AI
A guide to deep learning in healthcare
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Computer knows best? The need for value-flexibility in medical AI
On the ethics of algorithmic decision-making in healthcare
Shared Decision Making — The Pinnacle of Patient-Centered Care
Dissecting racial bias in an algorithm used to manage the health of populations
Artificial Intelligence and Black‐Box Medical Decisions
Artificial Intelligence and Patient-Centered Decision-Making
The Morality of Freedom
| Obras citantes distintas | 74 |
|---|---|
| Citações por ano | 18,5 |
| Intervalo de citações | 2022 - 2026 (5) |
| Velocidade de citação | current |
| Altamente citado | Não |
| Tipos de citação | Neutras: 70 |