ArticleIEEE transactions on technology and society2022
Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients.
Article in IEEE transactions on technology and society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis 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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- The real-world impact of artificial intelligence ethics frameworks across a decade in healthcare: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- The Emergence of AI in Public Health Is Calling for Operational Ethics to Foster Responsible Uses.International journal of environmental research and public health · 2025Article
- Upskilling or deskilling? Measurable role of an AI-supported training for radiology residents: a lesson from the pandemic.Insights into imaging · 2025Article
- Machine learning for catalysing the integration of noncoding RNA in research and clinical practice.EBioMedicine · 2024Review
- Convolutional neural network based data interpretable framework for Alzheimer's treatment planning.Visual computing for industry, biomedicine, and art · 2024Article
- Developing machine learning systems worthy of trust for infection science: a requirement for future implementation into clinical practice.Frontiers in digital health · 2023Review
- AI revolution in healthcare and medicine and the (re-)emergence of inequalities and disadvantages for ageing population.Frontiers in sociology · 2022Article
Corrections and comments
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Authors and funding
57 authors.
Funding
Abstract
This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does "trustworthy AI" mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic.
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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.