ReviewIntensive care medicine experimental2019
Ethical considerations about artificial intelligence for prognostication in intensive care.
Review in Intensive care medicine experimental, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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
44 citing papers in PubMed.
- Ethical Aspects of Artificial Intelligence in Precision Medicine for Obesity Management.JMA journal · 2026Review
- Ethical and Governance Challenges of AI in Medical Imaging and Diagnostics: A Systematic Survey and Policy Framework Recommendations.Healthcare (Basel, Switzerland) · 2026Review
- Artificial intelligence and medical futility.BMC medical informatics and decision making · 2026Review
- Artificial intelligence-guided tools in adult ECMO: Current advancements, emerging Trends and future directions.Perfusion · 2026Review
- Decision-making for ICU admission: is there a place for AI? Exploring and understanding meaning, experiences, and perspectives.BMC medical ethics · 2026Article
- Artificial Intelligence in Ocular Surface Tumors: Current Advances, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Ethical, legal, and social issues of AI use in emergency healthcare: a scoping review.BMC medical informatics and decision making · 2026Article
- Artificial Intelligence, Informatics, and Pain in Aging.Clinics in geriatric medicine · 2026Review
- Evidence-based action plan for integrating artificial intelligence in an academic medical centre-a multidisciplinary approach.PloS one · 2026Article
- Evaluating the Efficacy of Large Language Models in Generating Medical Documentation: A Comparative Study of ChatGPT-4, ChatGPT-4o, and Claude.Aesthetic plastic surgery · 2025Article
- Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare.Nanomaterials (Basel, Switzerland) · 2025Review
- Ethical considerations on the role of artificial intelligence in defining the futility in emergency surgery.International journal of surgery (London, England) · 2025Article
- Machine Learning-Based Models for Intracerebral Hemorrhage In-Hospital Mortality Prediction.Journal of the American Heart Association · 2025Article
- The role of artificial intelligence in life-sustaining treatment decisions: current state and future considerations.Intensive care medicine · 2025Article
- Artificial intelligence, ethics, and hospital medicine: Addressing challenges to ethical norms and patient-centered care.Journal of hospital medicine · 2024Article
- The Untapped Potential of Dimension Reduction in Neuroimaging: Artificial Intelligence-Driven Multimodal Analysis of Long COVID Fatigue.Brain sciences · 2024Article
- Ethical Considerations and Equipoise in Cancer Surgery.Indian journal of surgical oncology · 2024Review
- Prognosticating the outcome of intensive care in older patients-a narrative review.Annals of intensive care · 2024Review
- Suitability of the Current Health Technology Assessment of Innovative Artificial Intelligence-Based Medical Devices: Scoping Literature Review.Journal of medical Internet research · 2024Article
- Brave New World of Artificial Intelligence: Its Use in Antimicrobial Stewardship-A Systematic Review.Antibiotics (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrognosticating the course of diseases to inform decision-making is a key component of intensive care medicine. For several applications in medicine, new methods from the field of artificial intelligence (AI) and machine learning have already outperformed conventional prediction models. Due to their technical characteristics, these methods will present new ethical challenges to the intensivist.
resultsIn addition to the standards of data stewardship in medicine, the selection of datasets and algorithms to create AI prognostication models must involve extensive scrutiny to avoid biases and, consequently, injustice against individuals or groups of patients. Assessment of these models for compliance with the ethical principles of beneficence and non-maleficence should also include quantification of predictive uncertainty. Respect for patients' autonomy during decision-making requires transparency of the data processing by AI models to explain the predictions derived from these models. Moreover, a system of continuous oversight can help to maintain public trust in this technology. Based on these considerations as well as recent guidelines, we propose a pathway to an ethical implementation of AI-based prognostication. It includes a checklist for new AI models that deals with medical and technical topics as well as patient- and system-centered issues.
conclusionAI models for prognostication will become valuable tools in intensive care. However, they require technical refinement and a careful implementation according to the standards of medical ethics.
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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.