“Artificial intelligence &amp; COVID-19: (bio)ethical aspects of end of life

Motta, Oswaldo Jesus Rodrigues da; Silva, Eugênio; Siqueira-Batista, Rodrigo

doi:10.1590/1806-9282.66.s2.5

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…In the early stages of an emerging infectious disease, collected data will often be noisy and incomplete. Although there may not be a large historical dataset of patients suffering from this disease, existing databases, assuming they have been properly mined, and patterns based on these data can contribute significantly to the choice of the most appropriate behaviour to adopt in each situation [232]. Many studies report the potential of the application of Big Data and AI technology to contribute to prevention, diagnosis, treatment and decision making concerning acute infectious public health events in the future.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, many of the reviewed studies dealt with sensitive personal data, which, even during a pandemic, must be carefully handled [235]. Therefore, this emergency situation has been a precious opportunity to behave in a more ethical and more responsible manner [232]. A limitation of our review is that it is almost impossible to cover all the (still) continuously appearing publications, which range in the tens of thousands.…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

Badiola-Zabala,

Lopez-Guede,

Estevez

et al. 2024

Electronics

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Background: The declaration of the COVID-19 pandemic triggered global efforts to control and manage the virus impact. Scientists and researchers have been strongly involved in developing effective strategies that can help policy makers and healthcare systems both to monitor the spread and to mitigate the impact of the COVID-19 pandemic. Machine Learning (ML) and Artificial Intelligence (AI) have been applied in several fronts of the fight. Foremost is diagnostic assistance, encompassing patient triage, prediction of ICU admission and mortality, identification of mortality risk factors, and discovering treatment drugs and vaccines. Objective: This systematic review aims to identify original research studies involving actual patient data to construct ML- and AI-based models for clinical decision support for early response during the pandemic years. Methods: Following the PRISMA methodology, two large academic research publication indexing databases were searched to investigate the use of ML-based technologies and their applications in healthcare to combat the COVID-19 pandemic. Results: The literature search returned more than 1000 papers; 220 were selected according to specific criteria. The selected studies illustrate the usefulness of ML with respect to supporting healthcare professionals for (1) triage of patients depending on disease severity, (2) predicting admission to hospital or Intensive Care Units (ICUs), (3) search for new or repurposed treatments and (4) the identification of mortality risk factors. Conclusion: The ML/AI research community was able to propose and develop a wide variety of solutions for predicting mortality, hospitalizations and treatment recommendations for patients with COVID-19 diagnostic, opening the door for further integration of ML in clinical practices fighting this and forecoming pandemics. However, the translation to the clinical practice is impeded by the heterogeneity of both the datasets and the methodological and computational approaches. The literature lacks robust model validations supporting this desired translation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

Badiola-Zabala,

Lopez-Guede,

Estevez

et al. 2024

Electronics

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On the Challenges for the Diagnosis of SARS-CoV-2 Based on a Review of Current Methodologies

et al. 2020

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Diagnosis of COVID-19 has been challenging owing to the need for mass testing and for combining distinct types of detection to cover the different stages of the infection. In this review, we have surveyed the most used methodologies for diagnosis of COVID-19, which can be basically categorized into genetic-material detection and immunoassays. Detection of genetic material with real-time polymerase chain reaction (RT-PCR) and similar techniques has been achieved with high accuracy, but these methods are expensive and require time-consuming protocols which are not widely available, especially in less developed countries. Immunoassays for detecting a few antibodies, on the other hand, have been used for rapid, less expensive tests, but their accuracy in diagnosing infected individuals has been limited. We have therefore discussed the strengths and limitations of all of these methodologies, particularly in light of the required combination of tests owing to the long incubation periods. We identified the bottlenecks that prevented mass testing in many countries, and proposed strategies for further action, which are mostly associated with materials science and chemistry. Of special relevance are the methodologies which can be integrated into point-of-care (POC) devices and the use of artificial intelligence that do not require products from a well-developed biotech industry.

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Dilemas bioéticos experimentados por el cuidador respiratorio durante el retiro de la ventilación mecánica en adultos al final de la vida.

Urrego¹,

Guarín²,

Cañón³

et al. 2022

Mov. cient.

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Introducción: sobre el retiro de la ventilación mecánica al final de la vida, la literatura ha documentado brechas conceptuales en los cuidadores respiratorios y los consecuentes dilemas bioéticos a los que se enfrentan estos profesionales. Objetivo: analizar los dilemas bioéticos que experimentan los cuidadores respiratorios durante el retiro de la ventilación mecánica en adultos al final de la vida a partir de una revisión integradora de la literatura publicada entre 2010 y 2021. Metodología: revisión integrativa de la literatura siguiendo los lineamientos de Whittemore & Knafl (2005) y Guirao Goris (2015). Para el análisis de la información se realizó una evaluación crítica de la literatura acopiada para identificar patrones de comportamiento de los cuidadores respiratorios. Resultados: las enfermedades no transmisibles y crónicas fueron identificadas como las principales condiciones clínicas que determinan la toma de decisiones del cuidador respiratorio en el retiro de la ventilación mecánica paliativa; prolongar la vida de forma artificial a pacientes en estado terminal fue el dilema bioético más frecuente. Conclusiones: los profesionales del cuidado respiratorio enfrentan dilemas éticos en el retiro de la ventilación mecánica al final de la vida de pacientes con afecciones oncológicas y no oncológica; la toma de decisiones se realiza principalmente con base en los principios bioéticos clásicos de Respeto por la autonomía, Beneficencia, No maleficencia y Justicia. Se evidencia la carencia de estudios sobre bioética en diálogo con lineamientos de la Declaración Universal sobre Bioética y Derechos humanos.

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“Artificial intelligence & COVID-19: (bio)ethical aspects of end of life

Cited by 3 publications

References 2 publications

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

On the Challenges for the Diagnosis of SARS-CoV-2 Based on a Review of Current Methodologies

Dilemas bioéticos experimentados por el cuidador respiratorio durante el retiro de la ventilación mecánica en adultos al final de la vida.

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