ética global de la IA en la recuperación del COVID-19: revisión narrativa y análisis ético del Contrato Social Personalista de la optimización de la eficacia de la salud pública y la equidad social impulsada por la IA

Monlezun, Dominique; Sotomayor, Claudia R.; Peters, Nathaniel J; Steigner, Laura; Gallagher, Colleen M.; Garcia, Alberto

doi:10.36105/mye.2022v33n2.02

Cited by 4 publications

(3 citation statements)

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“…Es por ello que las herramientas basadas en la inteligencia artificial en el campo de la salud, se han diversificación con el transcurso del tiempo y el adelanto tecnológico, pudiendo dividirlas en dos ramas: una física que proporciona apoyo al personal de salud durante la atención de los pacientes en aspectos como el diagnóstico, seguimiento y elección del tratamiento, así como pueden brindar soporte al paciente durante el autocuidado 2,4,5 , y una rama virtual mediante la aplicación de un software para el análisis de datos médicos con herramientas que están dirigidas al campo de la investigación, la salud pública e incluso pueden ser utilizadas para orientar políticas públicas 6 . Permitiendo de esta manera que las ciencias de la salud evolucionen a gran velocidad gracias a la creciente información nueva con la que cuenta en bases de datos como por ejemplo MEDLINE la cual integra aproximadamente 700000 nuevos archivos al año, lo que pone en manifiesto lo inabarcable de la información 7,8 .…”

Section: Contextounclassified

La inteligencia artificial un desafío en el campo de la salud-The artificial intelligence, a challenge in the field of health

López,

Rosales-Torres,

Miño-Rodríguez

et al. 2024

Rev Fac Cien Med (Quito)

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El avance de la tecnología ha transformado rápidamente a la sociedad, especialmente en el ámbito de la salud, donde la inteligencia artificial despierta un gran interés debido a sus diversas aplicaciones. Varias de las áreas que utilizan estos sistemas, incluidas las ciencias de la salud, han considerado indispensable incorporar estas herramientas a su campo, es así que a lo largo de las décadas, la inteligencia artificial se ha utilizado en medicina, desde interpretar electrocardiogramas hasta analizar grandes conjuntos de datos para diagnósticos, tratamientos y dirigir políticas públicas. En el campo de las ciencias médicas se identifican dos ramas principales de aplicación: una física, que apoya al personal médico en la atención al paciente, y una virtual, que se centra en la investigación y salud pública. Aunque la inteligencia artificial puede aliviar la carga del personal sanitario, plantea riesgos como la pérdida de contacto directo entre médicos y pacientes, además de dilemas éticos sobre la privacidad de los datos de salud, por lo que es esencial evaluar continuamente su implementación y reflexionar sobre sus implicaciones éticas y sociales. Aunque se reconocen sus beneficios, es crucial abordar críticamente sus posibles impactos y preocupaciones éticas.

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Section: Contextounclassified

La inteligencia artificial un desafío en el campo de la salud-The artificial intelligence, a challenge in the field of health

López,

Rosales-Torres,

Miño-Rodríguez

et al. 2024

Rev Fac Cien Med (Quito)

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“…The second or ethical-policy step within AiCE was then conducted by integrating the above quantitative analyses with ethical analysis using the pluralistic global bioethical framework of the Personalist Social Contract (PSC) [18][19][20] . The PSC is a novel integration of modern ethics (principally utilitarianism-informed Rawlsian social contract of political liberalism, bounded by Kantian deontology and informed by feminist, Marxist, deconstructionist, and ecological ethics) and classical ethics (principally Thomistic-Aristotelian virtue ethics, articulated by William Carlo's esse/essence revision of Norris Clarke's Strong Thomistic Personalism, a derivative formulation of Thomism as a development of classical Aristotelianism metaphysics) [21][22][23][24][25][26][27] .…”

Section: Computational Ethical and Policy Analysismentioning

confidence: 99%

Seasonal Variations of Arrhythmias and Their Impact on Mortality in Cancer Patients with Health Disparities: A Propensity Score Adjusted Machine Learning Analysis of Over 100 Million Hospitalizations Across 3 Years

Park

Monlezun

Kim

et al. 2023

Preprint

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Background Arrhythmias are observed to increase during high influenza activity seasons (HIA, December to February) with significant clinical impact among high-risk patients, so their outcomes may be optimized through closer monitoring of these populations. It is unknown if cancer is such a risk factor. Methods This is a retrospective analysis of arrhythmia-related mortality and the effect of health disparities in patients with cancer during HIA and non-HIA seasons in a nationally representative database. Machine Learning-augmented Propensity Score adjusted multivariable regression (ML-PSr) was performed using the 2016–2018 National Inpatient Sample (NIS), the United States’ largest all-payer hospitalized dataset. Results 16,795,379 (18.48%) patients presented with arrhythmia of whom 3,214,914 (19.14%) were during HIA. In ML-PSr, HIA did not significantly increase the odds of arrhythmia for cancer patients (OR 1.01, 95%CI 0.99–1.03, p = 0.37), but the odds of arrhythmia-related mortality were higher during HIA seasons (OR 1.19, 95%CI 1.12–1.27, p < 0.001) compared to non-HIA seasons (OR 1.17, 95%CI 1.13–1.22, p < 0.001). Primary malignancies with the highest prevalence of arrhythmias during HIA were lung (19.60%), leukemia (11.49%), non-Hodgkin lymphoma (NHL) (8.24%), prostate (8.15%), and multiple myeloma (MM) (6.21%) (p < 0.001). HIA increased arrhythmia-related mortality most for the following primary malignancies by year: gastrointestinal in 2016 (OR 1.15, 95%CI 1.01–1.32, p = 0.039), leukemia in 2017 (OR 1.31, 95%CI 1.10–1.54, p = 0.002), GI in 2018 (OR 1.14, 95%CI 1.01–1.29, p = 0.029), and renal in 2018 (OR 1.54, 95%CI 1.06–2.23, p = 0.025). Among patients with active cancer and arrhythmia, African Americans had significantly greater mortality than Caucasians (OR 1.13, 95%CI 1.03–1.23, p = 0.013) independent of socio-economic and clinical confounders. Conclusion This study suggests arrhythmia-related mortality was higher during HIA seasons compared to non-HIA seasons in cancer patients and showed notable disparities by race and worse outcomes by primary malignancy.

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“…The second or ethical-policy step within AiCE was then conducted by integrating the above quantitative analyses with ethical analysis using the pluralistic global bioethical framework of the Personalist Social Contract (PSC) [51][52][53][54]. The PSC is a novel integration of modern ethics (principally utilitarianism-informed Rawlsian social contract of political liberalism, bounded by Kantian deontology and informed by feminist, Marxist, deconstructionist, and ecological ethics) and classical ethics (principally Thomistic-Aristotelian virtue ethics, articulated by William Carlo's esse/essence revision of Norris Clarke's Strong Thomistic Personalism, a derivative formulation of Thomism as a development of classical Aristotelianism) [55][56][57][58][59][60][61].…”

Section: Ethical and Policy Analysismentioning

confidence: 99%

Artificial Intelligence-Augmented Propensity Score, Cost Effectiveness and Computational Ethical Analysis of Cardiac Arrest and Active Cancer with Novel Mortality Predictive Score

Monlezun

Sinyavskiy

Peters³

et al. 2022

Medicina

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Background and objectives: Little is known about outcome improvements and disparities in cardiac arrest and active cancer. We performed the first known AI and propensity score (PS)-augmented clinical, cost-effectiveness, and computational ethical analysis of cardio-oncology cardiac arrests including left heart catheterization (LHC)-related mortality reduction and related disparities. Materials and methods: A nationally representative cohort analysis was performed for mortality and cost by active cancer using the largest United States all-payer inpatient dataset, the National Inpatient Sample, from 2016 to 2018, using deep learning and machine learning augmented propensity score-adjusted (ML-PS) multivariable regression which informed cost-effectiveness and ethical analyses. The Cardiac Arrest Cardio-Oncology Score (CACOS) was then created for the above population and validated. The results informed the computational ethical analysis to determine ethical and related policy recommendations. Results: Of the 101,521,656 hospitalizations, 6,656,883 (6.56%) suffered cardiac arrest of whom 61,300 (0.92%) had active cancer. Patients with versus without active cancer were significantly less likely to receive an inpatient LHC (7.42% versus 20.79%, p < 0.001). In ML-PS regression in active cancer, post-arrest LHC significantly reduced mortality (OR 0.18, 95%CI 0.14–0.24, p < 0.001) which PS matching confirmed by up to 42.87% (95%CI 35.56–50.18, p < 0.001). The CACOS model included the predictors of no inpatient LHC, PEA initial rhythm, metastatic malignancy, and high-risk malignancy (leukemia, pancreas, liver, biliary, and lung). Cost-benefit analysis indicated 292 racial minorities and $2.16 billion could be saved annually by reducing racial disparities in LHC. Ethical analysis indicated the convergent consensus across diverse belief systems that such disparities should be eliminated to optimize just and equitable outcomes. Conclusions: This AI-guided empirical and ethical analysis provides a novel demonstration of LHC mortality reductions in cardio-oncology cardiac arrest and related disparities, along with an innovative predictive model that can be integrated within the digital ecosystem of modern healthcare systems to improve equitable clinical and public health outcomes.

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ética global de la IA en la recuperación del COVID-19: revisión narrativa y análisis ético del Contrato Social Personalista de la optimización de la eficacia de la salud pública y la equidad social impulsada por la IA

Cited by 4 publications

References 11 publications

La inteligencia artificial un desafío en el campo de la salud-The artificial intelligence, a challenge in the field of health

La inteligencia artificial un desafío en el campo de la salud-The artificial intelligence, a challenge in the field of health

Seasonal Variations of Arrhythmias and Their Impact on Mortality in Cancer Patients with Health Disparities: A Propensity Score Adjusted Machine Learning Analysis of Over 100 Million Hospitalizations Across 3 Years

Artificial Intelligence-Augmented Propensity Score, Cost Effectiveness and Computational Ethical Analysis of Cardiac Arrest and Active Cancer with Novel Mortality Predictive Score

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