P09.14 Predictive Analytics in Real-World Data from Peru: The New Models for Personalized Oncology

Pino, Luis Eduardo; Triana, Iván; Mejía, Juan Camilo Giraldo; Camelo, M.; Gálvez-Nino, Marco; Ruiz, Rossana; Roque, Katia; Moreno, J.; Olivera, Mivael; Valdiviezo, Natalia; Coanqui, Ofelia; Mas, LA

doi:10.1016/j.jtho.2021.01.442

Cited by 2 publications

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“…Actualmente existen múltiples modelos de este tipo. Localmente, por ejemplo, usamos una red neuronal para la predicción de la supervivencia de pacientes con cáncer de pulmón de células no pequeñas avanzado, en una institución colombiana, y otro modelo similar para estimar la supervivencia en pacientes con este mismo diagnóstico, pero con mutación en el gen EGFR (14). Logette y colaboradores desarrollaron un modelo de predicción por machine learning respecto a niveles de glucosa en suero y progresión en la severidad de Covid-19 (15).…”

Section: En La Predicción Clínicaunclassified

El encuentro entre la medicina y la matemática

Rubio¹,

2022

Med.

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El encuentro entre la medicina y las matemáticas va mucho más allá de la modelación estadística o la biofísica. La matemática aplicada en medicina es la fuente principal de los modelos avanzados de analítica, pero a la vez permite optimizar la minería de datos, los métodos de visualización, la algoritmización y posteriormente los razonadores alojados en plataformas de salud. Presentamos en este capítulo un breve resumen de las técnicas matemáticas de mayor uso para la evolución de la medicina computacional y su posterior migración a modelos mecanicistas.

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Section: En La Predicción Clínicaunclassified

El encuentro entre la medicina y la matemática

Rubio¹,

2022

Med.

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The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review

Istasy¹,

Lee²,

Iansavichene³

et al. 2022

J Med Internet Res

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Background The field of oncology is at the forefront of advances in artificial intelligence (AI) in health care, providing an opportunity to examine the early integration of these technologies in clinical research and patient care. Hope that AI will revolutionize health care delivery and improve clinical outcomes has been accompanied by concerns about the impact of these technologies on health equity. Objective We aimed to conduct a scoping review of the literature to address the question, “What are the current and potential impacts of AI technologies on health equity in oncology?” Methods Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases from January 2000 to August 2021 for records engaging with key concepts of AI, health equity, and oncology. We included all English-language articles that engaged with the 3 key concepts. Articles were analyzed qualitatively for themes pertaining to the influence of AI on health equity in oncology. Results Of the 14,011 records, 133 (0.95%) identified from our review were included. We identified 3 general themes in the literature: the use of AI to reduce health care disparities (58/133, 43.6%), concerns surrounding AI technologies and bias (16/133, 12.1%), and the use of AI to examine biological and social determinants of health (55/133, 41.4%). A total of 3% (4/133) of articles focused on many of these themes. Conclusions Our scoping review revealed 3 main themes on the impact of AI on health equity in oncology, which relate to AI’s ability to help address health disparities, its potential to mitigate or exacerbate bias, and its capability to help elucidate determinants of health. Gaps in the literature included a lack of discussion of ethical challenges with the application of AI technologies in low- and middle-income countries, lack of discussion of problems of bias in AI algorithms, and a lack of justification for the use of AI technologies over traditional statistical methods to address specific research questions in oncology. Our review highlights a need to address these gaps to ensure a more equitable integration of AI in cancer research and clinical practice. The limitations of our study include its exploratory nature, its focus on oncology as opposed to all health care sectors, and its analysis of solely English-language articles.

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P09.14 Predictive Analytics in Real-World Data from Peru: The New Models for Personalized Oncology

Cited by 2 publications

References 0 publications

El encuentro entre la medicina y la matemática

El encuentro entre la medicina y la matemática

The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review

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