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Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists

Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advanced medical technologies, innovation, and digital communication. Thanks to machine learning, we are not limited anymore to a descriptive analysis of the...

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Autores principales: Musacchio, Nicoletta, Giancaterini, Annalisa, Guaita, Giacomo, Ozzello, Alessandro, Pellegrini, Maria A, Ponzani, Paola, Russo, Giuseppina T, Zilich, Rita, de Micheli, Alberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338925/
https://www.ncbi.nlm.nih.gov/pubmed/32568088
http://dx.doi.org/10.2196/16922
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author Musacchio, Nicoletta
Giancaterini, Annalisa
Guaita, Giacomo
Ozzello, Alessandro
Pellegrini, Maria A
Ponzani, Paola
Russo, Giuseppina T
Zilich, Rita
de Micheli, Alberto
author_facet Musacchio, Nicoletta
Giancaterini, Annalisa
Guaita, Giacomo
Ozzello, Alessandro
Pellegrini, Maria A
Ponzani, Paola
Russo, Giuseppina T
Zilich, Rita
de Micheli, Alberto
author_sort Musacchio, Nicoletta
collection PubMed
description Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advanced medical technologies, innovation, and digital communication. Thanks to machine learning, we are not limited anymore to a descriptive analysis of the data, as we can obtain greater value by identifying and predicting patterns resulting from inductive reasoning. Machine learning software programs that disclose the reasoning behind a prediction allow for “what-if” models by which it is possible to understand if and how, by changing certain factors, one may improve the outcomes, thereby identifying the optimal behavior. Currently, diabetes care is facing several challenges: the decreasing number of diabetologists, the increasing number of patients, the reduced time allowed for medical visits, the growing complexity of the disease both from the standpoints of clinical and patient care, the difficulty of achieving the relevant clinical targets, the growing burden of disease management for both the health care professional and the patient, and the health care accessibility and sustainability. In this context, new digital technologies and the use of artificial intelligence are certainly a great opportunity. Herein, we report the results of a careful analysis of the current literature and represent the vision of the Italian Association of Medical Diabetologists (AMD) on this controversial topic that, if well used, may be the key for a great scientific innovation. AMD believes that the use of artificial intelligence will enable the conversion of data (descriptive) into knowledge of the factors that “affect” the behavior and correlations (predictive), thereby identifying the key aspects that may establish an improvement of the expected results (prescriptive). Artificial intelligence can therefore become a tool of great technical support to help diabetologists become fully responsible of the individual patient, thereby assuring customized and precise medicine. This, in turn, will allow for comprehensive therapies to be built in accordance with the evidence criteria that should always be the ground for any therapeutic choice.
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spelling pubmed-73389252020-07-14 Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists Musacchio, Nicoletta Giancaterini, Annalisa Guaita, Giacomo Ozzello, Alessandro Pellegrini, Maria A Ponzani, Paola Russo, Giuseppina T Zilich, Rita de Micheli, Alberto J Med Internet Res Viewpoint Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advanced medical technologies, innovation, and digital communication. Thanks to machine learning, we are not limited anymore to a descriptive analysis of the data, as we can obtain greater value by identifying and predicting patterns resulting from inductive reasoning. Machine learning software programs that disclose the reasoning behind a prediction allow for “what-if” models by which it is possible to understand if and how, by changing certain factors, one may improve the outcomes, thereby identifying the optimal behavior. Currently, diabetes care is facing several challenges: the decreasing number of diabetologists, the increasing number of patients, the reduced time allowed for medical visits, the growing complexity of the disease both from the standpoints of clinical and patient care, the difficulty of achieving the relevant clinical targets, the growing burden of disease management for both the health care professional and the patient, and the health care accessibility and sustainability. In this context, new digital technologies and the use of artificial intelligence are certainly a great opportunity. Herein, we report the results of a careful analysis of the current literature and represent the vision of the Italian Association of Medical Diabetologists (AMD) on this controversial topic that, if well used, may be the key for a great scientific innovation. AMD believes that the use of artificial intelligence will enable the conversion of data (descriptive) into knowledge of the factors that “affect” the behavior and correlations (predictive), thereby identifying the key aspects that may establish an improvement of the expected results (prescriptive). Artificial intelligence can therefore become a tool of great technical support to help diabetologists become fully responsible of the individual patient, thereby assuring customized and precise medicine. This, in turn, will allow for comprehensive therapies to be built in accordance with the evidence criteria that should always be the ground for any therapeutic choice. JMIR Publications 2020-06-22 /pmc/articles/PMC7338925/ /pubmed/32568088 http://dx.doi.org/10.2196/16922 Text en ©Nicoletta Musacchio, Annalisa Giancaterini, Giacomo Guaita, Alessandro Ozzello, Maria A Pellegrini, Paola Ponzani, Giuseppina T Russo, Rita Zilich, Alberto de Micheli. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 22.06.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Viewpoint
Musacchio, Nicoletta
Giancaterini, Annalisa
Guaita, Giacomo
Ozzello, Alessandro
Pellegrini, Maria A
Ponzani, Paola
Russo, Giuseppina T
Zilich, Rita
de Micheli, Alberto
Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists
title Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists
title_full Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists
title_fullStr Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists
title_full_unstemmed Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists
title_short Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists
title_sort artificial intelligence and big data in diabetes care: a position statement of the italian association of medical diabetologists
topic Viewpoint
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338925/
https://www.ncbi.nlm.nih.gov/pubmed/32568088
http://dx.doi.org/10.2196/16922
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