Evidence map›Paper›PMID 32568088›Full record

ArticleJournal of medical Internet research2020

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

Nicoletta Musacchio, Annalisa Giancaterini, Giacomo Guaita, Alessandro Ozzello, Maria A Pellegrini, Paola Ponzani, Giuseppina T Russo, Rita Zilich, Alberto de Micheli

Open access · goldAbstract readConsensus Statement
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
8.0field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 40 citations in OpenAlex.

  1. Review
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  4. Article
  5. Review
  6. Artificial intelligence perspective in the future of endocrine diseases.Journal of diabetes and metabolic disorders · 2022
    Review
  7. Article
  8. Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Nicoletta MusacchioItalian Association of Diabetologists, Rome, Italy.ORCID 0000-0001-9530-2869
Annalisa GiancateriniDiabetology Service, Muggiò Polyambulatory, Azienda Socio Sanitaria Territoriale, Monza, Italy.ORCID 0000-0002-4946-3132
Giacomo GuaitaDiabetology, Endocrinology and Metabolic Diseases Service, Azienda Tutela Salute Sardegna-Azienda Socio Sanitaria Locale, Carbonia, Italy.ORCID 0000-0002-6709-5898
Alessandro OzzelloDepartmental Structure of Endocrine Diseases and Diabetology, Azienda Sanitaria Locale TO3, Pinerolo, Italy.ORCID 0000-0002-0876-5134
Maria A PellegriniItalian Association of Diabetologists, Rome, Italy.ORCID 0000-0002-1582-0314
Paola PonzaniOperative Unit of Diabetology, La Colletta Hospital, Azienda Sanitaria Locale 3, Genova, Italy.ORCID 0000-0002-0961-0302
Giuseppina T RussoDepartment of Clinical and Experimental Medicine, University of Messina, Messina, Italy.ORCID 0000-0002-4565-3131
Rita ZilichKnowledge Management - Mix-x, Milano, Italy.ORCID 0000-0002-4590-9395
Alberto de MicheliAssociazione dei Cavalieri Italiani del Sovrano Militare Ordine di Malta, Genova, Italy.ORCID 0000-0002-7105-3148
ASL Roma · ITAzienda Socio Sanitaria Territoriale Lariana · ITUniversity of Messina · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Big DataArtificial IntelligenceAssociationClinical Decision-MakingDiabetes MellitusHumansItalyMachine LearningPhysiciansPrecision Medicineartificial intelligencebig data analyticsclinical decision makingdiabetes managementhealth care

Identifiers

PMID32568088
PMCPMC7338925
OpenAlexW3015334216

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.