Literature DB >> 30182736

Digital Diabetes Data and Artificial Intelligence: A Time for Humility Not Hubris.

David Kerr1, David C Klonoff2.   

Abstract

In the future artificial intelligence (AI) will have the potential to improve outcomes diabetes care. With the creation of new sensors for physiological monitoring sensors and the introduction of smart insulin pens, novel data relationships based on personal phenotypic and genotypic information will lead to selections of tailored, effective therapies that will transform health care. However, decision-making processes based exclusively on quantitative metrics that ignore qualitative factors could create a quantitative fallacy. Difficult to quantify inputs into AI-based therapeutic decision-making processes include empathy, compassion, experience, and unconscious bias. Failure to consider these "softer" variables could lead to important errors. In other words, that which is not quantified about human health and behavior is still part of the calculus for determining therapeutic interventions.

Entities:  

Keywords:  artificial intelligence; big data analytics; clinical decision-making; human behavior; quantitative fallacy

Mesh:

Year:  2018        PMID: 30182736      PMCID: PMC6313275          DOI: 10.1177/1932296818796508

Source DB:  PubMed          Journal:  J Diabetes Sci Technol        ISSN: 1932-2968


  39 in total

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Authors:  Michael A Babyak
Journal:  Psychosom Med       Date:  2004 May-Jun       Impact factor: 4.312

2.  Medicine and the McNamara fallacy.

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Review 3.  The impact of culturally competent diabetes care interventions for improving diabetes-related outcomes in ethnic minority groups: a systematic review.

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Journal:  Diabet Med       Date:  2012-10       Impact factor: 4.359

Review 4.  Diabetes and technology in 2030: a utopian or dystopian future?

Authors:  D Kerr; C Axelrod; C Hoppe; D C Klonoff
Journal:  Diabet Med       Date:  2018-02-09       Impact factor: 4.359

Review 5.  Big Data Technologies: New Opportunities for Diabetes Management.

Authors:  Riccardo Bellazzi; Arianna Dagliati; Lucia Sacchi; Daniele Segagni
Journal:  J Diabetes Sci Technol       Date:  2015-04-24

6.  Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors.

Authors:  Narges Razavian; Saul Blecker; Ann Marie Schmidt; Aaron Smith-McLallen; Somesh Nigam; David Sontag
Journal:  Big Data       Date:  2015-12       Impact factor: 2.128

7.  Linguistic barriers in diabetes care.

Authors:  G Reach
Journal:  Diabetologia       Date:  2009-06-13       Impact factor: 10.122

Review 8.  Challenges and Opportunities of Big Data in Health Care: A Systematic Review.

Authors:  Clemens Scott Kruse; Rishi Goswamy; Yesha Raval; Sarah Marawi
Journal:  JMIR Med Inform       Date:  2016-11-21

Review 9.  Wearable Sensors for Remote Health Monitoring.

Authors:  Sumit Majumder; Tapas Mondal; M Jamal Deen
Journal:  Sensors (Basel)       Date:  2017-01-12       Impact factor: 3.576

Review 10.  Technical challenges for big data in biomedicine and health: data sources, infrastructure, and analytics.

Authors:  N Peek; J H Holmes; J Sun
Journal:  Yearb Med Inform       Date:  2014-08-15
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  5 in total

1.  Behavioral Theory: The Missing Ingredient for Digital Health Tools to Change Behavior and Increase Adherence.

Authors:  David C Klonoff
Journal:  J Diabetes Sci Technol       Date:  2019-01-24

2.  Pediatric Smart Insulin Pen Use: The Next Best Thing.

Authors:  Jeniece Ilkowitz; Vanessa Wissing; Mary Pat Gallagher
Journal:  J Diabetes Sci Technol       Date:  2021-09-02

3.  Level of Digitalization in Germany: Results of the Diabetes Digitalization and Technology (D.U.T) Report 2020.

Authors:  Timm Roos; Sabine Hochstadt; Winfried Keuthage; Jens Kröger; Andreas Lueg; Hansjörg Mühlen; Lisa Schütte; Nikolaus Scheper; Dominic Ehrmann; Norbert Hermanns; Lutz Heinemann; Bernhard Kulzer
Journal:  J Diabetes Sci Technol       Date:  2020-10-27

4.  Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations.

Authors:  Joanna Kedra; Timothy Radstake; Aridaman Pandit; Xenofon Baraliakos; Francis Berenbaum; Axel Finckh; Bruno Fautrel; Tanja A Stamm; David Gomez-Cabrero; Christian Pristipino; Remy Choquet; Hervé Servy; Simon Stones; Gerd Burmester; Laure Gossec
Journal:  RMD Open       Date:  2019-07-18

5.  Digital Diabetes Care System Observations from a Pilot Evaluation Study in Vietnam.

Authors:  Tran Quang Khanh; Pham Nhu Hao; Eytan Roitman; Itamar Raz; Baruch Marganitt; Avivit Cahn
Journal:  Int J Environ Res Public Health       Date:  2020-02-03       Impact factor: 3.390

  5 in total

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