ArticleJournal of diabetes science and technology2019
Digital Diabetes Data and Artificial Intelligence: A Time for Humility Not Hubris.
Article in Journal of diabetes science and technology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations.RMD open · 2019Pooled it
- Recommended guidelines concerning artificial intelligence and health equity: a scoping review.Health affairs scholar · 2026Review
- Artificial Intelligence to Diagnose Complications of Diabetes.Journal of diabetes science and technology · 2025Review
- Early Detection of Dementia in Populations With Type 2 Diabetes: Predictive Analytics Using Machine Learning Approach.Journal of medical Internet research · 2024Article
- The Stakeholders' Involvement in Healthcare 4.0 Services Provision: The Perspective of Co-Creation.International journal of environmental research and public health · 2023Article
- Co-Creation in Health 4.0 as a New Solution for a New Era.Healthcare (Basel, Switzerland) · 2023Article
- Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes.Journal of diabetes science and technology · 2023Article
- Artificial intelligence and health inequities in primary care: a systematic scoping review and framework.Family medicine and community health · 2022Article
- Pediatric Smart Insulin Pen Use: The Next Best Thing.Journal of diabetes science and technology · 2022Article
- Level of Digitalization in Germany: Results of the Diabetes Digitalization and Technology (D.U.T) Report 2020.Journal of diabetes science and technology · 2022Article
- Artificial intelligence technologies and compassion in healthcare: A systematic scoping review.Frontiers in psychology · 2022Article
- The impact of race and socioeconomic factors on paediatric diabetes.EClinicalMedicine · 2021Review
- Digital Diabetes Care System Observations from a Pilot Evaluation Study in Vietnam.International journal of environmental research and public health · 2020Article
- Behavioral Theory: The Missing Ingredient for Digital Health Tools to Change Behavior and Increase Adherence.Journal of diabetes science and technology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.