AI-based diabetes care: risk prediction models and implementation concerns
The utilization of artificial intelligence (AI) in diabetes care has focused on early intervention and treatment management. Notably, usage has expanded to predict an individual’s risk for developing type 2 diabetes. A scoping review of 40 studies by Mohsen et al. shows that while most studies used...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-02-01
|
| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-024-01034-7 |
| _version_ | 1797273135197716480 |
|---|---|
| author | Serena C. Y. Wang Grace Nickel Kaushik P. Venkatesh Marium M. Raza Joseph C. Kvedar |
| author_facet | Serena C. Y. Wang Grace Nickel Kaushik P. Venkatesh Marium M. Raza Joseph C. Kvedar |
| author_sort | Serena C. Y. Wang |
| collection | DOAJ |
| description | The utilization of artificial intelligence (AI) in diabetes care has focused on early intervention and treatment management. Notably, usage has expanded to predict an individual’s risk for developing type 2 diabetes. A scoping review of 40 studies by Mohsen et al. shows that while most studies used unimodal AI models, multimodal approaches were superior because they integrate multiple types of data. However, creating multimodal models and determining model performance are challenging tasks given the multi-factored nature of diabetes. For both unimodal and multimodal models, there are also concerns of bias with the lack of external validations and representation of race, age, and gender in training data. The barriers in data quality and evaluation standardization are ripe areas for developing new technologies, especially for entrepreneurs and innovators. Collaboration amongst providers, entrepreneurs, and researchers must be prioritized to ensure that AI in diabetes care is providing quality and equitable patient care. |
| first_indexed | 2024-03-07T14:40:15Z |
| format | Article |
| id | doaj.art-e6ea5b8cd3aa4e99a3774102232037b4 |
| institution | Directory Open Access Journal |
| issn | 2398-6352 |
| language | English |
| last_indexed | 2024-03-07T14:40:15Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj.art-e6ea5b8cd3aa4e99a3774102232037b42024-03-05T20:27:20ZengNature Portfolionpj Digital Medicine2398-63522024-02-01711210.1038/s41746-024-01034-7AI-based diabetes care: risk prediction models and implementation concernsSerena C. Y. Wang0Grace Nickel1Kaushik P. Venkatesh2Marium M. Raza3Joseph C. Kvedar4Harvard Medical SchoolHarvard Medical SchoolHarvard Medical SchoolHarvard Medical SchoolHarvard Medical SchoolThe utilization of artificial intelligence (AI) in diabetes care has focused on early intervention and treatment management. Notably, usage has expanded to predict an individual’s risk for developing type 2 diabetes. A scoping review of 40 studies by Mohsen et al. shows that while most studies used unimodal AI models, multimodal approaches were superior because they integrate multiple types of data. However, creating multimodal models and determining model performance are challenging tasks given the multi-factored nature of diabetes. For both unimodal and multimodal models, there are also concerns of bias with the lack of external validations and representation of race, age, and gender in training data. The barriers in data quality and evaluation standardization are ripe areas for developing new technologies, especially for entrepreneurs and innovators. Collaboration amongst providers, entrepreneurs, and researchers must be prioritized to ensure that AI in diabetes care is providing quality and equitable patient care.https://doi.org/10.1038/s41746-024-01034-7 |
| spellingShingle | Serena C. Y. Wang Grace Nickel Kaushik P. Venkatesh Marium M. Raza Joseph C. Kvedar AI-based diabetes care: risk prediction models and implementation concerns npj Digital Medicine |
| title | AI-based diabetes care: risk prediction models and implementation concerns |
| title_full | AI-based diabetes care: risk prediction models and implementation concerns |
| title_fullStr | AI-based diabetes care: risk prediction models and implementation concerns |
| title_full_unstemmed | AI-based diabetes care: risk prediction models and implementation concerns |
| title_short | AI-based diabetes care: risk prediction models and implementation concerns |
| title_sort | ai based diabetes care risk prediction models and implementation concerns |
| url | https://doi.org/10.1038/s41746-024-01034-7 |
| work_keys_str_mv | AT serenacywang aibaseddiabetescareriskpredictionmodelsandimplementationconcerns AT gracenickel aibaseddiabetescareriskpredictionmodelsandimplementationconcerns AT kaushikpvenkatesh aibaseddiabetescareriskpredictionmodelsandimplementationconcerns AT mariummraza aibaseddiabetescareriskpredictionmodelsandimplementationconcerns AT josephckvedar aibaseddiabetescareriskpredictionmodelsandimplementationconcerns |