Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review
ABSTRACT Introduction Artificial intelligence (AI) has significant potential to improve health outcomes in oncology. However, as AI utility increases, it is imperative to ensure that these models do not systematize racial and ethnic bias and further perpetuate disparities in health. This scoping rev...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-03-01
|
| Series: | Cancer Medicine |
| Online Access: | https://doi.org/10.1002/cam4.70728 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | ABSTRACT Introduction Artificial intelligence (AI) has significant potential to improve health outcomes in oncology. However, as AI utility increases, it is imperative to ensure that these models do not systematize racial and ethnic bias and further perpetuate disparities in health. This scoping review evaluates the transparency of demographic data reporting and diversity of participants included in published clinical studies utilizing AI in oncology. Methods We utilized PubMed to search for peer‐reviewed research articles published between 2016 and 2021 with the query type “(“deep learning” or “machine learning” or “neural network” or “artificial intelligence”) and (“neoplas$” or “cancer$” or “tumor$” or “tumour$”).” We included clinical trials and original research studies and excluded reviews and meta‐analyses. Oncology‐related studies that described data sets used in training or validation of the AI models were eligible. Data regarding public reporting of patient demographics were collected, including age, sex at birth, and race. We used descriptive statistics to analyze these data across studies. Results Out of 220 total studies, 118 were eligible and 47 (40%) had at least one described training or validation data set publicly available. 69 studies (58%) reported age data for patients included in training or validation sets, 60 studies (51%) reported sex, and six studies (5%) reported race. Of the studies that reported race, a range of 70.7%–93.4% of individuals were White. Only three studies reported racial demographic data with greater than two categories (i.e. “White” vs. “non‐White” or “White” vs. “Black”). Conclusions We found that a minority of studies (5%) analyzed reported racial and ethnic demographic data. Furthermore, studies that did report racial demographic data had few non‐White patients. Increased transparency regarding reporting of demographics and greater representation in data sets is essential to ensure fair and unbiased clinical integration of AI in oncology. |
|---|---|
| ISSN: | 2045-7634 |