308 Background: Artificial intelligence (AI) has significant potential to improve health outcomes in oncology. However, lack of demographic representation in data sets used to train and test these models can lead to model bias and potential disparities. We sought to assess transparency in reporting and representation of race, sex, and age data in published clinical studies utilizing AI in oncology. Materials and Methods: In this scoping review, we used 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. Three investigators reviewed eligible studies to collect data regarding public reporting of patient demographics, 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 had at least one described training or validation data set. Of these, 69 studies (58%) reported age data for patients included in training or validation sets, while 60 studies (51%) reported sex and 6 studies (5%) reported race. Across studies, age ranged from 16 days-96 years. For studies that reported mean age, the overall mean was 57.8+/- 10.8 years. 48.5% of patients included in studies with reported sex were male, and 51.5% were female. 38 studies (63.3%) that reported sex included a majority of males, and 22 studies (36.7%) used a majority of females. From the 6 studies that reported race, 70.7%-93.4% of individuals were White. Only 3 studies (2.5%) reported racial demographic data with more than two categories (i.e. “White” vs. “non-White” or “White” vs. “Black”). Conclusions: In this scoping review of published studies in oncology using AI models, we found that a minority of studies reported complete demographic data of training and validation sets. In particular, studies rarely reported race, and those that did had few non-White patients. Increased transparency regarding demographic data of patients included in data sets for creation of AI models is essential to ensure fair and unbiased clinical integration of AI.