The American Society of Clinical Oncology (ASCO) has released the principles for the responsible use of artificial intelligence (AI) in oncology emphasizing fairness, accountability, oversight, equity, and transparency. However, the extent to which these principles are followed is unknown. The goal of this study was to assess the presence of biases and the quality of studies on AI models according to the ASCO principles and examine their potential impact through citation analysis and subsequent research applications. A review of original research articles centered on the evaluation of predictive models for cancer diagnosis published in the ASCO journal dedicated to informatics and data science in clinical oncology was conducted. Seventeen potential bias criteria were used to evaluate the sources of bias in the studies, aligned with the ASCO’s principles for responsible AI use in oncology. The CREMLS checklist was applied to assess the study quality, focusing on the reporting standards, and the performance metrics along with citation counts of the included studies were analyzed. Nine studies were included. The most common biases were environmental and life-course bias, contextual bias, provider expertise bias, and implicit bias. Among the ASCO principles, the least adhered to were transparency, oversight and privacy, and human-centered AI application. Only 22% of the studies provided access to their data. The CREMLS checklist revealed the deficiencies in methodology and evaluation reporting. Most studies reported performance metrics within moderate to high ranges. Additionally, two studies were replicated in the subsequent research. In conclusion, most studies exhibited various types of bias, reporting deficiencies, and failure to adhere to the principles for responsible AI use in oncology, limiting their applicability and reproducibility. Greater transparency, data accessibility, and compliance with international guidelines are recommended to improve the reliability of AI-based research in oncology.

Background: Although the Hospital Anxiety and Depression Scale (HADS) has been widely studied across various populations, there is still no consensus on its factor structure. This study aims to evaluate the psychometric properties of the HADS in cancer patients.Methods: Our study was cross-sectional and non-probabilistic. It involved 467 cancer patients aged 18 years and over, who were treated at a public institution specialized in oncology. The Hospital Anxiety and Depression Scale, the Beck Anxiety Inventory, and the Beck Depression Inventory were used. We evaluated their internal structure, measurement invariance, relationship with other variables, and reliability.Results: It was found that the HADS is best suited to a bifactorial structure where there is one general factor (emotional distress) and two specific factors (anxiety and depression). The HADS demonstrates invariance with respect to sex and years of education. It shows a moderate correlation with the Beck Anxiety Inventory and the Beck Depression Inventory. In addition, it presents acceptable levels of reliability and relationship with instruments used in the diagnosis of anxiety and depression.Conclusion: The HADS is best suited to a bifactorial structure in cancer populations, with comparisons across both sexes and varying levels of education. Its brevity, versatility, hospital-focused design, and extensive validation make the HADS a very important instrument in the detection of anxiety and depression in cancer patients.