ABSTRACT
Artificial intelligence (AI) and machine learning are central components of today's medical environment. The fairness of AI, i.e. the ability of AI to be free from bias, has repeatedly come into question. This study investigates the diversity of members of academia whose scholarship poses questions about the fairness of AI. The articles that combine the topics of fairness, artificial intelligence, and medicine were selected from Pubmed, Google Scholar, and Embase using keywords. Eligibility and data extraction from the articles were done manually and cross-checked by another author for accuracy. Articles were selected for further analysis, cleaned, and organized in Microsoft Excel; spatial diagrams were generated using Public Tableau. Additional graphs were generated using Matplotlib and Seaborn. Linear and logistic regressions were conducted using Python to measure the relationship between funding status, number of citations, and the gender demographics of the authorship team. We identified 375 eligible publications, including research and review articles concerning AI and fairness in healthcare. Analysis of the bibliographic data revealed that there is an overrepresentation of authors that are white, male, and are from high-income countries, especially in the roles of first and last author. Additionally, analysis showed that papers whose authors are based in higher-income countries were more likely to be cited more often and published in higher impact journals. These findings highlight the lack of diversity among the authors in the AI fairness community whose work gains the largest readership, potentially compromising the very impartiality that the AI fairness community is working towards.
ABSTRACT
BACKGROUND: Written informed consent forms (ICFs) are important for ensuring that physicians disclose core information to patients to help them autonomously decide about treatment and for providing substantial evidence for the surgeon in case of a legal dispute. This paper aims to assess the legal and ethical appropriateness and sufficiency of the contents of ICFs designed for several elective surgical procedures currently in use in Turkish hospitals. METHODS: One hundred and twenty-six forms were randomly selected and were analyzed for 22 criteria. The results were compared using the Fisher' exact test, and 95% confidence intervals were calculated. RESULTS: More than 80% of ICFs contained information about the risks of the proposed treatment, the diagnosis of the patient, and the patient's voluntariness/willingness, as well as a designated space for the signatures of the patient and the physician and a description of the proposed treatment. Some ICFs were designed for obtaining blanket consent for using patients' specimens. CONCLUSIONS: The ICFs for general elective surgery contain many deficiencies regarding disclosure of information, and there is significant variation among primary healthcare providers. Unrealistic expectations regarding the surgery or the post-operative recovery period due to insufficient information disclosure may lead patients, who experience post-surgical inconveniences, to file lawsuits against their surgeons. Although all ICFs, regardless of their institution, are generally insufficient for defending hospital administrations or surgeons during a lawsuit, ICFs of private hospitals might be considered better equipped for the situation than those of state or university hospitals. However, further research is needed to show if private hospitals have lower lawsuit rates or better lawsuit outcomes than state or university hospitals in Turkey.
