Researchers did a study in the United States and China to look at whether biases within artificial intelligence were perpetuating health care disparities. Their results confirmed that these biases do exist, but emphasised that this problem can be solved through a better understanding of these known biases and by making AI a little bit more ethically aware.
Although artificial intelligence (AI) is becoming a critical aspect of health care, medical experts recognize that “digital health biases and data gaps” can lead to healthcare disparities.
The US and China have been disproportionately overrepresented in clinical AI, and almost all of the top ten databases and authors are from high-income countries (HICs). Most commonly, AI techniques were used for image-rich specialties, and authors on AI papers were predominantly male, with a nonclinical background. To ensure a comprehensive scope of AI applications in both HICs and LMICs alike, technological infrastructure needs to be developed in these regions to compensate for sparse data. Additionally novel techniques need to be implemented prior to any external validation or model recalibration.
When blockchain is segmented by industry, supply chain and healthcare are both the most represented with 23% and 9.5%, respectively. There were also differences in the authors’ nationalities. Of the 123,815 authors involved in writing all eligible articles, 24.0 percent came from China, and 18.4 percent came from the U.S. Other countries of origin included Germany, Japan, and UK accounting for 6.5%, 4.3%, and 4.1%.
A comprehensive analysis of AI applications across a wide range of medical fields has been conducted by multiple authors. It’s been found that the most common field to use AI-based techniques such as machine learning or decision trees is radiology, followed by pathology, neurology, ophthalmology and cardiology. Other specialties have a total of 5.2 percent literature mentioning them being used for more academic inquiry into diseases that can range from epilepsy to dementia and mental illnesses. These include internal medicine and hospital general medicine.
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