The miseducation of algorithms is a critical problem; when artificial intelligence mirrors unconscious thoughts, racism, and biases of the humans who generated these algorithms, it can lead to serious harm. Computer programs, for example, have wrongly flagged Black defendants as twice as likely to reoffend as someone who's white. When an AI used cost as a proxy for health needs, it falsely named Black patients as healthier than equally sick white ones, as less money was spent on them. Even AI used to write a play relied on using harmful stereotypes for casting. Removing sensitive features from the data seems like a viable tweak.
New research has found that artificial intelligence (AI) analyzing medical scans can identify the race of patients with an astonishing degree of accuracy, while their human counterparts cannot. With the Food and Drug Administration (FDA) approving more algorithms for medical use, the researchers are concerned that AI could end up perpetuating racial biases. They are especially concerned that they could not figure out precisely how the machine-learning models were able to identify race, even from heavily corrupted and low-resolution images. In the study, published on pre-print service Arxiv, an international team of doctors investigated how deep learning models can detect race from medical images. Using private and public chest scans and self-reported data on race and ethnicity, they first assessed how accurate the algorithms were, before investigating the mechanism.
Artificial intelligence (AI) has the potential to expand the role of chest imaging in COVID-19 beyond diagnosis to enable risk stratification, treatment monitoring, and discovery of novel therapeutic targets. AI's power to generate models from large volumes of information – fusing molecular, clinical, epidemiological, and imaging data – may accelerate solutions to detect, contain, and treat COVID-19. Two healthcare workers fell ill in Wuhan, China, where the first Coronavirus Disease 2019 (COVID-19) case was reported. Both were 29 years old and were hospitalized after contracting the virus. One survived, the other died. In a global pandemic that has suddenly pushed doctors, scientists, and healthcare workers to the frontlines, why some patients are falling critically ill while others have minimal or no symptoms is one of the most mysterious aspects of the disease caused by Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2).
Artificial intelligence (AI) is used by medical facilities to help analyze x-rays and other medical scans, but a new study finds the technology can see more than just a patient's health – it can determine their race with startling accuracy. The study's 20 authors found deep learning models can identify race in chest and hand x-rays and mammograms among patients who identified as black, white and Asian. The algorithms correctly identify which images were from a black person more than 90 percent of the time, but also showed it was able to identity race with 99 percent accuracy at times. However, what is even more alarming is that the team was unable to explain how the AI systems were making accurate predictions, some of which were done with scans that were blurry or low-resolution. 'We emphasize that model ability to predict self-reported race is itself not the issue of importance,' Ritu Banerjee, associate professor of pediatrics at Washington University School of Medicine and lead author of the study, and collages wrote in the study published in arXiv.
Chen, Qingyu, Keenan, Tiarnan D. L., Allot, Alexis, Peng, Yifan, Agrón, Elvira, Domalpally, Amitha, Klaver, Caroline C. W., Luttikhuizen, Daniel T., Colyer, Marcus H., Cukras, Catherine A., Wiley, Henry E., Magone, M. Teresa, Cousineau-Krieger, Chantal, Wong, Wai T., Zhu, Yingying, Chew, Emily Y., Lu, Zhiyong
Objective Reticular pseudodrusen (RPD), a key feature of age-related macular degeneration (AMD), are poorly detected by human experts on standard color fundus photography (CFP) and typically require advanced imaging modalities such as fundus autofluorescence (FAF). The objective was to develop and evaluate the performance of a novel'M3' deep learning framework on RPD detection. Materials and Methods A deep learning framework M3 was developed to detect RPD presence accurately using CFP alone, FAF alone, or both, employing 8000 CFP-FAF image pairs obtained prospectively (Age-Related Eye Disease Study 2). The M3 framework includes multi-modal (detection from single or multiple image modalities), multi-task (training different tasks simultaneously to improve generalizability), and multi-attention (improving ensembled feature representation) operation. Performance on RPD detection was compared with state-of-the-art deep learning models and 13 ophthalmologists; performance on detection of two other AMD features (geographic atrophy and pigmentary abnormalities) was also evaluated. Results For RPD detection, M3 achieved area under receiver operating characteristic (AUROC) 0.832, 0.931, and 0.933 for CFP alone, FAF alone, and both, respectively. M3 performance on CFP was very substantially superior to human retinal specialists (median F1-score 0.644 versus 0.350). External validation (on Rotterdam Study, Netherlands) demonstrated high accuracy on CFP alone (AUROC 0.965). The M3 framework also accurately detected geographic atrophy and pigmentary abnormalities (AUROC 0.909 and 0.912, respectively), demonstrating its generalizability. Conclusion This study demonstrates the successful development, robust evaluation, and external validation of a novel deep learning framework that enables accessible, accurate, and automated AMD diagnosis and prognosis. INTRODUCTION Age-related macular degeneration (AMD) is the leading cause of legal blindness in developed countries [1 2]. Late AMD is the stage with the potential for severe visual loss; it takes two forms, geographic atrophy and neovascular AMD. AMD is traditionally diagnosed and classified using color fundus photography (CFP) , the most widely used and accessible imaging modality in ophthalmology. In the absence of late disease, two main features (macular drusen and pigmentary abnormalities) are used to classify disease and stratify risk of progression to late AMD . More recently, additional imaging modalities have become available in specialist centers, particularly fundus autofluorescence (FAF) imaging [4 5]. Following these developments in retinal imaging, a third macular feature (reticular pseudodrusen, RPD) is now recognized as a key AMD lesion [6 7].