AI could predict suicidal behaviors and save lives
Researchers have developed a new machine learning-based algorithm that shows high accuracy in identifying adolescents who are experiencing suicidal thoughts and behavior. Orion Weller of Johns Hopkins University in Baltimore, Maryland, and his team presented the findings in the open-access journal PLOS ONE on November 3rd, 2021. The new algorithm's accuracy is higher than previous ones, which can improve understanding of adolescent suicidal behaviors and alert. Ai could ultimately improve prevention efforts and new practices tackling this issue.
Ai in radiology
An artificial intelligence-based mammography triage software is helping to improve the interpretation process, according to a case study published Tuesday in the Journal of the American College of Radiology. Patients can often experience delays in receiving their breast imaging results for various reasons, such as physician shortages or failing to bring along previous outside exams. Such delays can lead to worse health and mental conditions. The imaging center in Southern California and its partners implemented an AI software that aids in detection. After two years in, they see improvements in their work. “Triage of screening mammograms resulted in significant improvement in reporting of recalled patients, thereby expediting workup,” lead author Marie Tartar, MD, a radiologist with Scripps Green Hospital in La Jolla, California, and colleagues wrote Oct. 5. “Subjectively, the radiologist experience over 2 years was improved by having fewer, more meaningful flags to evaluate and the perceived benefit of a sorted screening mammography work list,” they added later.
Ai could eliminate animal testing
Animal testing is still happening on a large scale with over 100 million animals undergoing tests for drug discoveries, diseases, pharmaceutical, or beauty industry purposes. However, new findings are showing that AI models can save the lives of millions of animals and replace the testings with computer vision and accurate datasets. This is one of the very possible alternatives to animal testings for drug discoveries. The emergence of quantum computing will make it possible due to large datasets and computing power. In 2016, Thomas Hartung led some researchers from Johns Hopkins University to successfully develop an artificial intelligence algorithm that determines substance toxicity after comparing it to similar databases and predictions from previously conducted animal testing. Not only we will eradicate cruelty, but the Ai will be able to achieve more precise results.