Artificial intelligence (AI) tools show promise in overcoming the limitations of traditional anxiety disorders and/or depression, according to the results of a study published in Springer. Investigators established that audio and/or facial video features have been most analyzed, followed by electroencephalography (EEG) signals, to detect anxiety disorders and/or depression. Traditional screening tools include the Columbia Suicide Screen, Risk of Suicide Questionnaire, Suicidal Ideation Questionnaire, and more. These screening programs are often used in schools to assess suicide risk, according to investigators. However, these traditional screening tools have limitations, such as a high prevalence of false positives, a lack of resources because of funding for the assessment programs in schools, others demands on educators and school counselors.

A deep learning algorithm successfully detects periodontal disease from 2D bitewing radiographs, according to research presented at EuroPerio10, the world's leading congress in periodontology and implant dentistry organized by the European Federation of Periodontology (EFP). "Our study shows the potential for artificial intelligence (AI) to automatically identify periodontal pathologies that might otherwise be missed," said study author Dr. Burak Yavuz of Eskisehir Osmangazi University, Turkey. "This could reduce radiation exposure by avoiding repeat assessments, prevent the silent progression of periodontal disease, and enable earlier treatment." Previous studies have examined the use of AI to detect caries, root fractures and apical lesions but there is limited research in the field of periodontology. This study evaluated the ability of deep learning, a type of AI, to determine periodontal status in bitewing radiographs.

Farmland bird species are declining over most of Europe. Birds breeding on the ground, are particularly vulnerable because they are exposed to mechanical operations, like ploughing and sowing, which take place in spring and often accidentally destroy nests. Researchers flew a drone carrying a thermal camera over agricultural fields to record images. These were then fed to an artificial intelligence algorithm capable of accurately identifying nests, a first step to aid their protection. Researchers tested the system in Southern Finland near University of Helsinki's Lammi Biological Station, using wild nests with eggs of the Lapwing Vanellus vanellus.

The same technology that suggests friends for you to tag in photos on social media could provide an exciting new tool to help dermatologists diagnose skin cancer. While artificial intelligence systems for skin cancer detection have shown promise in research settings, however, there is still a lot of work to be done before the technology is appropriate for real-world use. "AI systems for skin cancer detection are still in their very early stages," says board-certified dermatologist Roger S. Ho, MD, MPH, FAAD, assistant professor in the Ronald O. Perelman Department of Dermatology at NYU Langone Health in New York. "Nothing is 100 percent clear-cut yet." One murky area is the skin cancer "scores" that AI algorithms assign to suspicious spots.

During a typical day, we use a variety of applications that, by virtue of their artificial intelligence, automatically understand our speech and provide near-real-time feedback to support decision-making. But is machine learning, one of a number of AI techniques, ready for clinical applications, specifically to accelerate drug development and/or reduce development costs? Machine learning encompasses a variety of algorithmic techniques that clinical drug developers can use to identify and infer patterns to support enhanced/automated decision-making. One such technique is Natural Language Processing, which can be used to "read" scientific text and infer its semantic context in order to search and find information more easily. The main benefit of machine learning and natural language processing is that they can be used to either augment or replace the error-prone manual analysis work performed by people, and they can scale infinitely as the volume and variety of data grow.

During a typical day, we use a variety of applications that, by virtue of their artificial intelligence, automatically understand our speech and provide near-real-time feedback to support decision-making. But is machine learning, one of a number of AI techniques, ready for clinical applications, specifically to accelerate drug development and/or reduce development costs? Machine learning encompasses a variety of algorithmic techniques that clinical drug developers can use to identify and infer patterns to support enhanced/automated decision-making. One such technique is Natural Language Processing, which can be used to "read" scientific text and infer its semantic context in order to search and find information more easily. The main benefit of machine learning and natural language processing is that they can be used to either augment or replace the error-prone manual analysis work performed by people, and they can scale infinitely as the volume and variety of data grow.