Of particular protein folding, and materials science [1], it interest are deviations from standard approaches, has not been widely applied to understanding effective such as the use of novel therapeutic techniques, new therapy. Large language models (LLMs) are ways to sequence therapeutic techniques within a already used for analyzing, assisting, and replacing conversation, applications of techniques in unusual [2, 3, 4, 5] therapeutic conversations, but these contexts, and/or more adaptive approaches based on efforts primarily replicate known therapeutic approaches client characteristics. What follows is a proof-ofconcept (e.g., Cognitive Behavioral Therapy [CBT] study and a discussion on how AI can serve and Motivational Interviewing [MI]) rather than contribute as a discovery engine for psychotherapy research.

Recent advances in deep learning enabled the development of AI systems that outperform humans in many tasks and have started to empower scientists and physicians with new tools. In this Comment, we discuss how recent applications of AI to aging research are leading to the emergence of the field of longevity medicine. Aging is a universal feature shared by all living beings. While the rate of aging may vary among individuals and species, the time elapsed since birth is a strong predictor of health status and mortality. Targeting aging may extend the average life expectancy more substantially than prevention or treatment of individual diseases1.

Summary: Novel artificial intelligence software can provide a precise assessment of the progression of geographic atrophy. The technology can also determine the integrity of photoreceptors and detect progressive degenerative changes beyond the main lessons associated with GA. A software based on artificial intelligence (AI), which was developed by researchers at the Eye Clinic of the University Hospital Bonn, Stanford University and University of Utah, enables the precise assessment of the progression of geographic atrophy (GA), a disease of the light sensitive retina caused by age-related macular degeneration (AMD). This innovative approach permits the fully automated measurement of the main atrophic lesions using data from optical coherence tomography, which provides three-dimensional visualization of the structure of the retina. In addition, the research team can precisely determine the integrity of light sensitive cells of the entire central retina and also detect progressive degenerative changes of the so-called photoreceptors beyond the main lesions.

There is no effective treatment for geographic atrophy, one of the most common causes of blindness in industrialized nations. The disease damages cells of the retina and causes them to die. The main lesions, areas of degenerated retina, also known as "geographic atrophy," expand as the disease progresses and result in blind spots in the affected person's visual field. A major challenge for evaluating therapies is that these lesions progress slowly, which means that intervention studies require a long follow-up period. "When evaluating therapeutic approaches, we have so far concentrated primarily on the main lesions of the disease. However, in addition to central visual field loss, patients also suffer from symptoms such as a reduced light sensitivity in the surrounding retina," explains Prof. Dr. Frank G. Holz, Director of the Eye Clinic at the University Hospital Bonn.

A software based on artificial intelligence (AI), which was developed by researchers at the Eye Clinic of the University Hospital Bonn, Stanford University and University of Utah, enables the precise assessment of the progression of geographic atrophy (GA), a disease of the light sensitive retina caused by age-related macular degeneration (AMD). This innovative approach permits the fully automated measurement of the main atrophic lesions using data from optical coherence tomography, which provides three-dimensional visualization of the structure of the retina. In addition, the research team can precisely determine the integrity of light sensitive cells of the entire central retina and also detect progressive degenerative changes of the so-called photoreceptors beyond the main lesions. The findings will be used to assess the effectiveness of new innovative therapeutic approaches. The study has now been published in the journal "JAMA Ophthalmology".