A web tool designed to spark reminiscence could help people with dementia and their caregivers feel more connected to each other and less impacted by feelings of pre-death grief, according to a clinical trial co-led by USC and Weill Cornell Medicine published in JAMA Network Open.

With features such as photo albums, autobiographical questions, and journaling prompts, the Living Memory Home for Dementia Care Pairs (LMH-4-DCP) website is a customizable virtual space that facilitates collaborative and interactive reminiscence therapy for both dementia patients and caregivers, said lead author Francesca Falzarano, assistant professor at the USC Leonard Davis School of Gerontology.

Shriners Children’s has named internationally renowned pediatric hand surgeon Scott H. Kozin, M.D. as its Chief Medical Officer, tapping a globally recognized innovator to lead the nonprofit healthcare system through its next phase of growth and medical advancement.

Target identification is a critical and challenging step in drug discovery, with only a small fraction of human genes considered druggable and even fewer successfully targeted by approved therapies. Traditionally, this process has been slow and complex, but artificial intelligence (AI) is transforming it into a more systematic, data-driven approach. 

A recent review by Insilico Medicine highlights how AI is accelerating target discovery by integrating vast multimodal datasets—including omics data, clinical records, imaging, and scientific literature—to uncover novel disease mechanisms and therapeutic targets. 

Advanced machine learning methods, such as supervised and unsupervised learning, graph neural networks, and generative AI models, enable researchers to prioritize targets, simulate biological systems, and generate new hypotheses with greater precision. Platforms like PandaOmics and emerging “virtual biologist” AI agents further enhance this capability by synthesizing complex biological knowledge. 

Clinical progress demonstrates the real-world impact of these approaches, with AI-enabled platforms accelerating timelines and contributing to promising therapies, such as the TNIK inhibitor rentosertib for idiopathic pulmonary fibrosis. 

Looking ahead, the field is moving toward AI-driven closed-loop systems that integrate computational predictions with automated experimentation, aiming to significantly improve efficiency, success rates, and the delivery of new treatments to patients.