For children who need braces or jaw correction, timing is everything. If treatment starts before or after a growth spurt, it can be far less effective. But doctors have long struggled to predict exactly when those spurts will happen. Now, a team of South Korean researchers has created an artificial intelligence (AI) tool that can read simple neck X-rays and spot the signs of rapid growth. The technology could give orthodontists a powerful new way to plan care with precision.

Sepsis is a life-threatening condition caused by dysregulation of the immune system after an infection. Corticosteroids are the only known and recommended course of treatment. In a recently published narrative review, researchers describe the mechanisms by which corticosteroids modulate the immune response, causing sepsis and allowing patients to recover. They also describe the other protective effects of corticosteroids and provide a framework to choose the appropriate course of treatment.

The 3rd edition of Artificial Intelligence & Nephrology 2025 (November 20–21, Paris) will explore how generative AI and large language models (LLMs) are transforming kidney medicine. 

A new review in BMC Medicine explores how large language models (LLMs) can enhance the design and conduct of clinical trials, from protocol design and informed consent to patient recruitment, data management, safety monitoring, and outcome prediction. The authors highlight LLMs’ advantages over traditional natural language processing, including contextual understanding, few-shot learning, and multitask capability. While applications such as data mapping and real-time adverse event monitoring show early promise, challenges in data privacy, model transparency, and regulatory alignment must be addressed to ensure safe, effective integration into clinical research.

Controlling the state of a cell in a desired direction is one of the central challenges in life sciences, including drug development, cancer treatment, and regenerative medicine. However, identifying the right drug or genetic target for that purpose is extremely difficult. To address this, researchers at KAIST have mathematically modeled the interaction between cells and drugs in a modular “Lego block” manner—breaking them down and recombining them—to develop a new AI technology that can predict not only new cell–drug reactions never before tested but also the effects of arbitrary genetic perturbations.