JMIR Publications released three News and Perspectives stories on how digital technologies are reshaping clinical care, diagnostics, and patient self-management. 

New research indicates that athletes with ADHD are more likely to sustain a concussion, report more severe concussion symptoms and experience differences in concussion diagnosis and management. 

Generative AI is moving into many corners of medical education. One of the most tempting uses is also one of the most time-consuming parts of teaching: assessing student work. For short-answer questions with clear reference answers, large language models have already shown some ability to score responses. But reflective writing is different. It has no single correct answer. It asks students to draw on coursework, clinical exposure, personal experience, and professional values. Can AI still judge such work reliably? A case study published in the Chinese Journal of Medical Education Research offers a cautious answer. Researchers from West China Second University Hospital, Sichuan University; the National Research Center for Educational Materials; and West China Medical Center examined how reasoning-capable large language models performed when assessing reflective writing assignments by medical students. They found that the models could generate relatively complete comments, but still showed clear limitations in scoring consistency, scoring stability, alignment with the grading rubric, and judgments related to academic integrity. The authors conclude that current reasoning-capable large language models are not yet effective tools for independently assessing medical students’ reflective writing. A more realistic path, they argue, is not substitution, but human–AI collaboration.

A gift from AI company Anthropic will provide $1 million in Claude API credits to support research and education at the Data Sciences Institute at the University of Toronto. These usage credits will grant access to Claude’s Application Programming Interface (API), which enables interaction with Claude models. The initiative will equip researchers with access to cutting-edge AI tools that can support discovery, analysis, software development, and innovation across disciplines.

Researchers have demonstrated a novel AI model that can predict which DNA molecules bind with which other DNA molecules. Providing a more thorough understanding of these hypercomplex binding relationships has utility in applications ranging from biomedical diagnostic tools to DNA computing.