Millions of people already chat about their mental health with large language models (LLMs), the conversational form of artificial intelligence. Some providers have integrated LLM-based mental healthcare tools into routine workflows. John Torous, MD, MBI and colleagues, of the Division of Digital Psychiatry at Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, urge clinicians to take immediate action to ensure these tools are safe and helpful, not wait for ideal evaluation methodology to be developed. In the November issue of the Journal of Psychiatric Practice®, part of the Lippincott portfolio from Wolters Kluwer, they present a real-world approach and explain the rationale.

A new study reveals that the human brain processes spoken language in a sequence that closely mirrors the layered architecture of advanced AI language models. Using electrocorticography data from participants listening to a narrative, the research shows that deeper AI layers align with later brain responses in key language regions such as Broca’s area. The findings challenge traditional rule-based theories of language comprehension and introduce a publicly available neural dataset that sets a new benchmark for studying how the brain constructs meaning.

Researchers at Thomas Jefferson University have developed a groundbreaking automated machine learning (AutoML) model that can accurately differentiate between two common types of brain tumors using preoperative MRI scans, potentially improving surgical planning and patient outcomes.

The study, published in the December 2025 issue of Otolaryngology–Head and Neck Surgery, represents the first application of AutoML technology specifically trained to classify pituitary macroadenomas and parasellar meningiomas—two benign but challenging-to-distinguish brain tumors that require different treatment approaches.

Drug discovery is a high-risk, high-cost endeavor, with over 90% of candidates failing in clinical development. A significant proportion of these failures stem from unresolved biopharmaceutic challenges—poor solubility, limited permeability, transporter-mediated efflux, and extensive first-pass metabolism—which compromise a drug's absorption and bioavailability, rendering it ineffective in vivo. Traditional formulation and screening methods have proven inadequate for the modern pipeline of complex molecules. This review examines how the integration of artificial intelligence (AI)-driven predictive tools and innovative formulation technologies, such as 3D printing and biodegradable nanocarriers, is revolutionizing the approach to these age-old limitations, offering a pathway to more efficient and personalized drug development.