A type of artificial intelligence (AI) called fine-tuned large language models (LLMs) greatly enhances error detection in radiology reports, according to a new study published in Radiology, a journal of the Radiological Society of North America (RSNA). Researchers said the findings point to an important role for this technology in medical proofreading.

A joint research team from The Hong Kong University of Science and Technology and The Hong Kong University of Science and Technology (Guangzhou) has published a perspective article in MedComm - Future Medicine. The article comprehensively evaluates DeepSeek-R1, a Chinese-developed open-source large language model (LLM), and its potential to transform the healthcare landscape.

A deep learning model was able to predict future lung cancer risk from a single low-dose chest CT scan, according to new research published at the ATS 2025 International Conference.

The model, called Sybil, which was originally developed using National Lung Screening Trial (NLST) data by investigators from the Massachusetts Institute of Technology and Harvard Medical School, could be used to guide more personalized lung cancer screening strategies.

Using advanced artificial intelligence (AI), researchers have developed a novel method to make drug development faster and more efficient.

In a new paper, Xia Ning, lead author of the study and a professor of biomedical informatics and computer science and engineering at The Ohio State University, introduces DiffSMol, a generative AI model capable of generating realistic 3D structures of small molecules that can serve as promising drug candidates.

Eyes may be the window to the soul, but a person's biological age could be reflected in their facial characteristics. Investigators from Mass General Brigham developed a deep learning algorithm called FaceAge that uses a photo of a person’s face to predict biological age and survival outcomes for patients with cancer.

An artificial intelligence (AI) model improved outcomes in hospitalized patients by quadrupling the rate of detection and treatment of delirium. The model identifies patients at high risk for delirium and alerts a specially-trained team to assess the patient and create a treatment plan, if needed.

The most effective way to harness the power of artificial intelligence (AI) when screening for breast cancer may be through collaboration with human radiologists - not by wholesale replacing them, says new research co-written by a University of Illinois Urbana-Champaign expert in the intersection of health care and technology.