Researchers at The University of Texas MD Anderson Cancer Center and collaborating institutions in Texas developed an artificial intelligence (AI) model that helps explain how immune cells find their way to specific locations in the body and why they avoid others. The findings could help scientists better understand cancer, autoimmune diseases and treatment response.

JMIR Publications released a feature News and Perspectives article, written by JMIR Correspondent and MD-MBA candidate Tejas S, Athni, PhD, on recent developments out of the UK’s National Commission into the Regulation of AI in Healthcare (established by the Medicines and Healthcare Products Regulatory Agency).

As more and more people turn to AI chatbots for quick, 24/7 medical advice, a new perspective paper co-authored by faculty at Binghamton University, State University of New York explores the risks this practice carries that are largely invisible to users - and healthcare systems.

The paper, written by researchers at Binghamton University, Stanford University, Texas A&M University, and Indiana University, and published in Nature Health, examines what happens when a chatbot gives someone bad health advice.

Artificial Intelligence (AI) has had a transformative effect on healthcare. The technology has been tested and applied in almost every area, from disease diagnostics to AI-powered digital clinics.

The rationale always tracks back to efficiency: AI-technology will achieve more with less. In theory, greater efficiency contributes to sustainability.

A team from the Universitat Politècnica de València (UPV) and the University of Valencia (UV) has developed and validated an artificial intelligence (AI) system capable of distinguishing Ewing sarcoma from other morphologically similar tumours with high accuracy. The model, which also enables the full optimisation of tissue use in biopsies, achieves 91.6% accuracy.

Case-based learning, or CBL, is a teaching approach that asks students to apply what they know to realistic problems, weigh their options, and make decisions before the consequences are real. Today, the approach has evolved well beyond reading a case and discussing it in class.

The studies included in a new George Mason review capture some of these newer approaches.

By analyzing heart rhythm recordings and blood tests, artificial intelligence may accurately flag when a transplant patient’s body starts attacking a donated heart, a new study suggests.

The current gold standard for diagnosing heart transplant rejection is a biopsy, the surgical removal of a small piece of heart muscle, which experts inspect under the microscope for inflammation and other changes that may indicate that the body’s immune system is rejecting an organ.