The combination of artificial intelligence and human collaboration is transforming the analysis of tissue (anatomopathological) samples obtained from biopsies or surgical specimens. A study published in the journal Nature Communications, with participation from the D’Or Institute for Research and Education (IDOR), presented an innovative collaborative platform capable of segmenting digital tissue images with high precision—even with limited manual data—and identifying markers related to the prognosis of patients with melanoma, a type of skin cancer. Dr. Lidiane Vieira Marins, a pathologist at Rede D'Or and an IDOR researcher, was among the authors representing Brazil in the study.

A survey published in the International Journal of Retina and Vitreous compiled 68 studies to map out how generative artificial intelligence, especially ChatGPT, is being used by professionals in healthcare, particularly in ophthalmic care. The review, which included the participation of the D’Or Institute for Research and Education (IDOR), shows how tools based on large language models (LLMs) are gaining ground in medical routines, especially for triage, preliminary diagnoses, and medical education.

MIT researchers developed a technique that enables LLMs to permanently absorb new knowledge by generating study sheets based on data the model uses to memorize important information.

A new heart monitoring system combining 3D printing and artificial intelligence could transform the way doctors measure and diagnose patients' heart health.

Developed at SFU’s School of Mechatronic Systems Engineering, the system features reusable dry 3D-printed electrodes embedded in a soft chest belt – the folding origami-shaped design uses gentle suction to stick to the skin.

Carbon-based ink printed on the suction cup replaces electrolyte gel, conducting the heart’s electrical signals through to a wearable device with built-in AI software to pre-diagnose of up to 10 types of arrhythmias, or irregular heart rhythms. 

A study published in the Journal of Critical Care, conducted with the participation of the D’Or Institute for Research and Education (IDOR), investigated how to measure efficiency in the use of resources for patients with severe community-acquired pneumonia (CAP), an illness contracted outside hospital settings and most common among older adults.

Severe CAP represents one of the greatest challenges for ICUs. It requires complex resources, ranging from prolonged hospitalizations to respiratory support, directly affecting hospitals’ ability to deliver quality care. Despite its relevance, traditional methods of evaluating hospital performance do not always take patient severity into account, which undermines fair comparisons between institutions and hinders more effective management strategies.

New applications of AI and machine learning techniques were presented at Neuroscience 2025, the annual meeting of the Society for Neuroscience and the world’s largest source of emerging news about brain science and health.