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 study published in the Journal of Patient Experience, with the participation of the D'Or Institute for Research and Education (IDOR), analyzed the perceptions of over 47,000 healthcare professionals on institutional efforts to improve patient experience in private hospitals in Brazil. The research indicated that engaging care teams remains the biggest challenge for concrete improvements in the quality of care.

Heart disease remains the leading cause of death in Brazil, with over 237,000 fatalities in 2024. A study published last year reveals that even among Brazilians diagnosed with arterial diseases, only a tiny fraction follows the full recommendations for cardiovascular prevention. Coordinated by the Hcor Research Institute with participation from the D’Or Institute for Research and Education (IDOR), the NEAT initiative showed that just 0.3% of patients with atherothrombosis adhere to all recommended medical practices to reduce the risk of heart attacks and strokes. The results were published in the journal Scientific Reports.

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.

Assessing the severity of illness in critically ill patients is a crucial task in Intensive Care Units (ICUs) worldwide. However, current systems are often based on local realities and can fail when applied to international contexts. An article published in the journal Critical Care Science, co-authored by researchers from the D'Or Institute for Research and Education (IDOR), argues that an international scoring system is urgently needed for intensive care medicine research and for the advancement of global public health.

Bladder cancer ranks among the ten most common types of cancer worldwide. The main treatment is bladder removal surgery, and despite advances in systemic therapies, recurrence is frequent in the most aggressive forms of the disease. For this reason, researchers have been seeking less invasive and more effective strategies to fight it.

A study involving the D’Or Institute for Research and Education (IDOR) and published in Biochemical Genetics points to a promising path: by blocking a small molecule called miR-21, bladder cancer cells lose their ability to multiply and spread. This discovery could pave the way for future therapies that are both less invasive and more precise.

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.