Artificial Intelligence in Breast Cancer Diagnosis and Treatment: Advances in Imaging, Pathology, and Personalized Care.

Breast cancer is the most prevalent cancer worldwide, affecting both low- and middle-income countries, with a growing number of cases. In 2024, about 310,720 women in the U.S.

Surgical treatment of pulmonary hydatid disease: a nine-year single-center experience.

Hydatidosis is one of the most critical parasitic zoonotic diseases worldwide. Lungs are the second most common site of the disease.

Artificial Intelligence Algorithms and Their Current Role in the Identification and Comparison of Gleason Patterns in Prostate Cancer Histopathology: A Comprehensive Review.

The development of the Gleason grading system has proven to be an irreplaceable tool in prostate cancer diagnostics within urology. Despite the advancements and developments in diagnostics, there remains a discrepancy in the grading process among even the most experienced pathologists. AI algorithms have demonstrated potential in detecting cancer and assigning Gleason grades, offering a solution to the issue of significant variability among pathologists' evaluations.

Combined laser and mechanical lithotripsy for endoscopic management of Bouveret's syndrome: A case report.

Bouveret's syndrome (BS) represents an exceedingly rare clinical entity characterized by gastric outlet obstruction induced by a gallstone passing through a cholecystoduodenal, cholecystogastric or choledochoduodenal fistula and impacting in the duodenum or pylorus. Endoscopy is the preferred first-line therapy. It has a favorable safety profile, but requires high level of expertise to achieve stone clearance.

The expanding role of artificial intelligence in the histopathological diagnosis in urological oncology: a literature review.

The ongoing growth of artificial intelligence (AI) involves virtually every aspect of oncologic care in medicine. Although AI is in its infancy, it has shown great promise in the diagnosis of oncologic urological conditions. This paper aims to explore the expanding role of artificial intelligence in the histopathological diagnosis in urological oncology.