Researchers have successfully demonstrated that advanced generative AI (GenAI) models can accurately assess lung adenocarcinoma pathological features with remarkable precision. The comprehensive study shows Claude-3.5-Sonnet achieving 82.3% accuracy in cancer grading, potentially revolutionizing how pathologists diagnose and predict outcomes for lung cancer patients.

Researchers have developed a compact, noninvasive imaging system that combines high-resolution structural imaging with chemical analysis to improve skin cancer diagnosis. The system integrates line-field confocal optical coherence tomography and confocal Raman microspectroscopy, allowing clinicians to examine both the cellular structure and molecular composition of skin tissues. In a year-long clinical study involving over 330 nonmelanoma skin cancer samples, the system enabled targeted chemical analysis of suspicious structures. An AI model trained on the spectral data achieved high accuracy in identifying cancerous tissues, with classification scores of 0.95 for basal cell carcinoma and 0.92 when including squamous cell carcinoma. This dual-modality approach promises to enhance diagnostic precision and deepen understanding of skin cancer biology.

A research team from Southern Medical University has developed a machine learning-based gene model that predicts whether nasopharyngeal cancer (NPC) patients will benefit from radiotherapy. This predictive tool, called the NPC-RSS, was validated in both cell lines and patient samples. The model may guide personalized treatment decisions and improve survival outcomes for NPC patients.