Researchers tested a large language model (LLM) on peer review tasks for cancer research papers. They found the AI could be abused to generate highly persuasive rejection letters and other fraudulent reviews, such as requests to cite unrelated papers. Crucially, current AI detection tools were largely unable to identify the AI-generated text, posing a significant, hidden threat to academic integrity.

The objective of this study is to assess the diagnostic performance of image analysis-capable generative AI (Gen-AI) (GPT-4-turbo, Google DeepMind's Gemini-pro-vision, and Anthropic’s Claude-3-opus) in interpreting CT images of lung cancer. This is the first study to integrate the diagnostic capabilities of these three models across distinct imaging settings. Additionally, a Likert scale is used to evaluate each model's internal tendencies. By examining the potential and limitations of multimodal large language models (MM-LLMs) for lung cancer diagnosis, this research aims to provide an evidence-based foundation for the future clinical applications of Gen-AI.

This meta-analysis provides preliminary evidence supporting the use of fecal microbiota transplantation (FMT) as a strategy to enhance the efficacy of immune checkpoint inhibitors (ICIs) in patients with advanced or refractory solid tumors.

Researchers have mapped the complex landscape of cytotoxic T lymphocytes (CTLs), revealing how their remarkable diversity and adaptability influence cancer immunotherapy outcomes. This comprehensive analysis identifies key biomarkers and regulatory pathways that could optimize personalized cancer treatments.

Researchers present a machine learning framework that forecasts individual mental health deterioration using limited, real-world data from wearables and smartphones. This enables personalized early interventions, shifting psychiatry from reactive to proactive care.

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.

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.