This review explores how multi-source data-driven machine learning is transforming lung cancer care, from early screening and diagnosis to personalized treatment and prognosis. It examines imaging, multi-omics, liquid biopsy, pathology, and clinical data, alongside traditional and advanced machine learning approaches. By linking data characteristics with model selection and clinical applications, the review highlights opportunities for precision medicine while addressing challenges in data standardization, multimodal integration, interpretability, and effective clinical translation.

A recent review reveals how aberrantly reprogrammed purine metabolism acts as a critical driver of tumor progression and therapy resistance. By comprehensively mapping the regulatory networks of key metabolic enzymes and their inhibitors, the study highlights the selective targeting of purine metabolism networks as a powerful next-generation anticancer strategy.

For sleep-deprived nursing moms, artificial intelligence may feel like a lifeline. In fact, a new national survey from Orlando Health reveals if women ages 18 to 44 had an urgent breastfeeding question or concern half (51%) of them would first turn to an AI chatbot, social media or a search engine.