The field of predictive modeling for cancer treatment and neurodegenerative diseases is rapidly evolving, with a focus on developing innovative frameworks that integrate multimodal data and leverage advanced machine learning techniques. Recent studies have demonstrated the potential of automated segmentation algorithms, radiomics-based survival analysis, and multimodal fusion methods to improve patient outcomes and enhance treatment precision. Notably, the use of zero-shot learning algorithms, sparse mixture-of-experts methods, and recurrent neural networks has shown promise in predicting disease progression, treatment response, and patient survival. These advances have significant implications for personalized medicine, enabling clinicians to make more informed decisions and develop tailored treatment strategies.

Noteworthy papers include: Live(r) Die, which presents a fully automated framework for surgical outcome prediction in colorectal liver metastasis, demonstrating a significant improvement in predictive power over existing clinical and genomic biomarkers. A Multimodal Foundation Model to Enhance Generalizability and Data Efficiency for Pan-cancer Prognosis Prediction, which introduces a novel multimodal foundation model that effectively integrates pathology images, clinical reports, and genomics data for precise pan-cancer prognosis prediction, outperforming state-of-the-art models and exhibiting remarkable data efficiency.