DeepSeek's new AI model uses a unified multimodal learner for clinical prediction, simplifying the process and achieving state-of-the-art results. This approach converts all patient data into a single natural language sequence and fine-tunes a pretrained language model. The model outperforms task-specific multimodal baselines and a clinically deployed gradient boosting system.

A new study introduces a benchmark evaluation framework for measuring the faithfulness of LLM-generated clinical trial summaries, identifying Unsupported Claims as the dominant failure mode. The study evaluates three language models, including GPT-4o, Claude Sonnet 4.6, and Gemini 2.5 Flash, and proposes a knowledge-graph-augmented retrieval system to improve faithfulness scores. This research has significant implications for the use of LLMs in high-stakes contexts such as healthcare.

Google researchers introduce HCC-STAR, a clinical-reasoning LLM for risk stratification and treatment guidance in hepatocellular carcinoma. This model achieves state-of-the-art performance in treatment recommendation and risk stratification. The study demonstrates the potential of AI in precision therapy for HCC patients.