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Radiology report generation models can now verbalize calibrated confidence estimates, enabling targeted radiologist review of potentially hallucinated findings.
An RL-aligned LLM can outperform expert toxicologists in identifying ingested substances from heterogeneous clinical data, suggesting a path to AI-assisted decision-making in high-stakes medical environments.
Instruction-tuned LLMs can mine free-text radiology reports to create a knowledge base that significantly improves the accuracy of structured report generation, especially for rare and detailed findings.
