3 articles tagged with #clinical-applications. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Research study finds that LLaMA-70B-Instruct hallucinated in 19.7% of medical Q&A responses despite high plausibility scores, highlighting significant reliability issues in AI healthcare applications. The study shows that lower hallucination rates correlate with higher usefulness scores, emphasizing the need for better safeguards in medical AI systems.
Researchers developed a method to improve foundation models in medical histopathology by introducing robustness losses during training, reducing sensitivity to technical variations while maintaining accuracy. The approach was tested on over 27,000 whole slide images from 6,155 patients across eight popular foundation models, showing improved robustness and prediction accuracy without requiring retraining of the foundation models themselves.
Researchers developed a multi-pass LLM post-processing system that significantly improves French clinical speech transcription accuracy by alternating between speaker recognition and word recognition passes. The system achieved significant word error rate reductions in suicide prevention conversations while maintaining stability in neurosurgery consultations with feasible computational costs for clinical deployment.
