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Almanac: Retrieval-Augmented Language Models for Clinical Medicine
Cyril Zakka1, Akash Chaurasia1,2, Rohan Shad3
1Department of Cardiothoracic Surgery, Stanford Medicine.
Almanac, a new large language model, improves medical guideline accuracy and safety for clinical decision-making. This AI framework enhances factuality and completeness in patient care recommendations.
Area of Science:
- Artificial Intelligence
- Clinical Medicine
- Natural Language Processing
Background:
- Large language models (LLMs) show promise in medicine but generate inaccuracies and toxic content.
- Clinical adoption is limited by reliability concerns in LLM outputs.
Conclusions:
- LLMs, like Almanac, have potential as effective clinical decision-making tools.
- Careful testing and deployment are crucial to mitigate LLM shortcomings in healthcare.
- Almanac shows promise for improving AI-assisted medical guidance.
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