Summary of Clinical Insights: a Comprehensive Review Of Language Models in Medicine, by Nikita Neveditsin et al.
Clinical Insights: A Comprehensive Review of Language Models in Medicine
by Nikita Neveditsin, Pawan Lingras, Vijay Mago
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study delves into the current state of language models in healthcare, investigating their clinical applications. The research highlights the shift from traditional encoder-based systems requiring extensive fine-tuning to modern large language and multimodal models that can learn contextually through in-context learning. The analysis focuses on locally deployable models, emphasizing data privacy and operational autonomy, which are applied in tasks such as text generation, classification, information extraction, and conversational systems. Additionally, the paper proposes a structured organization of tasks and a tiered ethical approach, providing a valuable resource for researchers and practitioners. Key challenges addressed include ethics, evaluation, and implementation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how language models can help in healthcare. It looks at how these models have changed over time and how they’re being used now. The research focuses on ways to keep patient data private while still using the models’ power. Applications include generating medical reports, diagnosing patients, and creating chatbots for doctors. The paper also suggests a way to organize tasks and make ethical decisions about using these models in healthcare. Overall, this study is important because it helps researchers and healthcare professionals understand how language models can be used safely and effectively. |
Keywords
» Artificial intelligence » Classification » Encoder » Fine tuning » Text generation
