Summary of Speaking the Same Language: Leveraging Llms in Standardizing Clinical Data For Ai, by Arindam Sett et al.
Speaking the Same Language: Leveraging LLMs in Standardizing Clinical Data for AI
by Arindam Sett, Somaye Hashemifar, Mrunal Yadav, Yogesh Pandit, Mohsen Hejrati
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The study proposes using large language models to address challenges in standardizing multi-modal healthcare data. By leveraging these models, clinicians can identify and map clinical data schemas to established data standards, such as Fast Healthcare Interoperability Resources (FHIR). This approach significantly reduces the need for manual data curation, increasing the efficacy of the data standardization process. The methodology has the potential to accelerate the integration of AI in healthcare, improve patient care, while minimizing time and financial resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI can help improve healthcare outcomes, access, and satisfaction. However, there’s a challenge: quality multi-modal healthcare data is hard to come by. To address this issue, researchers used large language models to standardize healthcare data. They found that these models helped identify and map clinical data schemas to established standards like FHIR. This makes it easier to prepare data for AI, which can improve patient care. |
Keywords
» Artificial intelligence » Multi modal
