The renewed popularity of AI has many healthcare executives both curious and cautious about how AI can help them work faster, gain precision, improve patient outcomes, and launch innovative new pursuits. Life sciences companies and healthcare providers are eager to discover the best strategies for making their AI investments count.
In our white paper, “Not All Healthcare AI is Created Equal: Accelerating Insights and Improving Outcomes Using Clinical Language Models,” we delve into clinical natural language processing (CNLP) as a powerful alternative to using large language models (LLMs) for training AI to extract insights from healthcare data sources. When CNLP is paired with a robust healthcare-specific ontology like SNOMED CT, the results can yield more rapid, targeted, and valuable analysis.
Discover how:
- CNLP differs from LLMs, enabling AI operations that are more focused and transparent, making them easier to trace and refine.
- Using CNLP with SNOMED CT surfaces granular insights that enable a faster, more effective patient cohort identification process while also improving clinical efficiency and documentation.
- Choosing the right healthcare AI approach can achieve the precision needed to decipher important unstructured data and help overcome biases in AI training.
Read the white paper to learn how Clinithink's innovative approach to AI can unlock valuable insights and improve healthcare outcomes and efficiency for your organization.
