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The truth about AI in healthcare: How CNLP and LLMs differ in delivering improved outcomes

Written by Clinithink | Nov 14, 2024 3:02:09 PM

Without a doubt, AI is a formidable presence in modern business technology. We’re all aware of current AI trends, popularized by chat-based interactive experiences like ChatGPT and other large language models (LLMs). But in fact, AI has evolved steadily over many decades, and as this rapid evolution continues, the potential for AI is profound in many industries, including healthcare—but we need ways to filter out what’s truly important, and set aside the generalities and hype.

Based on IDC research, the healthcare industry recognizes that 80% of healthcare data is unstructured. Translating the language of healthcare presents unique challenges due to the inconsistent and esoteric nature of medical terminology. There is a wide variety of ways to express the same clinical concepts across organizations and specialties. Clinical jargon, acronyms, abbreviations, and misspellings are all commonly found in both structured and unstructured data, further complicating the process of conversion and categorization.

‍While the insights contained within unstructured data are essential to risk assessment and population health initiatives, it is typically not incorporated into analysis. As a result, providers, health plans, and life science companies resort to inefficient and unreliable manual processes to extract insights from unstructured data.

At the core of discovering the possibilities for AI in healthcare is understanding how specific AI technologies are more apt than others. Converting unstructured data into structured inputs is an expensive and complex endeavor that requires a specialized combination of machine learning algorithms and clinically trained natural language processing (CNLP).

Certain language models go beyond LLM capabilities and limitations to save clinical and administrative time and resources in ways that traditional LLMs cannot, setting the stage for deeper insights and advancements.

Over the coming months, Clinithink engineers and product leaders will share blog posts here that elaborate on these CNLP/LLM distinctions. Our goal is to help decision makers at healthcare organizations who are wary of mainstream AI perceptions to equip themselves with the best, latest healthcare AI knowledge. This knowledge can empower you to learn about unique opportunities for AI investment that will serve your business and wellness communities through the focused, efficient application of CNLP as an integral part of emerging healthcare AI solutions.

Topic examples include:

  • Provenance and traceability. Like all scientists, data scientists succeed best when the data they gather can be traced to its source. LLMs operate in ways that often obfuscate transparency, making potential research insights unreliable or opaque.
  • Hallucinations and other false positives. LLMs often fabricate details that are not present in the source data or prompt. CNLP helps to exclude these hallucinations, prioritizing fact and truth over guesswork or faulty interpolation.
  • Speed and granularity. Many LLMs are far too slow to meet many healthcare intelligence needs. When you try to speed them up, LLMs often lose granularity along the way, making them inadequate for obtaining high-value insights.
  • Transformers and attention. The architecture of transformers, which are fundamental to sequence-based tasks like CNLP, use “self-attention” and other mechanisms to process language in ways that yield better precision than LLMs.
  • Training and bias. AI models like LLMs can exhibit biases in their generative output, mostly stemming from the specific ways they are trained. Navigating and eluding these biases is key to generating high-quality healthcare insights.

By better understanding these concepts and acquainting yourself with the challenges and strengths around modern healthcare AI, you and your business will be in a position of greater strength when plotting your technology roadmap toward improved healthcare outcomes.

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