Transformers and Large Language Models in Healthcare

Transformers and Large Language Models in Healthcare

Introduction

Large language models (LLMs) like GPT, BERT, and Med-PaLM have captured global attention for their ability to generate coherent, human-like text. At the core of these systems is the transformer architecture, a deep learning design that revolutionized natural language processing (NLP). This post introduces transformers in clinician-friendly terms, explores applications in healthcare, and highlights the limitations and risks of LLMs.

What Makes Transformers Different?

Before transformers, NLP relied on recurrent networks that processed text sequentially, making them slow and limited in handling long contexts. Transformers use an attention mechanism, allowing the model to weigh relationships between all words in a sequence simultaneously. This parallelism enables LLMs to handle large datasets and capture nuanced language patterns.

Token A Token B Attention Layer Contextual Output

Simplified illustration: tokens processed in parallel through the attention mechanism, producing context-aware outputs.

Applications in Healthcare

  • Clinical documentation: Summarizing lengthy EHR notes into concise progress reports.
  • Radiology reporting: Drafting structured reports from dictated findings.
  • Patient communication: Generating discharge instructions in plain language.
  • Literature review: Rapid synthesis of medical research for clinicians and guideline developers.
  • Trial matching: Identifying eligible patients for clinical trials by parsing eligibility criteria.

Limitations and Risks

  • Hallucinations: LLMs may generate plausible but false information.
  • Context limits: Performance depends on how much text fits in the model’s context window.
  • Data privacy: Sensitive clinical text must be handled with strict safeguards.
  • Regulation: Few LLM-based clinical tools have yet been cleared by the FDA or CE-marked.

Conclusion & Next Step

Transformers and LLMs represent a powerful new wave of clinical AI, with promising applications in documentation, reporting, and communication. But they also introduce risks such as hallucinations and privacy concerns. Next we will learn about evaluating and aalidating of AI Tools in Medicine.