This article explores text summarization techniques for healthcare using Amazon SageMaker, addressing the challenge of processing vast clinical data efficiently.

• Three implementation approaches: custom models, SageMaker JumpStart foundation models, and fine-tuned Hugging Face models
• SageMaker JumpStart offers proprietary models (AI21 Jurassic, Cohere) and open-source models (FLAN T5, Bloom) with HIPAA eligibility
• Fine-tuning Hugging Face models provides faster training, better domain performance, and seamless SageMaker integration
• Step-by-step guide includes dataset preparation, model loading, tokenization, training configuration, and inference deployment
• Fine-tuned models demonstrate improved healthcare terminology and structured output for clinical summarization
• Custom models offer flexibility but require more time and resources than pre-trained approaches

The article provides practical guidance for healthcare organizations to choose appropriate summarization solutions based on customization needs, resources, and performance requirements.