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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Large Language Models in Healthcare and Medical Applications: A Review.

Subhankar Maity1,2, Manob Jyoti Saikia1,2

  • 1Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USA.

Bioengineering (Basel, Switzerland)
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) show great promise for transforming healthcare with advanced NLP. Addressing challenges in privacy, ethics, and accuracy is crucial for their responsible integration into medical practice.

Keywords:
LLMshealthcarelarge language modelsmedicalpatientreview

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Natural Language Processing

Background:

  • Large language models (LLMs) offer advanced natural language processing (NLP) capabilities with significant potential to revolutionize healthcare.
  • Current LLM applications span clinical decision support, medical education, diagnostics, and patient care.

Purpose of the Study:

  • To systematically examine the potential and challenges of LLMs in the healthcare domain.
  • To provide a comprehensive understanding of LLM background, evolution, architecture, and multimodal capabilities in medicine.

Main Methods:

  • Exploration of domain-specific data acquisition, large-scale pre-training, and supervised fine-tuning.
  • Analysis of prompt engineering and in-context learning for healthcare use cases.
  • Categorization of prominent application areas and examination of methodological aspects.

Main Results:

  • LLMs demonstrate promising applications across various medical fields, enhancing capabilities in decision support, education, diagnostics, and patient care.
  • Identified key trends and prominent application areas for LLMs in medicine.
  • Highlighted critical challenges including privacy, ethical deployment, factual accuracy, model bias, interpretability, and regulatory compliance.

Conclusions:

  • Responsible integration of LLMs in healthcare requires addressing significant technical and social challenges.
  • Future research should focus on emerging directions and strategic recommendations for developing and deploying healthcare LLMs ethically and effectively.