Integration of Large Language Models in Clinical Decision Support: A Framework for Human-AI Collaboration in Healthcare

Madhu Babu Kola

doi:10.32628/CSEIT2410612432

Authors

Madhu Babu Kola Tata Consultancy Services LTD, USA Author

DOI:

https://doi.org/10.32628/CSEIT2410612432

Keywords:

Clinical Decision Support Systems, Large Language Models in Healthcare, Human-AI Collaboration, Healthcare AI Implementation, Medical Diagnostic Intelligence

Abstract

The integration of Large Language Models (LLMs) in clinical decision support represents a transformative advancement in healthcare delivery, fundamentally changing how medical professionals interact with patient data and make clinical decisions. This comprehensive article examines the implementation of AI systems in healthcare settings, focusing on the synergistic relationship between human expertise and artificial intelligence. The article explores the evolution from traditional diagnostic approaches to AI-augmented decision-making, addressing key aspects including technical architecture, clinical validation, ethical considerations, and practical implementation challenges. Through analysis of current applications in medical imaging, clinical decision support, and treatment optimization, the article demonstrates how AI systems enhance diagnostic accuracy while maintaining human oversight. The article encompasses critical aspects of data security, patient privacy, and regulatory compliance, while also addressing the prevention of algorithmic bias and ensuring equitable healthcare delivery. By examining successful implementation cases and identifying potential barriers, this article provides a framework for healthcare organizations to effectively integrate AI technologies while maintaining focus on patient care quality and clinical outcomes.

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