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Generative artificial intelligence: Foundational models. Natural language processing and large language models
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AI
DOI：10.1016/j.rce.2025.502413.png)
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Abstract
En 中文
This work aims to provide internists with a practical, focused overview of how generative artificial intelligence (AI) based on large language models can be effectively integrated into daily clinical practice. It describes the primary adaptation mechanisms like fine-tuning and retrieval-augmented generation (RAG) for tasks such as report generation, synthesis of clinical findings, and support in differential diagnoses, highlighting real-world examples in Internal Medicine. Technical and organizational requirements for adoption are analyzed, including computing infrastructure, integration with electronic health records, and security/privacy protocols under GDPR and the EU AI Act. Opportunities for enhancing clinical decision-making, optimizing workflows, and reducing administrative burden are emphasized, alongside current limitations like bias, hallucinations, and the need for human oversight. Finally, recommendations are offered for prospective validation in real-world settings and for ensuring explainable transparency, with the goal of empowering internists to incorporate these innovative tools responsibly and efficiently.
Keywords:
Artificial Intelligence
Clinical decision support systems
Electronic health records
Internists
Natural language processing
Journal
R
IF:
1.7
Papers:
95
Citations:
924
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