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Related Experiment Video

Updated: Jan 8, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Generative AI: foundational models. Natural Language Processing (NLP) and LARGE Language Models (LLM).

Juan Mora-Delgado¹, Luis Ramos-Ruperto², María José Pardilla³

¹Grupo de Trabajo Medicina Digital de la SEMI, Spain; Unidad de Gestión Clínica de Medicina Interna y Cuidados Paliativos, Hospital Universitario Jerez de la Frontera, Jerez, Spain.

Revista Clinica Espanola

|December 22, 2025

Summary

This summary is machine-generated.

Generative AI, including large language models, offers practical integration for internists in clinical practice. Key adaptation mechanisms and requirements are discussed for enhancing decision-making and workflows.

Keywords:

Artificial intelligence Clinical decision support systems Electronic health records Inteligencia artificial Internistas Internists Natural language processing Procesamiento de lenguaje natural Registros electrónicos de salud Sistemas de apoyo a la decisión clínica

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Medical Informatics
Artificial Intelligence in Medicine
Clinical Decision Support

Background:

Generative AI and large language models (LLMs) are emerging technologies with potential applications in healthcare.
Internists require practical guidance on integrating these tools into daily clinical workflows.

Purpose of the Study:

To provide internists with a practical overview of integrating generative AI and LLMs into clinical practice.
To analyze adaptation mechanisms, technical requirements, and opportunities for AI in Internal Medicine.

Main Methods:

Description of primary adaptation mechanisms: fine-tuning and retrieval-augmented generation (RAG).
Analysis of technical and organizational requirements: infrastructure, EHR integration, security/privacy (GDPR, EU AI Act).
Highlighting real-world examples in Internal Medicine for report generation, clinical synthesis, and differential diagnoses.

Main Results:

Generative AI can enhance clinical decision-making, optimize workflows, and reduce administrative burden.
Identified limitations include AI bias, hallucinations, and the necessity for human oversight.
Technical requirements involve infrastructure, EHR integration, and robust security/privacy protocols.

Conclusions:

Internists can responsibly and efficiently incorporate generative AI tools with proper validation and transparency.
Prospective validation in real-world settings is recommended for ensuring explainable AI.
The study empowers internists to leverage AI for improved patient care and practice efficiency.