Large language models: the AI systems clinicians are now encountering - Irish Medical Times

2026-04-03 · Source: artifical intelligence via Google News · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Medical Devices & Health Technology · Depth: Novice, medium

Summary

Large language models (LLMs) are rapidly integrating into clinical practice, enabling tasks like drafting letters, summarizing research, and explaining complex topics. These AI systems, trained on vast text quantities, predict word sequences to generate coherent responses, but do not "understand" medicine in a human sense; they identify patterns. LLMs exist across a spectrum, from computationally expensive flagship models (e.g., ChatGPT, Claude, Gemini, Grok) capable of complex analysis, to lightweight models optimized for speed, and mid-tier models balancing both. A critical distinction for clinicians is between closed (proprietary) models, whose internal architecture is private, and open-weight models (e.g., Llama, DeepSeek, Qwen, Mistral, BLOOM), which allow local deployment for enhanced data governance and privacy, aligning better with regulations like the EU AI Act. Clinicians are encountering LLMs in medical literature summarization, letter drafting, patient education, and data analysis, with AI-native browsers like Microsoft Copilot in Edge and Google Gemini in Chrome emerging as new interfaces for medical information.

Key takeaway

For healthcare executives evaluating AI adoption, understanding the fundamental differences between open and closed large language models is crucial. Your choice impacts data governance, patient privacy, and regulatory compliance, particularly concerning frameworks like GDPR and the EU AI Act. Prioritize open-weight models for applications requiring local data hosting and transparency, while recognizing that proprietary systems may offer higher out-of-the-box performance. Ensure clinical staff are trained to critically verify all AI-generated information.

Key insights

LLMs offer diverse capabilities for clinicians, requiring understanding of their operational spectrum and open vs. closed distinctions.

Principles

• LLMs identify data patterns, not human understanding.
• Clinicians must verify all AI-generated output.
• Model choice impacts data governance and privacy.

In practice

• Use open models for local, secure data processing.
• Leverage AI browsers for conversational information access.
• Differentiate models by capability, speed, and specialization.

Topics

• Large Language Models
• Clinical AI Applications
• Closed AI Models
• Open-weight AI Models
• Data Governance

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Editorial summary, takeaway, and curation by AIssential. Original article published by artifical intelligence via Google News.