Artificial Intelligence: Looking at Large Language Models
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By
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Conexiant News Staff
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January 2, 2026
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1 min
Clinical Report: Artificial Intelligence: Looking at Large Language Models
Overview
Expand on specific applications of LLMs in reducing cognitive load and enhancing patient care.
Background
The integration of LLMs in healthcare is increasingly relevant as providers seek to manage extensive clinical information found in Electronic Health Records (EHRs). These models can streamline processes, allowing healthcare professionals to focus more on patient interaction. Understanding the capabilities and limitations of LLMs is crucial for informed implementation in clinical settings.
Data Highlights
No numerical data available in the source material.
Key Findings
- LLMs can assist in drafting patient education materials, improving communication.
- They support staff onboarding by generating educational summaries and dialogues.
- Human evaluation remains essential for ensuring the precision of LLM outputs.
- Global guidelines emphasize the need for human oversight and safety in LLM applications.
- Recent studies highlight the potential of LLMs to enhance clinical reasoning and documentation.
Clinical Implications
Healthcare providers should consider integrating LLMs to improve efficiency in patient education and administrative tasks. However, it is vital to maintain human oversight to ensure the accuracy and reliability of the information generated by these models.
Conclusion
The use of LLMs in clinical practice presents opportunities for enhanced efficiency but requires careful implementation and oversight to maximize benefits while minimizing risks.
References
- World Health Organization, WHO, 2025 -- Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models
- FDA, FDA, 2025 -- Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions
- npj Digital Medicine, npj Digital Medicine, 2025 -- Evaluating clinical AI summaries with large language models as judges
- npj Digital Medicine, npj Digital Medicine, 2025 -- The evaluation illusion of large language models in medicine
- npj Digital Medicine, npj Digital Medicine, 2026 -- Collaboration Between Humans and Large Language Models in Clinical Practice: A Systematic Review and Meta-Analysis
- npj Digital Medicine — Utilizing Large Language Models to Enhance Diagnosis of Language Disorders Linked to Autism and Recognize Unique Characteristics
- AMA issues AI guidance for health systems
- Coalition for Health AI (CHAI) Releases New Best Practice Guides and Testing & Evaluation Frameworks
- ESMO guidance on the use of Large Language Models in Clinical Practice
- Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models
- Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions | FDA
- Large language model diagnostic assistance for physicians in a lower-middle-income country: a randomized controlled trial | Nature Health
- GPT-4 assistance for improvement of physician performance on patient care tasks: a randomized controlled trial | Nature Medicine
- An LLM chatbot to facilitate primary-to-specialist care transitions: a randomized controlled trial
- Human–large language model collaboration in clinical medicine: a systematic review and meta-analysis | npj Digital Medicine
- Large language model integrations in cancer decision-making: a systematic review and meta-analysis | npj Digital Medicine
- Use of Ambient AI Scribes to Reduce Administrative Burden and Professional Burnout | Health Policy | JAMA Network Open | JAMA Network
This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.
