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Hi!
Today is LLM Friday.
A day when I will only share Research, Tools, and News on Large Language Models.
Are you ready? Let’s dive into it.
✨LLMs
This Nature Medicine review explains how Generative AI is reshaping research, clinical care, and healthcare operations.
🔬 Methods
Design: Narrative review (2020–2025) from peer-reviewed research and real-world examples (GPT-5, DeepSeek-R1, Gemini 2.5 Pro, Claude 4).
Focus areas:
Clinical reasoning and diagnostic support
Medical imaging (radiology, pathology, ophthalmology)
Administrative and documentation automation
Medical education and research innovation
📊 Results
Clinical support:
LLMs now perform at or above physician level on structured exams, and often write more empathetic responses to the patient’s questions.Documentation and coding:
AI scribes cut documentation time and improve note quality.Education:
Students who used AI feedback in their training improved within four sessions. However, there is still a risk of hallucinations from the LLM when discussing complex topics.Research:
Accelerates hypothesis generation, molecular modeling, and literature synthesis, but also drives a surge of low-quality “AI-generated” papers.Risks:
Potential for privacy leaks, language bias, and unequal global access.
🔑 Key Takeaways
AI is augmenting clinicians, not replacing them. It supports reasoning, documentation, and decision-making under human supervision.
Improving AI literacy will enable clinicians and patients to make the best use of GenAI tool.
Deployment in real-world settings is relatively understudied. Clinical validation is essential.
Smaller, domain-specific models often outperform large general systems.
Transparency on the system make decisions and bias control are key.
Agentic AI should be integrated into clinical workflows.
🔗Teo ZL, Thirunavukarasu AJ, Elangovan K, et al. Generative Artificial Intelligence in Medicine. Nature Medicine. 2025 Oct; 31(10): 3270–3282. doi:10.1038/s41591-025-03983-2
🦾TechTools
A few issues back, we talked about prompting. Today, we will start digging deeper into different prompting styles, one per week.
Zero-Shot Prompting
Zero-shot prompting is when you ask an AI model to perform a task without giving it any examples, which means the model relies entirely on what it already knows from training.
When is it useful?
It’s ideal for general tasks where the model already has enough background knowledge — like defining a concept, summarizing a topic, or explaining clinical applications.
Example: “Explain the role of AI in stroke rehabilitation.”
→ The model will generate an answer directly from what already knows.
Zero-shot prompting works well for quick, broad questions but can lead to less specific or less accurate answers in complex clinical contexts.
We’ll continue with this series next week.
That’s all for today. I hope you have a relaxing and fun weekend.
You’re already ahead of the curve in medical LLMs — don’t keep it to yourself. Forward AIMedily to a colleague who’d appreciate the insights.
Thank you!
Until Wednesday.
Itzel Fer, MD PM&R
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