Prompting as Clinical Reasoning
A practical online session on AI in healthcare, LLMs, and better clinical prompts
Why this webinar matters
AI is already part of everyday clinical work, supporting research, documentation, clinical questions, and routine workflow tasks.
But using AI effectively does not start with longer prompts.
It starts with clearer clinical thinking.
This session brings those two ideas together. ZAKA leads a practical look at how AI and large language models are used across healthcare and research, while DR. INFO shows it live through source-linked medical information: guideline retrieval, evidence summaries, and citations you can trace back to the source.
The aim is simple: help healthcare professionals understand how to ask better questions, recognize where LLMs are useful, and see why source visibility matters in medical information workflows.
What you will learn
By joining this session, you will learn:
- How AI and LLMs are being used in healthcare and medical research
- Why prompt quality matters when working with medical information
- How to structure clearer clinical questions for AI tools
- Where general-purpose LLMs can support healthcare workflows
- Where caution is needed when using AI-generated answers
- Why source-linked medical information, citations, and traceability matter
- How DR. INFO supports guideline retrieval, evidence summaries, citations, and visual abstracts
About the speakers
Dr. Nader Absi
Dr. Nader Absi is a physician (MD, American University of Beirut; Harvard postdoc) focused on clinical architecture, where medical logic meets software and deployment. He builds clinical engines that turn static medical guidelines into dynamic, operational products: structured logic layers that run triage, risk stratification, and care pathways safely and consistently. He co-founded Brainz Health in the GCC, designing the clinical logic for a precision mental health platform.
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Dr. Mohamad Mehdi Diab
Dr. Mohamad Mehdi Diab is a medical doctor and applied AI specialist working at the intersection of healthcare, generative AI, and education. He has contributed to Stability AI’s SDXL image model development, served as a research fellow at the American University of Beirut applying machine learning to digital pathology diagnostics, and authored widely used resources on Stable Diffusion and prompt engineering. As an AI instructor at ZAKA, he focuses on making advanced AI concepts practical, accessible, and impactful for real-world applications.
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