Gen Ai Basics For Health

Inspired by the University of Helsinki's "Elements of AI" (2M+ enrollments, 26 languages), this skill is the "Elements of Health AI" equivalent — the first exposure to AI for health professionals anywhere in the world.

This skill is for anyone in health — doctors, nurses, pharmacists, administrators, community health workers, students. No technical background required. You need only a smartphone or computer with internet access.

Steps

Step 1: What Is Generative AI? (30 minutes)

1. Read the provided brief (or use the Elements of AI Chapter 1 as reference)
2. Key concepts to understand:
   • AI = pattern recognition from data (not thinking or understanding)
   • Large Language Models (LLMs) = predict the next word based on training data
   • "Generative" = creates new text, not retrieves existing text
   • Training data cutoff = the model doesn't know what happened after training
   • Temperature/creativity = models can be more or less creative (and more or less accurate)
3. Write a 5-sentence summary of how LLMs work in your own words. Avoid jargon.

Step 2: Your First Health AI Interaction (30 minutes)

1. Open a free Gen AI tool: ChatGPT (chat.openai.com), Claude (claude.ai), or Gemini (gemini.google.com)

2. Complete 3 tasks using the AI tool:

   Task A — Information synthesis:

   "Summarize the 5 most common causes of maternal mortality in Sub-Saharan Africa, with approximate percentages, and cite sources."

   Task B — Clinical reasoning support:

   "A 45-year-old male presents with sudden onset headache, neck stiffness, and photophobia. What is the differential diagnosis and what initial investigations should be ordered?"

   Task C — Document drafting:

   "Draft a 200-word patient information leaflet about managing Type 2 diabetes through diet, appropriate for a patient with primary school education in a rural African setting."

3. For each response, note:

   • Was it helpful? (1-5)
   • Did anything seem wrong or suspicious?
   • Would you trust this without checking?

Step 3: The Hallucination Problem (45 minutes)

1. What is a hallucination? When AI generates plausible-sounding but factually wrong information. In health, hallucinations can be dangerous.

2. Find 3 hallucinations. Ask the AI tool:

   "What clinical trials have been conducted on [specific drug] for [specific condition] in [specific African country]?"

   Choose a narrow, specific query. The AI will likely generate trial names, dates, and results that do not exist. Try to verify each claim.

3. Types of health AI hallucinations:

   • Citation hallucination — invents journal articles with realistic DOIs
   • Dosage hallucination — generates plausible but incorrect drug dosages
   • Guideline hallucination — references guidelines that don't exist or misquotes them
   • Epidemiology hallucination — fabricates statistics with false precision
   • Clinical trial hallucination — invents trials with specific sample sizes and outcomes

4. Document your 3 hallucinations with:

   • The prompt you used
   • The AI's response
   • What was wrong (with evidence)
   • What the correct information is (with source)

Step 4: The VERIFY Framework (30 minutes)

Apply this framework to EVERY AI output in a health context:

Exercise: Apply the VERIFY framework to the 3 AI responses from Step 2. Fill in the table for each.

Step 5: Privacy and Patient Data (30 minutes)

1. The cardinal rule: NEVER enter patient-identifiable data into public AI tools (ChatGPT, Claude, Gemini). This includes:

   • Names, dates of birth, ID numbers
   • Specific clinical histories that could identify someone
   • Hospital record numbers
   • Photos of patients (even with consent)

2. Why? Public AI tools may:

   • Store your inputs for training (opt-out varies by tool)
   • Be accessed by the AI company's employees
   • Leak data through future model outputs
   • Violate GDPR, local data protection acts, and HIPAA

3. Safe alternatives:

   • De-identify before querying: "A 45-year-old male..." not "Mr. Ngum from Kumba..."
   • Use approved institutional AI tools when available
   • Use local/on-premise models for sensitive data
   • Ask about general patterns, not specific patients

4. Write a 1-page privacy policy for AI use in your institution or practice. Include:

   • What data can be entered into AI tools
   • What data must never be entered
   • How to de-identify clinical scenarios
   • Who to contact if a data breach occurs

Step 6: AI as Tool vs Decision-Maker (15 minutes)

1. The spectrum:

   AI as search engine ──── AI as assistant ──── AI as advisor ──── AI as decision-maker
   (acceptable)              (acceptable)         (with caution)     (NOT acceptable in health)
   

2. Write a reflection (300 words): "How could AI help in my work, and where should it never replace human judgment?" Include at least:

   • One task where AI would save you time
   • One task where AI could be dangerous
   • Your personal rule for when to trust AI output

Artifacts

You must produce all 5 artifacts to complete this skill:

1. LLM Summary — 5-sentence explanation of how large language models work, in your own words, jargon-free
2. 3 AI Interaction Logs — Screenshots or transcripts of your 3 health AI tasks (information synthesis, clinical reasoning, document drafting) with helpfulness ratings and suspicion notes
3. Hallucination Hunt Report — 3 documented hallucinations with: prompt used, AI response, what was wrong, correct answer with source
4. VERIFY Analysis — Completed VERIFY framework table applied to your 3 AI responses from Step 2
5. AI Privacy Policy + Reflection — 1-page institutional AI privacy policy draft PLUS 300-word reflection on AI as tool vs decision-maker

Assessment Criteria

Passing score: 10/15 (at least "Adequate" on all criteria)

Common Mistakes

1. Assuming AI "knows" things — LLMs predict probable text, they don't have knowledge. A confident-sounding answer is not necessarily correct.
2. Trusting citations without checking — AI frequently invents realistic-looking journal references. Always verify DOIs.
3. Entering patient data — Even "just to ask a question." Once data is submitted, you cannot retrieve it.
4. Dismissing AI entirely — The solution is not to avoid AI but to use it critically. The tool is powerful when used correctly.
5. Using AI for diagnosis — In its current form, Gen AI is not approved as a diagnostic tool. Use it for education, synthesis, and drafting — not for making clinical decisions.
6. Forgetting context bias — Most AI training data comes from high-income countries. Drug availability, clinical guidelines, and disease patterns may not match your setting.

Related Skills

• Prerequisite: Digital Literacy — Basic digital skills needed to access AI tools
• Next: Health Data Awareness — Deeper dive into health data rights and responsibilities
• Leads to: TRIPOD+AI Checklist — Evaluate health AI studies using the gold standard
• Leads to: Model Calibration — Assess AI model reliability

References

1. WHO (2021). Ethics and Governance of Artificial Intelligence for Health. World Health Organization.
2. University of Helsinki & MinnaLearn (2018). Elements of AI. https://www.elementsofai.com/
3. Ji, Z. et al. (2023). Survey of Hallucination in Natural Language Generation. ACM Computing Surveys, 55(12), 1-38.
4. Thirunavukarasu, A.J. et al. (2023). Large language models in medicine. Nature Medicine, 29, 1930-1940.
5. Meskó, B. & Topol, E.J. (2023). The imperative for regulatory oversight of large language models in healthcare. npj Digital Medicine, 6, 120.
6. Omiye, J.A. et al. (2024). Large language models propagate race-based medicine. npj Digital Medicine, 7, 44.