Understanding AI in Healthcare: A Practical Journey for Public Leaders
Artificial Intelligence (AI) is no longer on the horizon — it is already here. Hospitals, clinics, and public health agencies are adopting AI tools to detect diseases faster, predict patient needs, and manage resources more efficiently.
This interactive experience is designed for public leaders and policymakers. You'll explore how AI is shaping healthcare today, where its promises lie, where caution is needed, and how to approach decisions with confidence and clarity.
How AI is Changing Healthcare: Opportunities to Build On
Before considering risks or rules, it's worth recognizing where AI is already helping.
Two major strengths stand out across healthcare systems today:
AI's ability to find meaningful patterns in huge amounts of data (Intelligence at Scale)
AI's ability to improve day-to-day operations and resource use (Operational Precision)
Let's look at real examples of how these strengths are supporting patients and communities.
AI systems can analyze thousands of patient records quickly — helping doctors detect diseases earlier and health agencies track emerging risks.
Example: Mayo Clinic uses Google Health's AI algorithms to predict resource needs, helping them prepare for patient surges and manage ICU beds and ventilators efficiently.
AI helps hospitals and agencies manage logistics more effectively — adjusting staffing levels, preparing for patient spikes, and managing critical supplies.
Example: Mount Sinai Health System (New York) used AI to predict ICU occupancy and patient flow during the COVID-19 pandemic, allowing for better emergency planning.
The opportunities are exciting — but even the best tools can miss important signals. Leading responsibly means knowing when to trust AI — and when human judgment must step in.
When AI is Helpful — and When It Misses Critical Details
Even powerful AI systems make mistakes. Review these scenarios and see how AI performs in real-world decisions.
Review the following scenarios and select what you think is the correct diagnosis. There are no right or wrong answers — this exercise is about building critical thinking skills for AI oversight.
Seeing the Gaps: Spotting Risk Before It Becomes Harm
Take a moment to read these scenarios. You won't be graded. Think about where you see risks, then reveal the answers to learn how hidden issues can be caught before they cause damage.
What hidden risk or bias might be present?
Take a moment to consider before revealing the answer.
Equipping Yourself: Smart Questions for Smarter AI Decisions
This AI Evaluation Toolkit offers practical questions to ask during any procurement, pilot, or policy discussion. Each question expands to provide the reasoning behind it. You can download the full Toolkit after reviewing it.