Reason through the hardest ethical dilemmas AI is bringing to medicine
This is a rigorous clinical ethics seminar — not a compliance checklist. You'll apply Kantian deontology, principlism, and equity frameworks to real cases involving algorithmic triage, predictive diagnostics, and patient autonomy, until defensible ethical judgment becomes second nature.
"I don't want you to leave with the right answers — I want you to leave with a method rigorous enough to find them yourself, in any room, under pressure."— Dr. Jill Young
What you'll learn
What you'll be able to do
- Apply classical frameworks — Kantian deontology, utilitarianism, virtue ethics, and principlism — to concrete AI decision-making situations in clinical settings
- Analyze real-world hypothetical cases involving algorithmic bias, triage automation, and predictive diagnostics using a structured ethical reasoning method
- Evaluate the tension between patient autonomy and AI-driven clinical recommendations, and articulate defensible positions in multidisciplinary team discussions
- Identify and mitigate sources of algorithmic bias and data inequity that produce disparate health outcomes across patient populations
- Design or critique institutional AI governance policies by applying modern frameworks such as the EU AI Act, FDA guidance, and emerging bioethics standards
- Communicate ethical findings and recommendations clearly to both technical and non-technical stakeholders — from engineers to patients to hospital boards
How it works
A school that adapts to you
This isn't a set of static videos. Every lesson is generated live and tuned to where you actually are.
We learn your level
A quick placement check tailors your starting point so you're never bored or lost.
Lessons adapt as you go
Each lesson is written for your pace and your goal, adjusting as your skills grow.
Your AI coach keeps you moving
Checkpoints, feedback, and gentle nudges turn progress into a real result.
The curriculum
What's inside your school
6 modules · 28 lessons
Foundations: Ethical Frameworks for AI in Medicine
Establishes the philosophical bedrock — classical and modern frameworks — that students will apply throughout every subsequent case-based module.
- 1.1Why AI Ethics in Medicine Is DifferentIncluded
- 1.2Classical Frameworks: Kant, Mill, and Virtue EthicsIncluded
- 1.3Principlism in the Age of AlgorithmsIncluded
- 1.4Modern Frameworks: From Responsible AI to Bioethics StandardsIncluded
- 1.5A Structured Ethical Reasoning Method for Clinical AIIncluded
Patient Autonomy vs. AI-Driven Recommendations
Examines the tension between algorithmic authority and the patient's right to understand, question, and refuse AI-informed care.
- 2.1Informed Consent When the Decision-Maker Is an AlgorithmIncluded
- 2.2When Patients Reject the Algorithm's AdviceIncluded
- 2.3Shared Decision-Making in AI-Augmented ConsultationsIncluded
- 2.4Vulnerable Populations and Diminished AutonomyIncluded
Algorithmic Bias and Health Equity
Investigates how biased training data and flawed model design produce disparate outcomes, and what clinicians and administrators can do about it.
- 3.1How Bias Enters Clinical AI: Data, Design, and DeploymentIncluded
- 3.2Case Deep-Dive: Pulse Oximetry, Sepsis Scores, and Racial InequityIncluded
- 3.3Socioeconomic and Geographic Bias in Predictive DiagnosticsIncluded
- 3.4Auditing AI for Bias: Practical Tools and Institutional ResponsibilityIncluded
- 3.5Designing Equity-Centered Corrective PoliciesIncluded
Triage, Prognosis, and Life-or-Death Automation
Confronts the hardest scenarios in clinical AI — automated rationing, end-of-life prediction, and time-critical triage — through competing ethical frameworks.
- 4.1Automating Triage: Who Decides When the Algorithm Decides?Included
- 4.2Predictive Mortality Scores and the Ethics of Prognostic AIIncluded
- 4.3Utilitarian vs. Deontological Responses to Crisis Triage AIIncluded
- 4.4Human Override: When Clinicians Must Countermand the AlgorithmIncluded
Governance, Accountability, and Institutional Policy
Equips students to design, critique, and champion institutional AI governance structures that are ethically grounded and regulatorily informed.
- 5.1Mapping Accountability: Who Is Responsible When AI Harms a Patient?Included
- 5.2Reading Regulatory Frameworks as Ethical ToolsIncluded
- 5.3Building an Ethical AI Governance CommitteeIncluded
- 5.4Procurement Ethics: Evaluating AI Vendors Through an Ethical LensIncluded
- 5.5Whistleblowing and Dissent When Institutional AI Goes WrongIncluded
Communication, Advocacy, and Ethical Leadership
Builds the practical communication skills to translate ethical reasoning into action across clinical teams, boardrooms, patients, and engineering partners.
- 6.1Explaining AI Ethics to Patients and FamiliesIncluded
- 6.2Presenting Ethical Findings to Hospital Boards and ExecutivesIncluded
- 6.3Collaborating with Engineers and Data Scientists on Ethical DesignIncluded
- 6.4Leading an Ethics Review in Multidisciplinary Team MeetingsIncluded
- 6.5Capstone: Full Ethical Analysis of a Complex AI CaseIncluded
Who it's for
Is this you?
Attending Physicians
You encounter AI-generated recommendations daily and need a defensible ethical framework for when to follow them, when to override them, and how to explain either choice to your team and your patients.
Clinical Nurses
You're often the last human checkpoint before an algorithmic recommendation becomes action — this school gives you the vocabulary and the frameworks to raise ethical concerns with authority.
Health-Tech Product Managers
You ship tools that touch patient lives, and you need a rigorous ethical design lens — from bias auditing to consent architecture — that goes well beyond standard responsible-AI platitudes.
Hospital Administrators
Procurement decisions, governance committees, vendor evaluations, and regulatory compliance all carry ethical weight you can now assess systematically rather than by instinct.
Clinical Researchers
Your work with predictive models and clinical datasets sits upstream of patient harm — this school equips you to identify equity problems early and design studies and policies that address them.
Bioethics Students
You're building a scholarly foundation in moral philosophy and want to apply it to the most consequential technological development in contemporary medicine — this curriculum is that bridge.
Questions
Frequently asked
Your teacher
A note from your teacher
Dr. Jill Young
If you've found yourself in a meeting where an AI tool is being rolled out across your institution and the conversation never once turned to what the algorithm might be getting wrong — or who it might be harming — then you already know why this school exists.
Most professionals at the intersection of AI and medicine are operating without a structured ethical vocabulary. That's not a personal failure; it's a systemic one. Medical training emphasizes clinical decision-making. Engineering training emphasizes model performance. Neither reliably produces the kind of cross-domain moral reasoning that AI-driven medicine now demands. I built this curriculum because the gap between "we know this is complicated" and "we know how to reason through it rigorously" is where patients get hurt and institutions incur liability — and where good professionals, without the right framework, simply go quiet when they should push back.
What this school teaches is a method. We begin with the canonical frameworks — Kant, Mill, virtue ethics, the four principles of bioethics — not because memorizing them is the goal, but because they are precise instruments for disaggregating what's actually in conflict when an algorithm recommends an action that troubles you. We then take those instruments directly into the cases that matter: informed consent when the decision-maker is an algorithm, racial inequity embedded in sepsis scores and pulse oximetry, the utilitarian logic of crisis triage automation and its deontological limits, the accountability gap when AI harms a patient and everyone points elsewhere. The curriculum doesn't resolve these cases for you — it teaches you to resolve them yourself, and to defend your reasoning to a surgeon, a data scientist, a patient's family, and a hospital board.
I want to be honest about what this school is not. It is not a compliance training. It will not give you a checklist. If you are looking for a set of approved positions to adopt so that the ethics box gets ticked, this is the wrong place. If you are looking to develop the kind of ethical judgment that makes you genuinely valuable — to your team, your institution, and the patients whose care your work touches — then you are exactly who this is for.
The seminar energy here is collegial and Socratic. You will be asked to reason out loud, to take positions and defend them, to sit with genuine uncertainty rather than paper over it with policy language. That discomfort is not a bug; it is precisely the condition under which real ethical judgment is built.
I'd like to invite you in.
— Dr. Jill Young
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- 6 modules, 28 lessons
- AI-adaptive lessons tuned to your level
- Quizzes & checkpoints to lock in progress
- Your own AI learning coach
- Learn on any device, at your pace
- Full access for as long as you're subscribed