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Healthcare AI represents one of the highest-stakes applications of artificial intelligence—where algorithmic decisions can literally determine who lives, who dies, and who receives care. When you study AI ethics in healthcare, you're examining how core ethical principles like autonomy, beneficence, non-maleficence, and justice collide with the realities of machine learning systems that can perpetuate bias, obscure decision-making, and shift responsibility away from human practitioners.
You're being tested on your ability to identify ethical tensions, not just list concerns. Exam questions will ask you to analyze trade-offs: When does efficiency justify reduced human oversight? How do we balance innovation with patient protection? Who bears responsibility when an algorithm fails? Don't just memorize these ten considerations—understand which ethical principles each one challenges and how they interconnect.
These considerations center on the patient's right to control their own health information and make informed decisions about their care. Autonomy—the principle that individuals should direct their own lives—becomes complicated when AI systems require vast amounts of personal data to function effectively.
Compare: Patient privacy vs. informed consent—both protect autonomy, but privacy focuses on data control while consent addresses decision-making authority. FRQs often ask you to distinguish between these overlapping but distinct protections.
Algorithmic bias represents one of the most pressing ethical challenges in healthcare AI. These considerations address how AI systems can perpetuate or amplify existing health disparities, violating the principle of distributive justice—the fair allocation of benefits and burdens across populations.
Compare: Algorithmic bias vs. data representativeness—bias can exist even in representative datasets (through biased labels), while unrepresentative data creates bias even with neutral algorithms. Know both failure modes for exam questions about AI fairness.
These considerations address the "black box" problem in AI: when systems make consequential decisions through processes that humans cannot easily understand or explain. Transparency concerns what we can know about AI reasoning; accountability concerns who bears responsibility for outcomes.
Compare: Transparency vs. accountability—a system can be transparent (we understand how it works) without clear accountability (who's responsible when it fails), and vice versa. Exam questions often test whether you can distinguish these related but separate concerns.
These considerations examine how AI integration changes the practice of medicine itself—affecting relationships, roles, and the fundamental nature of clinical care. The core tension is between efficiency gains from automation and irreplaceable human elements in healthcare.
Compare: Doctor-patient relationship vs. human oversight—the first concerns interpersonal dynamics between patient and provider, while the second addresses institutional protocols for human-machine collaboration. Both involve human elements but at different levels of analysis.
Clinical trials using AI introduce novel ethical challenges that extend traditional research ethics frameworks. These considerations address how beneficence (doing good through research) must be balanced against non-maleficence (protecting participants from harm) in AI-driven studies.
Compare: Clinical trial ethics vs. algorithmic bias—both involve fairness in participant/patient selection, but trials focus on research populations while bias concerns address clinical care populations. Strong FRQ responses connect these parallel concerns.
| Ethical Principle | Key Considerations | Core Tension |
|---|---|---|
| Autonomy | Patient privacy, Informed consent | Data needs vs. individual control |
| Justice/Fairness | Algorithmic bias, Data representativeness, Resource allocation | Efficiency vs. equity |
| Transparency | Explainability, Methodological openness | Interpretability vs. complexity |
| Accountability | Liability frameworks, Responsibility assignment | Innovation vs. clear responsibility |
| Beneficence | Clinical trials, Resource optimization | Research benefits vs. participant protection |
| Non-maleficence | Human oversight, Bias mitigation | Automation benefits vs. potential harms |
| Trust | Doctor-patient relationship, Patient perceptions | Efficiency vs. human connection |
Which two considerations both address patient autonomy but focus on different aspects of it? Explain what distinguishes them.
If an AI system produces accurate predictions overall but performs significantly worse for minority populations, which considerations are most relevant—and what ethical principle do they invoke?
Compare and contrast transparency and accountability in healthcare AI. Can a system satisfy one requirement while failing the other? Provide an example.
An FRQ asks you to evaluate the ethics of using AI for emergency room triage during a pandemic. Which three considerations would you prioritize, and why?
How does the consideration of "human oversight" connect to concerns about the "doctor-patient relationship"? What shared ethical tension underlies both?