Ethics in AI for Healthcare: A Guided Q&A Framework #
This document presents a structured chain-of-thought (CoT) using guiding questions and answers to understand ethical considerations in the development and deployment of AI in healthcare, based on Module 7 from the Stanford “Introduction to Clinical Data” course.
1. Why is ethics important in the context of AI in healthcare? #
Answer:
AI tools impact patients directly or indirectly, whether through their development (research) or their deployment (clinical practice). Each of these domains carries different ethical responsibilities that must be considered and governed carefully.
➡️ Leads to: Understanding the foundations of research ethics.
2. How has the field of research ethics developed over time? #
Answer:
Through responses to unethical practices (e.g., Tuskegee Study, Nazi experiments), a series of ethical frameworks and regulations emerged, including the Nuremberg Code, the Declaration of Helsinki, and most notably, the Belmont Report.
➡️ Leads to: A deeper look into the Belmont Report and its enduring impact.
3. What does the Belmont Report contribute to research ethics? #
Answer:
It introduces three core principles:
- Respect for Persons: Informed consent and autonomy
- Beneficence: Minimize harm, maximize benefit
- Justice: Fair distribution of research benefits and burdens
➡️ Leads to: Applying these principles to modern AI data sources.
4. Where does AI get its data, and what ethical concerns arise? #
Answer:
AI uses data from research repositories, clinical records, and even consumer devices. Ethical concerns include consent validity, privacy, data security, and the risk of underrepresenting vulnerable populations.
➡️ Leads to: Addressing secondary uses of data and consent workarounds.
5. How can researchers ethically use data collected for other purposes? #
Answer:
Via:
- QA exemptions
- Use of de-identified data
- IRB-approved waiver of consent
These methods are sometimes necessary but ethically controversial due to risks of eroding public trust.
➡️ Leads to: The ethical dilemma of returning individual results.
6. Should researchers return results to participants? #
Answer:
It depends. Options range from never returning results (to avoid harm/confusion) to always returning them (to respect autonomy). Most agree on a middle ground: only return results that are valid and actionable.
➡️ Leads to: Examining systems where research and practice are merged—like a Learning Health System.
7. What is a Learning Health System (LHS), and how does it relate to AI? #
Answer:
An LHS continuously learns from clinical care data to improve outcomes. AI is central to this feedback loop, but it blurs the line between research and care, making traditional ethical boundaries harder to apply.
➡️ Leads to: Rethinking ethical frameworks for hybrid systems like LHS.
8. Is there an ethical model better suited for a Learning Health System? #
Answer:
Yes. A proposed model includes duties to:
- Respect patients (via transparency, not just consent)
- Improve care (beneficence)
- Reduce inequality (justice)
- Engage both clinicians and patients in the learning process
However, it lacks strict rules for handling trade-offs between these duties.
Summary:
Each principle in the Belmont Report supports the others. Respect enables informed choice, beneficence ensures that choice isn’t harmful, and justice guarantees fairness across all participants. As AI transforms healthcare, our ethical thinking must evolve accordingly.