Generative AI and Ethics in Healthcare: Why Governance Matters Now

Generative artificial intelligence is rapidly moving from experimentation to implementation in hospitals, clinics, and health systems worldwide. Large language models and multimodal generative systems can summarise medical records, draft clinical notes, support diagnostic reasoning, and accelerate research pipelines. But as deployment accelerates, so do the questions: How do we ensure that these tools are safe, fair, and aligned with core principles of medical ethics?

In recent years, international organisations have started to respond. The World Health Organization (WHO) has issued guidance on the ethics and governance of artificial intelligence for health, emphasising that AI systems must respect human rights, protect patient data, and remain under appropriate human oversight. At the same time, bodies such as UNESCO and the OECD have developed global AI ethics principles that stress transparency, accountability, and fairness across sectors, including healthcare.

For health professionals, this means generative AI cannot simply be treated as “just another IT tool”. It becomes part of the clinical decision-making environment. When a model drafts a discharge summary, suggests a diagnosis, or prioritises patients in a triage system, questions arise about liability, bias, explainability, and the patient–clinician relationship.

From potential to practice: the key ethical tensions

In practice, several ethical tensions appear again and again when generative AI enters healthcare:

  • Bias in training data: If the data used to train a model under-represents certain populations, the outputs may systematically disadvantage those groups, reinforcing existing inequities.
  • Data privacy and patient data ownership: Patients may not fully understand where their data goes, who can reuse it, or how long it will be stored, especially when cloud-based models are involved.
  • Explainability and trust: Clinicians and patients need to understand, at least at a high level, why a system is suggesting a particular answer or recommendation.
  • Autonomous decision-making: As diagnostic or triage systems become more capable, there is a risk of “automation bias”, where human professionals defer too quickly to the machine.
  • Health equity: If advanced AI tools are deployed only in well-resourced centres, they may widen the gap between those who have access to high-quality care and those who do not.

What this book offers

Generative Artificial Intelligence and Ethics for Healthcare provides a structured, in-depth guide to these issues. It begins with an accessible introduction to generative AI concepts in healthcare, then moves into chapters on bias, calibration, explainability, and core ethical concepts. Later chapters address data privacy and patient data ownership, autonomous diagnosis, health equity, lawfulness, empathy, and the role of governability in AI-enabled health systems.

For universities, hospitals, regulators, and AI teams, this book offers more than a theoretical discussion. It links ethical analysis to real-world case studies and policy debates, helping readers design governance frameworks, internal guidelines, and training programmes for clinicians and data scientists.

Connecting with global guidance and initiatives

The book’s themes resonate strongly with work already underway at the international level. Readers who want to explore the broader landscape can consult:

Together with Generative Artificial Intelligence and Ethics for Healthcare, these resources help institutions build a coherent view of how generative AI can support better care while protecting patients and promoting equity.

Why this matters for libraries and professionals

For academic and hospital libraries, this title is a strategic addition to collections on AI, digital health, medical ethics, and health policy. It serves clinicians, ethicists, data scientists, and students who need to navigate both the technical and normative dimensions of AI. For professionals, it is a practical roadmap for designing policies, protocols, and training programmes that keep patient welfare at the centre while embracing innovation.

At CLNZ Books, I curate specialised academic and professional titles that respond to these emerging needs. If your team, department, or library is discussing how to govern generative AI in healthcare, this book is a timely resource for informed, responsible decision-making.

👉 You can order Generative Artificial Intelligence and Ethics for Healthcare with FREE worldwide shipping here:



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