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Outline

Title
Abstract
FAQs
Introduction
Methodology
Search Terms Scopus Number of Search Results
Pubmed Number of Search Results
Concerns Related to Traceability
Discussion
The Inverse Data Quality Law
Conclusion
References

The Ethics of AI in Health Care: An Updated Mapping Review

Profile image of Luciano FloridiLuciano Floridi

2024

Abstract

Artificial intelligence's impact on healthcare is undeniable. What is less clear is whether it will be ethically justifiable. Just as we know that AI can be used to diagnose disease, predict risk, develop personalized treatment plans, monitor patients remotely, or automate triage, we also know that it can pose significant threats to patient safety and the reliability (or trustworthiness) of the healthcare sector as a whole. These ethical risks arise from (a) flaws in the evidence base of healthcare AI (epistemic concerns); (b) the potential of AI to transform fundamentally the meaning of health, the nature of healthcare, and the practice of medicine (normative concerns); and (c) the 'black box' nature of the AI development pipeline, which undermines the effectiveness of existing accountability mechanisms (traceability concerns). In this chapter, we systematically map (a)-(c) to six different levels of abstraction: individual, interpersonal, group, institutional, sectoral, and societal. The aim is to help policymakers, regulators, and other high-level stakeholders delineate the scope of regulation and other 'softer' Governing measures for AI in healthcare. We hope that by doing so, we may enable global healthcare systems to capitalize safely and reliably on the many life-saving and improving benefits of healthcare AI.

FAQs

sparkles

AI

What ethical issues arise from inconclusive evidence in AI healthcare diagnostics?add

Inadequate dataset quality can lead to misdiagnoses, affecting patient safety—e.g., faulty smartwatch readings can misdiagnose arrhythmias. Such systemic failures may disproportionately impact marginalized groups, exacerbating healthcare inequalities.

How does the use of generative AI transform patient-physician relationships?add

Generative AI risks displacing clinicians' roles, potentially undermining trust and the quality of patient care. It blurs the lines between algorithm-driven decisions and human empathy, which is crucial for effective healthcare.

What impacts do algorithmic biases have on healthcare equity?add

AI models trained on biased datasets can generate discriminatory outcomes, particularly for underrepresented groups. For instance, cervical cancer risk predictions can misqualify specific ethnic groups’ health, leading to systemic healthcare disparities.

When did notable healthcare guidelines for AI ethics emerge?add

By 2021, significant guidelines were issued, including the FDA's AI/ML software action plan and WHO's governance guidance. These developments reflect growing recognition of regulatory needs amidst rapid technological advancements.

Why is traceability an ethical concern in AI system deployments?add

Lack of clear responsibility attribution complicates accountability for negative AI outcomes, as seen with clinical decision support systems. This opacity can undermine trust in healthcare providers and the overall system.

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