# Ethical Responsibility in Medical AI: A Semi-Systematic Thematic Review and Multilevel Governance Model

## TL;DR

This paper reviews ethical challenges in medical AI, finding that transparency is heavily discussed while patient autonomy and professional roles are overlooked, and suggests a governance model to address these issues.

## Contribution

The paper introduces a multilevel governance model for AI in healthcare that integrates ethical principles and operational governance.

## Key findings

- Transparency and explainability dominate ethical discussions in medical AI, while patient autonomy and professional redefinition are largely neglected.
- Regulatory frameworks struggle to keep pace with AI innovation, leading to fragmented accountability.
- A multilevel governance model is proposed to address ethical responsibility across clinical, institutional, and regulatory dimensions.

## Abstract

What are the main findings?
Transparency and explainability dominate ethical discourse (34.8%), whereas patient autonomy (8.6%) and professional redefinition (1.1%) are neglected.A fragmented accountability landscape emerges, with regulatory frameworks lagging behind technological innovation.

Transparency and explainability dominate ethical discourse (34.8%), whereas patient autonomy (8.6%) and professional redefinition (1.1%) are neglected.

A fragmented accountability landscape emerges, with regulatory frameworks lagging behind technological innovation.

What are the implications of the main findings?
Healthcare institutions must operationalise multilevel governance models that integrate ex ante (preventive) and ex post (accountability) mechanisms.Policymakers should mandate algorithmic audits and participatory design to bridge gaps in epistemic justice in AI-driven medicine.

Healthcare institutions must operationalise multilevel governance models that integrate ex ante (preventive) and ex post (accountability) mechanisms.

Policymakers should mandate algorithmic audits and participatory design to bridge gaps in epistemic justice in AI-driven medicine.

Background: Artificial intelligence (AI) is transforming medical practice, enhancing diagnostic accuracy, personalisation, and clinical efficiency. However, this transition raises complex ethical challenges related to transparency, accountability, fairness, and human oversight. This study examines how the literature conceptualises and distributes ethical responsibility in AI-assisted healthcare. Methods: This semi-systematic, theory-informed thematic review was conducted in accordance with the PRISMA 2020 guidelines. Publications from 2020 to 2025 were retrieved from PubMed, ScienceDirect, IEEE Xplore databases, and MDPI journals. A semi-quantitative keyword-based scoring model was applied to titles and abstracts to determine their relevance. High-relevance studies (n = 187) were analysed using an eight-category ethical framework: transparency and explainability, regulatory challenges, accountability, justice and equity, patient autonomy, beneficence–non-maleficence, data privacy, and the impact on the medical profession. Results: The analysis revealed a fragmented ethical landscape in which technological innovation frequently outperforms regulatory harmonisation and shared accountability structures. Transparency and explainability were the dominant concerns (34.8%). Significant gaps in organisational responsibility, equitable data practices, patient autonomy, and professional redefinition were reported. A multilevel ethical responsibility model was developed, integrating micro (clinical), meso (institutional), and macro (regulatory) dimensions, articulated through both ex ante and ex post perspectives. Conclusions: AI requires governance frameworks that integrate ethical principles, regulatory alignment, and epistemic justice in medicine. This review proposes a multidimensional model that bridges normative ethics and operational governance. Future research should explore empirical, longitudinal, and interdisciplinary approaches to assess the real impact of AI on clinical practice, equity, and trust.