Shaping the Future of Healthcare: Ethical Clinical Challenges and Pathways to Trustworthy AI

Polat Göktaş; Andrzej Grzybowski

doi:10.3390/jcm14051605

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Shaping the Future of Healthcare: Ethical Clinical Challenges and Pathways to Trustworthy AI

Polat Göktaş, Andrzej Grzybowski

Multidisciplinary Digital Publishing Institute (2025) • Volume 14, Issue 5, Pages 1605-1605

SynthesisNarrativeReviewPDF AvailableGrade Eligible

Overall Assessment

Limited Methodological Quality

Assessment created by PaperScorers Medical AI v0.1.0 on Dec 15, 2025

D

46/100

Key Takeaways

•Narrative perspective proposing ‘Regulatory Genome’ and trust metrics for healthcare AI.
•Structured but non‑systematic search; broad, multidisciplinary synthesis.
•Emphasis on transparency, accountability, fairness, security, and sustainability.
•Policy proposals align with EU AI Act/FDA SaMD; call for continuous monitoring.
•No empirical evaluation; frameworks remain untested in real‑world deployments.

Conclusion

Useful high‑level roadmap with clear contributions, but evidentiary strength is limited by non‑systematic methods and absence of empirical validation.

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Quality Dimensions

Bias & Integrity

COI handling, outcome switching, selective reporting

C60

Methods Rigour

Protocol clarity, sampling, controls

F28

External Validity

Generalisability, setting, population

C-55

Transparency & Reproducibility

Data/code/materials availability, preregistration

D-34

Significance & Novelty

Contribution vs prior art; context if known

C64

Statistical Validity

Models, assumptions, multiplicity, CI priority

F22

Integrity & Transparency

Integrity checks

P-hacking risk?

?

None suspected

Signs of selective hypothesis testing or analysis choices that could inflate false positive rates.

Outcome switching?

?

None suspected

Evidence that primary outcomes were changed during or after the study, potentially distorting results.

Conflict of interest?

?

None suspected

Authors have financial or other relationships that could bias research findings.

Data integrity issues?

?

None suspected

Concerns about the accuracy, completeness, or authenticity of the reported data.

Open science signals

Research data

?

Not applicable

Raw data used in the analyses is publicly available or accessible upon request.

Analysis code

?

Not applicable

Code or scripts used to analyze the data are shared for reproducibility.

Study materials

?

Not applicable

Protocols, questionnaires, and other materials are publicly available.

Premise

Primary Research Question

How to achieve trustworthy AI in healthcare via ethics, governance, and actionable policy frameworks?

Hypothesis is Falsifiable

—Not specified

Conceptual perspective without testable hypotheses.

PICO Framework

Population—Not specified

Non‑clinical narrative synthesis.

Intervention—Not specified

No interventions; policy/ethics frameworks only.

Comparison—Not specified

No comparator design.

Outcome—Not specified

No predefined clinical outcomes.

Literature Positioning

Literature Review Balanced

Needs review

Structured search described but no PRISMA/flow; selection may be subjective.

Evidence: p. 2

Research Gap Clearly Stated

Well covered

Identifies lack of integrated ethics–governance–implementation frameworks.

Evidence: pp. 2–3

Stated Contribution Clear

Well covered

‘Key Contributions’ section enumerates framework, metrics, policy roadmap.

Evidence: p. 7

Study Provenance

Peer reviewed venueFunding disclosedNo conflicts declared5 affiliations listed

Authors & Affiliations

UCD School of Computer Science, Dublin; Dept. of Ophthalmology UWM Olsztyn; Institute for Research in Ophthalmology, Poznan.

Funding Statement

This research received no external funding.

Conflicts of Interest

The authors declare no conflicts of interest.

Peer Review

✓Journal of Clinical Medicine (MDPI) peer‑reviewed; article type ‘Perspective’.

Evidence: p. 1

Methodological Assessment

All design modules are marked as not applicable. Use the accordion below to review their details.

Modules without applicable checklist items (10)

These designs were fully marked as not applicable but remain available for reference.

Abstract

Background/Objectives: Artificial intelligence (AI) is transforming healthcare, enabling advances in diagnostics, treatment optimization, and patient care. Yet, its integration raises ethical, regulatory, and societal challenges. Key concerns include data privacy risks, algorithmic bias, and regulatory gaps that struggle to keep pace with AI advancements. This study aims to synthesize a multidisciplinary framework for trustworthy AI in healthcare, focusing on transparency, accountability, fairness, sustainability, and global collaboration. It moves beyond high-level ethical discussions to provide actionable strategies for implementing trustworthy AI in clinical contexts. Methods: A structured literature review was conducted using PubMed, Scopus, and Web of Science. Studies were selected based on relevance to AI ethics, governance, and policy in healthcare, prioritizing peer-reviewed articles, policy analyses, case studies, and ethical guidelines from authoritative sources published within the last decade. The conceptual approach integrates perspectives from clinicians, ethicists, policymakers, and technologists, offering a holistic “ecosystem” view of AI. No clinical trials or patient-level interventions were conducted. Results: The analysis identifies key gaps in current AI governance and introduces the Regulatory Genome—an adaptive AI oversight framework aligned with global policy trends and Sustainable Development Goals. It introduces quantifiable trustworthiness metrics, a comparative analysis of AI categories for clinical applications, and bias mitigation strategies. Additionally, it presents interdisciplinary policy recommendations for aligning AI deployment with ethical, regulatory, and environmental sustainability goals. This study emphasizes measurable standards, multi-stakeholder engagement strategies, and global partnerships to ensure that future AI innovations meet ethical and practical healthcare needs. Conclusions: Trustworthy AI in healthcare requires more than technical advancements—it demands robust ethical safeguards, proactive regulation, and continuous collaboration. By adopting the recommended roadmap, stakeholders can foster responsible innovation, improve patient outcomes, and maintain public trust in AI-driven healthcare.

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Study Overview

Study Design

Narrative Review

Primary classification used to evaluate this research.

Population

Peer‑reviewed literature and policy sources on AI ethics/governance in healthcare (last decade).

Brief summary of who or what was studied.

Pre-registration

Not registered

Indicates whether the protocol or trial was registered before data collection.

Reviewer Notes

Perspective/narrative review with a structured search; no empirical cohorts or trials.

Publication Details

DOI

10.3390/jcm14051605

Published

February 27, 2025

Citations

103 citations

FWCI

233.05

External Resources

DOI Link OpenAlex

Disclaimer: This assessment is generated by AI and should not be the sole basis for clinical or research decisions. Always review the original paper and consult with domain experts.

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