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Cited by 336Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Shaping the future of healthcare: ethical clinical challenges and pathways to trustworthy AI

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Goktas, Polat, Grzybowski, Andrzej·2025·Journal of Clinical Medicine
DOI 10.3390/jcm14051605PMC PMC11900311
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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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  • Traditional machine learning models often lack interpretability, making them less suited for ethically sensitive applications in mental health law.

    “Furthermore, traditional machine learning models often lack interpretability, making them less suited for ethically sensitive applications in mental health law”

    From A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support (2025) · cites this paper as Goktas and Grzybowski, 2025

    Section 3.1 says that opaque AI models, especially deep-learning systems, can lack interpretability and pose risks in clinical practice. But the Introduction says traditional rule-based models and decision trees offer greater transparency. The paper does not assess suitability for applications in mental health law; no page numbers are available in our copy. (LLM, conf=0.88)

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Cited by

Sorted by citations

Authors
Goktas, P., & Grzybowski, A.
Affiliations
  • UCD School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland
  • Foundation for the Development of Ophthalmology (PL)
Corresponding author
Grzybowski, Andrzej
ORCID
Venue
Journal of Clinical Medicine
Publisher
MDPI AG
Published
2025-02-27
Volume
14
Issue
5
Pages
1605
ISSN
2077-0383 (ISSN-L)
DOI
10.3390/jcm14051605
PMC
PMC11900311
OpenAlex
W4407998253
Cited by
336
References
30
Citations by year
2026: 208 · 2025: 126 · 2024: 1
FWCI
79.83
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Clinical Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Keywords
trustworthy AI, artificial intelligence in healthcare, AI ethics, AI governance, algorithmic bias, data privacy, regulatory oversight, transparency and accountability, fairness, environmental sustainability, bias mitigation, multi-stakeholder engagement, international cooperation, AI policy

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