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

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 regulat...

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Veröffentlicht in:Journal of clinical medicine Jg. 14; H. 5; S. 1605
Hauptverfasser: Goktas, Polat, Grzybowski, Andrzej
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 01.03.2025
MDPI
Schlagworte:
Accountability
Algorithms
Artificial intelligence
Automation
Bias
Collaboration
Decision making
Diagnostics
Ethical aspects
False information
Generative artificial intelligence
Health aspects
Health care
Health disparities
Health literacy
Interdisciplinary aspects
Low income groups
Management
Medical care
Medical innovations
Medical personnel
Medical records
Neural networks
Patient safety
Poland
Privacy
Professional ethics
Technology application
Transparency
Trust
Poland
health policy
natural language processing
regulation
bias
privacy
ethics
large language model
machine learning
artificial intelligence
ISSN:2077-0383, 2077-0383
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:2077-0383
2077-0383
DOI:10.3390/jcm14051605