Journal Article

A Conceptual Framework for Applying Ethical Principles of AI to Medical Practice

Hagos, Desta Haileselassie, Keles, Elif, Zhang, Zheyuan, Durak, Gorkem, Sharma, Vanshali, Jha, Debesh, Rauniyar, Ashish, Yazidi, Anis, Håkegård, Jan Erik, Tomar, Nikhil Kumar, Srivastava, Abhishek, Cicek, Vedat, Topcu, Ahmet, Miller, Frank H., Bagci, Ulas

2025

Overview

Artificial Intelligence (AI) is reshaping healthcare through advancements in clinical decision support and diagnostic capabilities. While human expertise remains foundational to medical practice, AI-powered tools are increasingly matching or exceeding specialist-level performance across multiple domains, paving the way for a new era of democratized healthcare access. These systems promise to reduce disparities in care delivery across demographic, racial, and socioeconomic boundaries by providing high-quality diagnostic support at scale. As a result, advanced healthcare services can be affordable to all populations, irrespective of demographics, race, or socioeconomic background. The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care. In contrast to humans, AI can potentially uncover complex relationships in the data from a large set of inputs and generate new evidence-based knowledge in medicine. However, integrating AI into healthcare raises several ethical and philosophical concerns, such as bias, transparency, autonomy, responsibility, and accountability. In this study, we examine recent advances in AI-enabled medical image analysis, current regulatory frameworks, and emerging best practices for clinical integration. We analyze both technical and ethical challenges inherent in deploying AI systems across healthcare institutions, with particular attention to data privacy, algorithmic fairness, and system transparency. Furthermore, we propose practical solutions to address key challenges, including data scarcity, racial bias in training datasets, limited model interpretability, and systematic algorithmic biases. Finally, we outline a conceptual algorithm for responsible AI implementations and identify promising future research and development directions.

• Publisher: MDPI AG,MDPI
• Subject: Accuracy / Algorithms / Annotations / Artificial intelligence / artificial intelligence (AI) / Best practice / Bias / Compliance / Confidentiality / Consent / Data collection / Data integrity / Datasets / Decision making / Democratization / Demographics / Demography / ethical AI / Ethical aspects / Ethics / Evidence-based medicine / Health care / Health care delivery / Health care industry / Health care policy / Health care reform / Health services / Image analysis / Image processing / Medical imaging / Medical imaging equipment / Medical personnel / Medicine / Officials and employees / Patient safety / philosophical AI / Practice / Privacy / Professional ethics / R&D / Racism / Research & development / Resource allocation / trustworthy AI