Exploring the Effects of Artificial Intelligence on Healthcare: Emphasizing Patient Safety, Data Security and Fair Access

Abstract

The use of artificial intelligence (AI) technologies in health systems has greatly improved clinical decision-making, process efficiency, and personalized care for patients. However, evolving AI use in health systems continues to raise social, ethical, and safety concerns. This paper examines three areas of concern—patient safety, data privacy, and healthcare access—and their corresponding risks of introducing AI to these areas. Issues of untested clinical algorithms, the black box nature of AI algorithms, and bias in data are all explored in terms of equitable access to healthcare. The protection of patient safety, privacy, and justice is key to the social justification of AI into health systems.

Introduction

The study examines the role of AI in healthcare, highlighting its potential to improve clinical workflows, diagnostic accuracy, treatment planning, and patient outcomes. Despite these benefits, challenges remain around patient safety, algorithm transparency, data privacy, and equitable access, particularly in low-resource settings.

The research employed a qualitative exploratory approach, combining systematic literature review, case studies, and expert interviews to understand ethical, operational, and practical implications of AI in healthcare.

A proposed AI healthcare framework emphasizes patient safety, privacy, and fairness. Key features include explainable AI (XAI) for transparent decision-making, privacy-preserving techniques such as federated learning and homomorphic encryption, bias detection modules, and ongoing validation to ensure reliability, inclusiveness, and ethical governance.

Results indicate that AI systems can improve diagnostic accuracy (15–20% higher than traditional methods), reduce clinical errors, enhance data security (35% fewer breaches), and support equitable healthcare delivery. Challenges include computational costs, data harmonization, and ensuring ethical, fair, and accountable deployment. Overall, the framework provides a structured, responsible approach for safe, secure, and inclusive AI integration in healthcare.

Conclusion

The use of AI in health care is a landmark step toward precision, efficiency and inclusivity. AI can contribute to greater diagnostic accuracy, support workflows, and improve patient-centric care. However, these benefits must be balanced with ethical responsibility, data governance and principles of appropriate access. Development of robust sustainable and trustworthy AI in health care will require regulation, engagement of technologists and clinicians to build transparent, bias-resistant and secure frameworks.

References

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Copyright

Copyright © 2025 Dr. N. Prabhu , Sornamangai M, Janani T, Preethi R, Thenmozhi M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.