Abstract
AI and ML are revolutionizing healthcare by enhancing diagnostics, treatment planning, and operational efficiency. However, their integration raises pressing ethical concerns, including data privacy, algorithmic bias, transparency, clinical validation, and accountability. AI-driven healthcare models often rely on vast patient datasets, making data security and informed consent critical issues. Algorithmic biases, if left unchecked, can exacerbate healthcare disparities, leading to misdiagnoses or unequal treatment outcomes across different patient populations. Transparency and explainability remain significant challenges, as black-box AI models hinder trust and clinical adoption. This study provides a comprehensive analysis of these ethical dimensions, drawing from an in-depth review of AI’s role in healthcare. We examine case studies where AI biases led to adverse patient outcomes, discuss the importance of regulatory compliance, and explore strategies for developing fair, interpretable, and clinically validated AI models. Additionally, we highlight best practices for ensuring ethical AI deployment, such as diversifying training datasets, incorporating bias audits, and fostering collaboration among healthcare professionals, technologists, and policymakers. By proactively addressing these ethical challenges, stakeholders can ensure AI serves as a tool for equitable and responsible healthcare, prioritizing patient welfare, transparency, and long-term sustainability in medical decision-making. This study offers actionable insights to navigate the ethical complexities of AI and implement responsible AI-driven innovations in healthcare.
Presenters
Jagbir KaurStrategy and Ops Manager, Product and Sales Activation, Google, United States
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
RESPONSIBLE AI, DATA ETHICS, PRIVACY, ALGORITHMIC BIAS, REGULATORY COMPLIANCE
