[Paper Review] Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond

Sidra Nasir, Rizwan Ahmed Khan+1 more|arXiv (Cornell University)|Aug 31, 2023

Artificial Intelligence in Healthcare and Education147 references11 citations

TL;DR

The paper proposes an ethical framework for AI in healthcare emphasizing transparency, fairness, explainability (XAI), human-centric oversight, and global standardization, while analyzing biases and limitations of AI systems.

ABSTRACT

In the past decade, the deployment of deep learning (Artificial Intelligence (AI)) methods has become pervasive across a spectrum of real-world applications, often in safety-critical contexts. This comprehensive research article rigorously investigates the ethical dimensions intricately linked to the rapid evolution of AI technologies, with a particular focus on the healthcare domain. Delving deeply, it explores a multitude of facets including transparency, adept data management, human oversight, educational imperatives, and international collaboration within the realm of AI advancement. Central to this article is the proposition of a conscientious AI framework, meticulously crafted to accentuate values of transparency, equity, answerability, and a human-centric orientation. The second contribution of the article is the in-depth and thorough discussion of the limitations inherent to AI systems. It astutely identifies potential biases and the intricate challenges of navigating multifaceted contexts. Lastly, the article unequivocally accentuates the pressing need for globally standardized AI ethics principles and frameworks. Simultaneously, it aptly illustrates the adaptability of the ethical framework proposed herein, positioned skillfully to surmount emergent challenges.

Motivation & Objective

• Motivate the need for an ethical framework as AI analytics become pervasive in healthcare and other domains.
• Propose a conscientious AI framework centered on transparency, equity, accountability, and human-centric design.
• Discuss the limitations, biases, and context-sensitivity of AI in healthcare to guide responsible adoption.
• Argue for global, standardized AI ethics principles and adaptable frameworks for emergent challenges.

Proposed method

• Synthesis of existing ethical concerns in AI/ML and DL with a focus on healthcare.
• Discussion of Explainable AI (XAI) approaches, including interpretable models and post-hoc methods (e.g., LIME, SHAP, GradCAM).
• Analysis of bias types (data-driven, systematic, generalization, human) and their impact on fairness in healthcare.
• Illustrative examples across medical imaging, ASD interventions, and clinical decision contexts to motivate transparency and trust.

Figure 1 : Machine Learning from data collection to its interpretability and explainability to humans

Experimental results

Research questions

• RQ1 What ethical considerations arise from deploying AI in healthcare, and how can transparency, fairness, and human oversight be incorporated?
• RQ2 How can Explainable AI (XAI) and interpretable methods mitigate black-box concerns in clinical settings?
• RQ3 What types of biases (data, systematic, generalization, human) threaten fairness in healthcare AI, and how can they be identified and mitigated?
• RQ4 Why is global standardization of AI ethics principles important, and how can frameworks adapt to emerging AI challenges?

Key findings

• AI systems in healthcare offer substantial benefits but raise transparency, accountability, and trust concerns due to black-box characteristics.
• Explainable AI techniques (interpretable models, LIME/SHAP, GradCAM) can improve clinician trust and regulatory compliance.
• Bias in data and design (statistical/social, dataset representation, and systemic biases) can lead to inequitable health outcomes; addressing fairness is essential.
• There is a need for globally standardized AI ethics principles and adaptable frameworks to address evolving AI applications beyond healthcare.
• Human oversight and education are critical to ensure responsible AI deployment and to preserve empathy in patient care.

Figure 2 : (a)Explainabilty vs (b) Interpretability.