The field of healthcare is going through a paradigm shift, and artificial intelligence is at the forefront of this change, especially in drug safety surveillance. While the potential benefits are enormous, we identified a critical gap in the ethical frameworks governing this integration. Our research was motivated by the urgent need to ensure patient safety and privacy are not compromised in the rush to adopt these powerful technologies.
What makes this particularly relevant now is the rapid acceleration of AI adoption in healthcare. We see increasingly sophisticated AI systems being deployed for adverse event detection and risk assessment, but without comprehensive ethical guidelines, we risk creating systems that could perpetuate biases or compromise patient privacy. Our paper addresses these challenges head-on, providing practical solutions for organisations implementing AI in pharmacovigilance.
Our framework is distinctive because it addresses the entire lifecycle of AI in pharmacovigilance, from initial data collection through to regulatory reporting. What makes it particularly innovative is its practical, implementable approach to ethical considerations.
The framework consists of several interconnected components. First, we address data privacy and security through privacy-preserving techniques like differential privacy and federated learning. Second, we tackle algorithmic bias through comprehensive guidelines for diverse data collection and regular bias testing. Third, we emphasize transparency and explainability in AI decision-making through techniques like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations).
Most importantly, we have designed the framework to be adaptable and scalable, recognising that both AI technology and ethical considerations will continue to evolve.
Q: How does your research address the challenge of balancing innovation with ethical responsibility?
This is perhaps one of the most crucial aspects of our work. We recognise that innovation in AI can dramatically improve drug safety monitoring, but it shouldn't come at the cost of ethical considerations. Our research provides specific guidelines for maintaining this balance.
For instance, we propose a multi-stakeholder approach where AI developers, healthcare providers, and regulatory experts collaborate throughout the development process. We have outlined specific checkpoints where ethical considerations must be evaluated without impeding technological progress.
We have also introduced the concept of "ethical roadmap" in AI development for pharmacovigilance. This means incorporating ethical considerations from the earliest stages of system development rather than treating them as compliance requirements to be addressed later.
A: Transparency is indeed crucial, particularly in healthcare, where AI decisions can directly impact patient safety. Our paper proposes several practical approaches to achieve this. First, we advocate for explainable AI (XAI) techniques that can provide clear rationales for AI decisions in drug safety monitoring.
We have detailed, specific methodologies for maintaining transparency at different levels, from algorithm development to result interpretation. This includes maintaining comprehensive documentation of training data sources, regular audits of AI decisions, and creating interpretable outputs that healthcare professionals can easily understand and validate.
Importantly, we have also addressed how to maintain transparency without compromising system performance or intellectual property rights, which has been a significant challenge in the field.
Read more: https://www.pharmafocusasia.com/interviews/ashish-jain
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