Council Post: Responsible AI in Healthcare

The growing significance and widespread adoption of AI have raised concerns among researchers and healthcare practitioners regarding the lack of transparency and responsibility in various aspects, from data acquisition to implementing algorithms in the field. However, much of the research in this area has been conducted within Western and European contexts. This is problematic because AI-based systems are highly influenced by the context in which they are deployed. As a result, many non-Western scholars find that much of the existing research on using AI-based systems in the public sector is not directly relevant to their contexts.

AI/ML-enabled Medical Devices with Governance

Implement robust regulatory frameworks for AI/ML-enabled medical devices; strong data privacy and security measures must be incorporated to protect patient confidentiality and prevent data breaches. These frameworks should also address the need for fair and unbiased algorithms, promoting transparency and explainability to ensure that healthcare providers and patients can understand and trust the AI system’s decisions. Regulatory bodies are essential for the safe and responsible use of these devices by clarifying the responsibilities of manufacturers and healthcare providers. Additionally, adherence to interoperability standards is crucial for seamless integration into healthcare systems. Governance frameworks should promote the development and adoption of these standards. Education and training for healthcare professionals are also critical, with governance frameworks including provisions for ongoing education to ensure the effective use of AI/ML-enabled medical devices and improve patient care. Regulatory bodies such as the FDA in the United States and the EMA in Europe are actively working to develop guidelines for approving and monitoring these devices.

Image Source: nih.gov

Empowering Global AI Research: Strategies for Non-Western Contexts

In the dynamic realm of AI research, ensuring its relevance and applicability across diverse contexts is paramount. Researchers can enhance this by contextualizing their findings, illustrating how AI can be effectively adopted in non-Western countries, considering their unique cultural, social, and economic landscapes. Collaborative research efforts between Western and non-Western scholars further enrich the discourse, bridging gaps and ensuring inclusivity in AI research.

It is essential to tailor and standardize training datasets to real-world settings before their integration into healthcare systems. This approach guarantees that AI algorithms are trained on data that accurately represents diverse patient populations and healthcare systems globally.

Ethical considerations are central to this endeavour. Integrating ethical frameworks for AI and technology into the review process ensures algorithm and dataset auditability, validation, and transparency, aligning them with governance frameworks.

Establishing regulatory bodies akin to the FDA and EMA in non-Western countries can promote collaborative efforts in implementing responsible AI in healthcare. This global collaboration is vital for ethically and responsibly deploying AI technologies for the benefit of all.

Conclusion

Responsible adoption of AI in healthcare is essential to address the growing concerns regarding transparency, responsibility, and ethical considerations. Implementing robust regulatory frameworks and strong data privacy measures to protect patient confidentiality and ensure the safe and responsible use of AI/ML-enabled medical devices is crucial. Collaboration between Western and non-Western scholars is key to enhancing the relevance and applicability of AI research in diverse contexts. By promoting transparency, fairness, and accountability, we can ensure that AI technologies are deployed ethically and responsibly, benefiting patients and society. Continued efforts to develop and adopt ethical frameworks and regulatory standards will be crucial in guiding the future of AI in healthcare towards a more inclusive and responsible direction.

Disclaimer: All views expressed by Santosh are personal and should not be considered as attributable to his employer.

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill the form here.