No-boundary thinking: a viable solution to ethical data-driven AI in precision medicine

Area of Science:

• Computer Science
• Medical Informatics
• Bioethics

Background:

• Artificial Intelligence (AI) algorithms are increasingly used for critical decisions in policy and health.
• AI relies on data-informed models, necessitating well-managed data for reliable and unbiased outcomes, especially in precision medicine.
• Ensuring AI algorithms do not perpetuate human biases is paramount.

Purpose of the Study:

• To highlight the potential of AI in advancing precision medicine.
• To emphasize the critical need for ethical considerations and careful implementation of AI in healthcare.
• To advocate for a 'no-boundary' or convergent approach in AI development and deployment.

Main Methods:

• The study advocates for a 'no-boundary' thinking approach, integrating diverse expert perspectives.
• This approach emphasizes collaborative problem definition and solving in AI and data management.
• It stresses the importance of a spectrum of activities requiring attention from a multidisciplinary team.

Main Results:

• AI offers significant promise for enhancing precision medicine applications.
• A 'no-boundary' approach is essential for ensuring sound and ethical AI-driven decisions.
• Careful data procurement, cleaning, and organization are vital for trustworthy AI results.

Conclusions:

• Advancing AI in precision medicine requires a cautious, ethically-informed strategy.
• A no-boundary, convergent approach involving diverse expertise is fundamental for responsible AI implementation.
• This integrated methodology ensures AI, data, and scientific foundations align for viable conclusions and policies.