Rethinking AI Workflows: Guidelines for Scientific Evaluation in Digital Health Companies

Abstract

Artificial intelligence (AI) is revolutionizing digital health, driving innovation in care delivery and operational efficiency. Despite its potential, many AI systems fail to meet real-world expectations due to limited evaluation practices that focus narrowly on short-term metrics like efficiency and technical accuracy. Ignoring factors such as usability, trust, transparency, and adaptability hinders AI adoption, scalability, and long-term impact in health care. This paper emphasizes the importance of embedding scientific evaluation as a core operational layer throughout the AI life cycle. We outline practical guidelines for digital health companies to improve AI integration and evaluation, informed by over 35 years of experience in science, the digital health industry, and AI development. It describes a multistep approach, including stakeholder analysis, real-time monitoring, and iterative improvement, that digital health companies can adopt to ensure robust AI integration. Key recommendations include assessing stakeholder needs, designing AI systems that can check its own work, conducting testing to address usability and biases, and ensuring continuous improvement to keep systems user-centered and adaptable. By integrating these guidelines, digital health companies can improve AI reliability, scalability, and trustworthiness, driving better health care delivery and stakeholder alignment.

Ähnliche Arbeiten