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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Safety and Precision AI for a Modern Digital Health System.

Elizabeth M Borycki¹, Linda W P Peute², Femke van Sinderen²

¹School of Health Information Science, University of Victoria, Victoria, British Columbia, Canada.

Yearbook of Medical Informatics

|April 8, 2025

Summary

Artificial intelligence (AI) can enhance healthcare efficiency and personalization. Addressing AI safety concerns and implementing rigorous testing are crucial for its successful integration into modern digital health systems.

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Area of Science:

Healthcare Technology
Medical Informatics
Artificial Intelligence

Background:

Artificial intelligence (AI) is increasingly being developed and deployed in healthcare to improve efficiency and enable personalized medicine.
The integration of AI in healthcare presents challenges, particularly concerning safety and the potential for technology-induced errors.
Ensuring the safe adoption of AI is paramount to realizing its full potential in precision healthcare.

Purpose of the Study:

To examine the relationship between AI and safety in healthcare.
To explore AI's connection to technology-induced errors.
To identify and categorize published safety issues associated with AI in healthcare.

Main Methods:

Review and categorization of published safety issues related to AI in healthcare.
Analysis of error sources, including data and AI design.
Discussion of recommendations for rigorous testing and evaluation.

Main Results:

Safety concerns with AI in healthcare stem from various sources, including data quality and application design.
Inadequate testing and limited analysis during procurement processes are significant issues.
Published safety issues highlight the need for a structured approach to AI implementation.

Conclusions:

Rigorous testing and evaluation, from laboratory to naturalistic settings, are essential for safe AI adoption.
Addressing data-driven errors and design flaws is critical for AI safety.
Implementing robust evaluation strategies will support the development of safe and precise AI for digital health.