AI Diagnostic Technologies and the Gap in Colorectal Cancer Screening Participation

Area of Science:

• Medical Informatics
• Oncology
• Human-Computer Interaction

Background:

• Artificial intelligence (AI) is a growing focus for augmenting clinical diagnostic tools in colorectal cancer (CRC) detection.
• AI-enhanced CRC diagnosis offers significant potential for improving patient outcomes and reducing mortality.
• However, widespread low patient participation globally limits the effectiveness of these AI tools.

Purpose of the Study:

• To examine the human factors contributing to low patient participation in AI-augmented CRC screening.
• To propose a socio-technical approach for developing and implementing AI tools that enhances screening uptake.
• To investigate how psycho-social and cultural dimensions influence the adoption of AI in CRC detection.

Main Methods:

• Literature review on AI in CRC detection and patient participation.
• Analysis of human factors influencing technology adoption in healthcare.
• Conceptual framework development for a socio-technical approach to AI implementation.

Main Results:

• Low patient participation is a significant barrier to the effectiveness of AI in CRC detection.
• Human factors, including psycho-social and cultural aspects, play a critical role in screening uptake.
• Current AI development may overlook crucial elements for patient engagement.

Conclusions:

• A socio-technical approach sensitive to psycho-social and cultural factors is needed for AI tool development in CRC.
• Addressing human factors can improve patient participation and enhance the impact of AI on CRC mortality and outcomes.
• Future AI tools for CRC detection must prioritize patient engagement and accessibility.