Perceived Benefits of AI-CDSSs Drive Adoption, While Cost and Lack of Transparency Hinder It

Why It Matters

Understanding the psychological and perceptual factors influencing the adoption of intelligent technologies is crucial for successful integration into clinical practice. Designers and developers must prioritize demonstrating clear utility and addressing concerns about algorithmic opacity to foster trust and encourage widespread use.

Key Finding

The study found that healthcare professionals' willingness to use AI-powered clinical decision support tools is primarily driven by how beneficial they believe these tools to be. However, the effort required to use the technology and a lack of understanding or control over how the AI works can significantly reduce their willingness to adopt.

Key Findings

• Perceived benefits of AI-CDSSs are the strongest predictor of adoption intention.
• Technology effort cost negatively impacts attitudes towards AI-CDSSs.
• Social and institutional influence positively fosters acceptance.
• Perceived control and transparency over AI enhance trust.
• Explainable and clinician-supervised AI systems are necessary.

Research Evidence

Aim: What are the key determinants influencing healthcare professionals' intention to adopt Artificial Intelligence Clinical Decision Support Systems (AI-CDSSs)?

Method: Quantitative Research

Procedure: A cross-sectional study was conducted using a structured questionnaire administered to healthcare professionals. Data were analyzed using structural equation modeling (PLS-SEM) to assess the relationships between perceived benefits, technological costs, social/institutional influence, transparency/control, and the intention to adopt AI-CDSSs.

Sample Size: 440 participants

Context: Healthcare

Design Principle

User trust in intelligent systems is built upon perceived utility, ease of use, and understandable, controllable algorithmic processes.

How to Apply

When designing AI-driven tools for professional use, conduct user research to identify key perceived benefits and potential barriers related to effort and transparency. Develop clear communication strategies and user interfaces that empower users with understanding and control.

Limitations

The study's findings are based on self-reported intentions and may not perfectly reflect actual adoption behavior. The cross-sectional nature limits the ability to establish causality.

Student Guide (IB Design Technology)

Simple Explanation: People are more likely to use new AI tools in healthcare if they see how helpful they are, but they won't use them if they are too hard to use or if they don't understand how they work.

Why This Matters: This research highlights that simply developing advanced technology isn't enough; understanding the human factors like perception, trust, and ease of use is essential for successful product design and implementation.

Critical Thinking: To what extent do the 'perceived benefits' outweigh the 'technological effort cost' for different user groups within healthcare, and how can design mitigate this trade-off?

IA-Ready Paragraph: This research demonstrates that the successful integration of AI-CDSSs in healthcare is contingent upon users perceiving significant benefits, manageable technological effort, and adequate transparency and control over the system's algorithms. These human factors directly influence adoption intention, underscoring the need for design strategies that prioritize user understanding and trust.

Project Tips

• When researching user adoption of technology, consider both the perceived advantages and the potential drawbacks like complexity or lack of clarity.
• Use surveys and statistical analysis to quantify user perceptions and their impact on technology acceptance.

How to Use in IA

• This study can be used to justify the importance of user-centered design principles when developing AI-powered systems, emphasizing the need to address user perceptions of benefits, costs, and transparency.

Examiner Tips

• Ensure your research clearly links user perceptions to adoption intentions, using appropriate quantitative methods to support your claims.

Independent Variable: ["Perceived benefits","Technological effort cost","Social and institutional influence","Transparency and control of algorithms"]

Dependent Variable: Intention to adopt AI-CDSSs

Controlled Variables: ["Type of healthcare professional","Experience level","Specific AI-CDSS being considered"]

Strengths

• Large sample size provides statistical power.
• Use of structural equation modeling allows for complex relationship analysis.

Critical Questions

• How might the actual performance of AI-CDSSs differ from perceived benefits, and how does this discrepancy affect long-term adoption?
• What are the ethical implications of relying on AI for clinical decisions, particularly concerning accountability and potential biases?

Extended Essay Application

• Investigate the impact of different levels of AI transparency on user trust and decision-making in a simulated clinical environment.

Source

The Digital Transformation of Healthcare Through Intelligent Technologies: A Path Dependence-Augmented–Unified Theory of Acceptance and Use of Technology Model for Clinical Decision Support Systems · Healthcare · 2025 · 10.3390/healthcare13111222