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Integrating AI as a teammate in modern clinical practice is transforming how doctors approach diagnosis and patient management. As healthcare systems increasingly adopt autonomous technologies, the way we label these systems significantly affects their success. Researchers have long debated whether to frame AI as a simple tool, a support system, or a full-fledged colleague. Consequently, this study explores how these labels influence professional acceptance through the lens of human psychology.
Acceptance of artificial intelligence depends heavily on "mind perception." This concept divides into two specific subdimensions: agency and conscious experience. Agency refers to the perceived ability of the AI to plan and execute actions. In contrast, conscious experience relates to the perceived capacity for feelings or awareness. Specifically, when clinicians view AI as a teammate, they tend to attribute higher levels of agency and consciousness to the technology. This shift in perception often dictates the level of trust a professional places in the system's recommendations.
The transition from viewing AI as a tool to viewing AI as a teammate offers several potential benefits. For instance, high agency perception correlates with stronger cognitive trust and higher acceptance rates in complex environments. However, forced labels carry hidden risks. If an organization labels a basic diagnostic tool as a "teammate" without providing the necessary collaborative capabilities, it can backfire. Consequently, clinicians may experience frustration or a loss of trust if the AI fails to meet the social expectations associated with the teammate role.
Furthermore, intermediate labels like "support" may currently be more appropriate for many clinical systems. This allows for a more realistic alignment between user expectations and technological reality. Therefore, healthcare administrators must choose these labels strategically to ensure long-term adoption and team cohesion.
In conclusion, the successful integration of artificial intelligence in India's healthcare landscape requires a deep understanding of human-AI dynamics. Shifting roles to emphasize collaboration can enhance acceptance, but accuracy in labeling is vital. Ultimately, hospitals should prioritize strategic role alignment to maximize the benefits of human-AI teaming.
Labeling AI as a teammate increases the user's perception of the system's agency. This perception typically boosts cognitive trust, making clinicians more willing to rely on AI for complex decision-making tasks.
Agency is the perceived ability to act and achieve goals, while conscious experience is the perceived ability to feel. AI systems are often perceived as having high agency but low conscious experience, which affects how humans form emotional bonds with the technology.
Yes. If a system is labeled as a teammate but lacks the necessary collaborative features, it can lead to unmet expectations. This mismatch often results in lower acceptance and decreased trust among healthcare professionals.
Disclaimer: This content is for informational and educational purposes only and does not constitute medical advice. The views expressed are based on recent research and should not replace professional judgment. Refer to the latest local and national guidelines for clinical practice.
References
Harris-Watson AM et al. What's in a Name? Implications of AI Roles and Mind Perception for Human-AI Teams. Hum Factors. 2026 Jun 12. doi: 10.1177/00187208261457702. PMID: 42283141.
Glikson E, Woolley AW. Human Trust in Artificial Intelligence: Review of Empirical Research. Acad Manag Ann. 2020;14(2):627-660.
Asan O, Bayrak AE, Choudhury A. Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians. J Med Internet Res. 2020;22(6):e15154.
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Research examines how AI roles like 'teammate' or 'tool' influence user acceptance through mind perception. While labeling AI as a teammate can boost trust, forced labels may create negative outcomes. This study highlights the need for strategic integration of AI in clinical workflows.
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