"Trust in healthcare AI is formed before the model runs. It's formed in the layperson's initial interaction with the technology, which can significantly influence their willingness to engage with it further."
"The JMIR 2025 study found that simply mentioning AI involvement in clinical care decreased patient trust and willingness to seek treatment, indicating a critical barrier to adoption."
"Lee and See's foundational research established that trust builds slowly from successes but collapses instantly from failures, underscoring the fragility of trust in healthcare AI."
"Despite improvements in AI models, including better training data and clearer clinical validation, the adoption needle has not moved as expected, revealing a deeper issue with trust."
Healthcare AI adoption struggles due to a trust gap that is not related to the technology itself but rather to initial user interactions. A physician abandoned an AI diagnostic tool shortly after opening it, highlighting the importance of first impressions. Research indicates that mentioning AI can decrease patient trust, and visible uncertainty in AI can lead to significant changes in clinician behavior. Despite advancements in AI models, adoption rates remain stagnant, emphasizing the need to address trust formation prior to model engagement.
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