In breast cancer diagnosis workflows, the way AI shares its results can change how accurate diagnoses are and how much healthcare workers trust AI. Two main styles exist: assertive (clear and direct) and suggestive (more gentle and open to interpretation). These styles affect clinicians differently based on their experience levels.
A study with 52 clinicians of different experience levels (interns, juniors, middle-level, and seniors) looked at how these communication types affect diagnosis. The results can help medical leaders in the U.S. improve how AI tools are used in clinics.
The study showed that when AI adjusts how it talks based on the clinician’s experience, accuracy and speed both improve:
This means AI communication should fit the user’s knowledge, not be the same for everyone. Matching AI style to experience helps provide good guidance and lets clinicians keep control.
Trust between clinicians and AI is very important when using new technology. The study found that clinicians liked assertive AI because it was clear and seemed knowledgeable:
Healthcare managers in the U.S. can use these findings to choose AI systems that change how they communicate depending on who is using them. This can help the systems be accepted and used well.
Breast cancer diagnosis requires careful look at complex images. This can be hard, especially for those still learning. In busy U.S. clinics, accuracy and speed in diagnosis mean better care and less delay.
Using AI with communication styles that fit the user helps clinics improve quality and control costs, which is important for healthcare leaders managing resources.
Using AI is not only about getting the right diagnosis but also making the workflow easier. Automating office and communication tasks must work well with AI’s diagnostic role for the best results.
For IT and hospital managers in the U.S., choosing AI that helps both clinical decisions and automates administrative work can add value. This is crucial as clinics face more patients and higher costs.
Researchers led by Francisco Maria Calisto studied how AI communication styles affect clinical results. Their study published in the International Journal of Human-Computer Studies provides clear data. It supports assertive AI for less experienced clinicians and suggestive AI for more experienced ones.
Medical leaders in the U.S. face challenges like staff shortages and the need to improve quality while using new technology well. Knowing about AI communication styles can help them make good decisions such as:
IT leaders should combine AI communication that fits clinicians with automation solutions like phone systems to improve both medical and office workflows.
Research shows that AI communication style affects how accurate diagnoses are and how much clinicians trust AI. Assertive AI helps less experienced clinicians reduce errors and work faster. Suggestive AI respects the judgment of experienced clinicians better. Adaptive communication lowers mental work and improves workflow efficiency.
By focusing on clear, personalized, and context-rich AI communication, U.S. healthcare can offer more reliable breast cancer diagnosis. This also helps reduce the pressure on clinicians and office staff. Such AI systems improve trust in technology and support safer, faster, and more effective breast cancer care.
Medical leaders, doctors, and IT teams should choose AI solutions that use these communication ideas and combine well with office automation to get the best patient results and clinic performance.
The research focuses on how personalized AI communication styles affect diagnostic performance, workload, and trust among clinicians during breast imaging diagnosis, emphasizing the adaptation of communication based on clinicians’ expertise levels.
Personalized AI communication reduces diagnostic time by a factor of 1.38 for interns and juniors, and by a factor of 1.37 for middle and senior clinicians, demonstrating significant efficiency improvements without compromising accuracy.
Interns and juniors reduce their diagnostic errors by 39.2% when interacting with a more authoritative AI agent, indicating that assertive communication enhances their decision-making and confidence.
Middle and senior clinicians achieved a 5.5% reduction in diagnostic errors when interacting with a more suggestive AI agent, showing preference for nuanced, less authoritative communication that respects their expertise.
Clinicians value assertiveness-based AI agents for their clarity and competence, appreciating detailed and contextual explanations over simple numerical outputs, which helps build trust and supports better clinical decisions.
AI systems should provide adaptable communication to match clinicians’ expertise, balance assertiveness and suggestiveness, reduce cognitive load, maintain accuracy, and build trust to effectively integrate into clinical workflows.
Personalized AI communication reduces cognitive load by tailoring explanations to the clinician’s experience level, making information processing more efficient and less mentally taxing during diagnosis.
This research advances understanding of AI-mediated clinical support by demonstrating the benefits of adaptable AI communication styles in improving trust, reducing workload, and enhancing diagnostic performance in healthcare.
The study engaged 52 clinicians across multiple expertise levels (interns, juniors, middles, seniors) who diagnosed breast imaging cases using conventional and assertiveness-based AI communication, measuring diagnostic time, errors, and preferences.
Yes, the data and code are publicly available on GitHub at https://github.com/MIMBCD-UI/sa-uta11-results, facilitating transparency and enabling further research in this domain.
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