In hospitals, AI tools help doctors understand complex medical data like imaging scans.
But the way AI shares its results can change how doctors make decisions.
There are two main styles of AI communication:
A study with 52 clinicians working in breast imaging looked at how these two AI styles affected diagnosis.
The study found that junior and senior doctors reacted differently to assertive and suggestive AI styles.
This research shows that matching AI communication to the doctor’s experience can improve accuracy and speed.
Medical administrators and IT staff should think about AI communication styles when adding AI to healthcare.
The study found that doctors like AI that is clear and shows it understands the situation.
Doctors want explanations that tell them why, not just numbers.
Trust in AI grows when the AI’s advice matches the doctor’s experience.
Junior doctors need clear, strong guidance to feel confident and accurate.
Senior doctors prefer detailed but gentle suggestions that respect their skills.
Trust is very important for doctors to use AI well.
Without trust, AI might not be used enough or might be used wrongly.
Doctors often feel tired and stressed from making many decisions.
Using AI communication that fits their experience can make their job easier.
Less mental strain improves accuracy and reduces stress.
This also helps hospitals keep their staff happy and working well.
How AI talks to doctors matters for running hospitals smoothly.
Combining clinical AI with front-office automation, like phone answering systems, can help a lot.
IT managers and medical leaders should pick AI tools that adjust to doctor experience and help both clinical and office work.
When U.S. medical groups add AI to their work, they should think about these points:
The breast cancer imaging study with 52 clinicians represents many U.S. healthcare workers.
Improving diagnosis speed and accuracy matters a lot in busy U.S. clinics.
Knowing that junior and senior doctors have different needs helps hospitals pick better AI systems.
Also, automating simple tasks like phone answering helps doctors focus on patients.
Using AI in both clinical support and office work creates a balanced system for hospitals.
Studies show that AI communication style affects doctors’ decisions differently depending on their experience.
Junior doctors do better with assertive AI, cutting errors by 39.2% and working faster.
Senior doctors do better with suggestive AI, lowering errors by 5.5%.
Personalizing AI communication builds trust, reduces mental effort, and improves diagnosis speed without losing accuracy.
Medical managers in the U.S. should use AI tools that change how they communicate based on the doctor using them.
Combining clinical AI with office automation, like phone answering systems, helps clinics work better.
Understanding and using AI that suits doctors’ needs will help improve healthcare in the future.
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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