Breast cancer diagnosis requires looking closely at images like mammograms and ultrasounds. Getting the diagnosis right and fast is very important for early treatment. Early treatment can help patients get better results. Doctors and medical staff have a lot of work and need to be very careful when checking these images. If they can do this faster without making mistakes, it helps reduce their workload, lets more patients be seen, and improves care.
Recent studies have looked at how AI can help doctors make decisions based on their experience level. Personalized AI explanations, which change how the AI talks depending on who is using it, have shown they can make diagnosis faster and with fewer mistakes.
A study with 52 clinicians, from beginners to seniors, tested how personalized AI communication affects diagnosing breast cancer from images. The clinicians were split into four groups: interns, juniors, middle-level, and senior clinicians. They compared the usual AI communication with new AI methods that changed how strongly the AI explained things.
By changing the AI’s explanations based on experience, the system helped doctors work faster and better. Younger doctors liked clear and firm advice, which helped them feel sure and make decisions faster. Experienced doctors liked softer suggestions that respected their knowledge but still gave useful help.
Making the right diagnosis is very important for breast cancer. Wrong results can delay treatment or cause unneeded procedures. This study showed personalized AI also helped reduce mistakes:
This difference shows how important it is to match AI communication style to how experienced the doctor is. Less experienced doctors need clear and strong guidance to reduce doubt. Experienced doctors want AI that supports their knowledge and lets them think while using AI advice.
Doctors said they liked AI agents that used clear and confident explanations. They preferred detailed explanations instead of just numbers or data.
The study also showed that personalized AI helps lower the mental effort for doctors. Looking at many complex images can cause tiredness, especially on long work shifts. Adaptive AI helps by:
Lower mental effort helps doctors keep performing well all day. Trust is also very important. Doctors prefer AI that explains clearly and shows it knows what it’s doing. AI that uses firm but clear explanations helps doctors trust it more because it shows the reasons behind its suggestions during patient exams.
Managers and IT staff in medical practices in the U.S. must balance good patient care with smooth operations. Using personalized AI tools for breast cancer imaging can help by:
Managers and IT staff should work together to pick AI solutions that adjust communication styles for different clinicians. To succeed, they must understand how AI fits into daily work, train staff to use it, and keep checking if it helps and is accepted.
Besides diagnosing, AI also helps with healthcare office work. For example, AI can manage phone calls and patient questions in medical offices in the U.S., lowering the workload on staff.
Using AI in office tasks works well with clinical AI tools for diagnosis. Together, they help health workers use their time better and focus on patient care.
Breast cancer is one of the most common cancers among women in the U.S. Early detection through good imaging greatly improves survival rates. Hospitals and clinics always try to find new ways to shorten the time from screening to diagnosis.
Using personalized AI communication in breast imaging supports the national goal of better cancer care. Making diagnosis faster and reducing doctor workload and mistakes helps start treatment sooner and lowers patient stress from slow results.
In short, using AI communication that matches the doctor’s experience during breast cancer imaging leads to faster work and fewer mistakes. Interns and juniors benefit the most from firm AI help, lowering mistakes by 39.2% and making decisions quicker. Middle and senior doctors get smaller but still important improvements with softer AI advice, reducing mistakes by 5.5%.
For healthcare managers and IT staff, these results show why it is important to pick AI systems that change how they talk based on who uses them. Also, adding AI for office tasks helps the healthcare team focus more on patients.
Personalized AI communication helps doctors be more accurate, builds trust, and lowers mental tiredness, which is important to keep healthcare good in busy medical settings in the U.S.
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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