Artificial intelligence (AI) is becoming more common in healthcare, especially in imaging and diagnosis. Breast cancer imaging is important for timely and accurate diagnosis. Using AI tools in this field has helped improve results. A recent study looked at clinicians with different experience levels—from interns to senior specialists—and found that AI communication styles that fit the clinician’s level can affect how accurate and fast diagnoses are. For medical practice administrators, owners, and IT managers in the United States, knowing about these findings can help improve clinical work, reduce mistakes, and improve patient care in breast cancer treatment.
The way clinicians and AI systems talk to each other is important for diagnosis. Like any type of communication, how AI shares information affects what doctors do, how sure they feel, and how much work they have. A recent study by Francisco Maria Calisto and others involved 52 clinicians divided by experience into interns, juniors, middle-level, and seniors. They studied how AI communication styles, especially the level of assertiveness, change performance in breast cancer imaging diagnosis.
The study showed that AI communication should not be the same for everyone. Changing explanations to fit the clinician’s experience helps make diagnoses faster and better. For example, interns and juniors, who often need more help, did better with more direct AI styles. This direct style cut their errors by 39.2% and made diagnoses faster by a factor of 1.38. Middle and senior doctors liked a softer, more suggestive style that respected their experience. This led to 5.5% fewer errors and diagnosis times also faster by a factor of 1.37.
These findings show that matching AI communication to experience level is important. It’s not just about how accurate AI is but also how it explains its advice. Clinicians said that AI agents with assertive styles were clearer and seemed more capable because they gave detailed explanations instead of just numbers or data. This helped reduce mental strain, which is very important in tough situations like breast cancer diagnosis where clear information and confidence affect decisions.
Reducing diagnosis time by more than 1.3 times for all types of clinicians can impact healthcare in the US. Many clinics face challenges with many patients, more cases, and not enough specialists. Using personalized AI communication in breast imaging helps clinics speed up diagnosis without losing accuracy.
Cutting errors by over 39% for less experienced clinicians helps start treatment earlier, reduces patient worry, and makes better use of resources. Medical administrators in charge of breast imaging or cancer departments can improve care quality and patient satisfaction by using AI that fits clinician experience. It helps junior staff feel more confident and work faster. Senior doctors get support from AI that matches their level of skill.
Across the US, where breast cancer is a common diagnosis, adaptive AI improves clinical workflow. This can lower repeat imaging, unneeded biopsies, and wrong diagnosis delays that can harm patients. IT managers should use AI systems that communicate in flexible ways. They should work closely with clinical teams to learn their needs and use data to adjust AI settings.
Since breast cancer diagnosis is so important, AI systems must do more than just recognize patterns accurately. They should use communication styles made for different users. Francisco Maria Calisto’s study showed some important design points:
Medical administrators should pick AI systems that can be adjusted and get feedback from clinics and clinicians. This helps fine-tune communication styles to fit different needs.
Automation in healthcare helps reduce delays and admin work. In breast cancer imaging, AI tools can pre-check mammograms and make suggestions. But just having automation is not enough; how AI shares findings with clinicians greatly affects workflow speed and quality.
The study showed that AI that changes its style—being direct with less experienced clinicians and softer with more experienced ones—can cut diagnosis time by over a third. This saves time and lets clinics see more patients without lowering quality.
Using personalized AI also lowers mental load on clinicians, which helps prevent mistakes from tiredness. This support lets doctors focus more on tough diagnosis and talking with patients, improving care.
IT departments can use these results to set up AI systems that work smoothly with electronic health records (EHR) and imaging storage systems (PACS). When AI explains things clearly during image reviews, clinicians decide faster and more confidently, cutting down on repeat tests and wrong referrals. Personalized AI also fits with plans focusing on better care at lower costs.
Practice owners get benefits by using workflow automation with adaptive AI. Less experienced clinicians can safely help more if AI gives proper guidance. This is useful, especially in places where senior radiologists are few, like rural clinics.
Administrators should pick AI tools that let users customize communication and show clear logic behind AI advice. Training is also needed so staff learn how to understand AI outputs and adjust settings.
AI is not just a tool; it is a partner in clinical decisions. How people interact with AI affects how useful it is in breast cancer diagnosis. The study involved 52 clinicians from interns to seniors and gave insights on how users like to work with AI agents.
Clinicians liked AI agents that clearly gave recommendations with confidence and details. They preferred explanations that went beyond just numbers and shared reasons or context. This shows how important it is for AI to be transparent and explainable. Understanding how AI works helps clinicians trust it more.
For IT managers, this means using AI with explainable AI (XAI) features. These let users see why AI made a suggestion, which increases trust and satisfaction.
Principles of human-computer interaction are very important when adding AI to clinical work. How information is shown matters as much as how good the AI is at analyzing data. Well-designed AI communication lowers frustration, makes work easier, and supports better patient care.
Knowing how personalized AI communication affects clinical work can help healthcare leaders make better choices about technology and workflow design:
Using AI in breast cancer imaging works better when AI changes how it communicates to match clinician experience. Medical administrators, practice owners, and IT managers in the US can improve clinical work by picking AI that adapts communication and assertiveness styles. Less experienced clinicians gain from direct AI advice, while experienced doctors prefer a softer style.
These findings, based on a study of 52 clinicians, show AI can make work faster, reduce errors, and fit better into workflows without losing accuracy. Combining human skill with good AI communication sets a new standard for breast cancer diagnosis in US healthcare.
Adding personalized AI communication to automation helps healthcare centers give better care while managing higher imaging demands. As AI technology grows, it will be important to keep checking and improving how AI and humans interact to make sure these tools meet clinical needs and improve results.
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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