Understanding Patient Reactions to AI and Clinician Discrepancies
As the use of artificial intelligence (AI) in medical imaging grows, radiologists are starting to use AI to help detect lung cancer earlier. However, when there is a disagreement between what the AI and the radiologist recommend, it can make patients doubt their radiologist’s expertise.
Key Findings from the Study
A study with 1,606 participants looked at how these discrepancies affect patients’ trust and satisfaction with their radiologists. Here are the main points:
- Patients were less likely to agree with a radiologist’s recommendation if the radiologist suggested less testing than the AI.
- Patients were also less likely to recommend that radiologist to others and rated them lower overall.
- Patients who prefer aggressive treatments (maximizers) agreed more with radiologists who recommended more testing than AI.
- Patients who prefer less intervention (minimizers) did not significantly disagree with radiologists who recommended less testing than AI.
Practical Solutions for Clinics
1. Define Measurable Outcomes
Set clear goals for understanding how patients react to AI and clinician recommendations. This will help improve patient trust and satisfaction.
2. Select AI Tools That Fit Clinical Needs
Choose AI solutions that are specifically designed for tasks relevant to your practice, ensuring they meet the needs of both clinicians and patients.
3. Implement Step by Step and Expand
Start with a pilot project using AI tools, track the results, and assess their real-world impact on patient reactions and satisfaction.
Contact Us for More Information
If you’re interested in AI solutions for medical management, reach out to us:
- Telegram: https://t.me/itinai
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- LinkedIn: https://www.linkedin.com/company/itinai/
