Differences in Exhibited Emotions between Junior Residents and Senior Doctors: Using an Artificial Intelligence-Based...

Area of Science:

• Medical Education Technology
• Artificial Intelligence in Healthcare
• Clinical Communication Analysis

Background:

• Current medical interviewing training relies on subjective methods.
• Facial expressions are linked to patient outcomes but understudied in medical education.
• Need for objective tools to analyze physician-patient interactions.

Purpose of the Study:

• To investigate differences in facial expressions between senior physicians and junior residents during medical interviews.
• To evaluate the utility of an AI-based facial expression analysis tool in medical education.

Main Methods:

• Recruited healthcare professionals from Dokkyo Medical University Saitama Medical Center (Nov 2017-Oct 2018).
• Utilized 'Kokoro Sensor,' an AI tool analyzing facial expressions into seven emotions.
• Compared interview duration and AI-analyzed facial emotion proportions between junior residents and senior physicians.

Main Results:

• Senior physicians had significantly shorter interview times (11.79 min) than junior residents (18.95 min).
• Physicians' faces were recognized by AI for longer durations by senior physicians (21.6%) vs. junior residents (9.4%).
• Senior physicians displayed significantly more 'surprise' expressions (15.9%) compared to junior residents (4.9%).

Conclusions:

• Significant differences exist in facial expressions and engagement between junior and senior physicians during interviews.
• AI-based facial expression analysis provides objective data for medical training.
• This technology can enhance the education of medical students and residents in patient communication.