What makes a good image? Exploring patients' physician selection behavior leveraging large language models and scenario experiments

As important information cues for patients' selection, physicians' online profile images have received limited attention. We explore the effects of visual cues—image feature (image clarity) and image contents (smile intensity and medical professionalism) on patients' selection behavior, while also examining the moderating effect of consultation price. Leveraging large language models, we annotate visual cues to facilitate empirical analysis. This analysis demonstrates that image clarity, smile intensity, and medical professionalism positively affect patients' selection behavior, with consultation price amplifying the effect of image clarity. We further conduct scenario-based experiments to examine the underlying mechanism from perspectives of information foraging and perceived diagnosticity. This study enriches theoretical insights into patients' selection behavior by mining physicians' image information. It also advances the empirical methodological paradigm by integrating the large language model with empirical analysis. Our findings help physicians and platform managers strategically optimize profile images and consultation prices to improve physicians' popularity in online health market.