In the past, researchers have used various data sources like social media, admission applications, or letters of recommendation to identify gender-based differences in linguistic data. One such avenue in healthcare is the online physician reviews website. Such websites, for example, RateMyDoctors.com, ZocDoc, or even Yelp.com have become a go-to place for patients when choosing their physicians. In the current research, we used two different natural language processing (NLP) approaches: semi-supervised and unsupervised topic modeling to analyze the text of the reviews to identify gender-based linguistic differences from patients' perspectives. We found that female physicians receive more reviews on their personable skills and warmth, aligning with the Stereotype Content Model. We also found other popular topics discussing bedside manners and overall patient experiences, where the reviews suggested that patients were happier with their experience with female physicians and perceived them to have more positive traits than their male counterparts. Although our study did not reflect significant linguistic differences; it highlights the importance for patients and doctors to be more aware of potential gender stereotypes and perceptions.