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  Indian J Med Microbiol
 

Figure 1: Our pipeline begins with pathologist recruitment (A). If a pathologist consents to having their images used (B), we download those images (C) and manually annotate them (D). Next, we train a Random Forest classifier to predict image characteristics, e.g., disease state (E). This classifier is used to predict disease and search. If a pathologist posts a case to social media and mentions @pathobot (F), our bot will use the post's text and images to find similar cases on social media and PubMed (G). The bot then posts summaries and notifies pathologists with similar cases (H). Pathologists discuss the results (I), and some also decide to share their cases, initiating the cycle again (A)

Figure 1: Our pipeline begins with pathologist recruitment (A). If a pathologist consents to having their images used (B), we download those images (C) and manually annotate them (D). Next, we train a Random Forest classifier to predict image characteristics, e.g., disease state (E). This classifier is used to predict disease and search. If a pathologist posts a case to social media and mentions @pathobot (F), our bot will use the post's text and images to find similar cases on social media and PubMed (G). The bot then posts summaries and notifies pathologists with similar cases (H). Pathologists discuss the results (I), and some also decide to share their cases, initiating the cycle again (A)