ROLE OF AI-BASED VIRTUAL ASSISTANTS IN ORAL AND MAXILLOFACIAL RADIOLOGY REPORT WRITING

BACKGROUNDArtificial Intelligence (AI) encompasses an array of devices, solutions, and codes ranging from a simple calculator to complex machine learning devices. A simple and successful AI system that is being routinely used is a voice-activated virtual assistant. There are several such devices in the market, including Alexa, Siri, Cortana, and Google Assistant. These devices offer a very simple voice-activated front end powered by a database at the back end. There are several instances where radiologists refer to a textbook or visit a website to look for disease-specific features, genetic mutations, and differential diagnoses.OBJECTIVE(S)The objective of this study was to evaluate the role and status of 4 voice-based virtual assistants in responding to radiology related queries.STUDY DESIGNA questionnaire consisting of 100 questions was queried to 4 commercially available voice-based virtual assistants: Alexa, Siri, Cortana, and Google Assistant. The questions were categorized based on 5 categories. The categorization was based on the frequency and reason for a radiologist to refer either to a textbook or an online resource before diagnosing and finalizing a radiology report. The evaluators queried the devices and rated them on a 4-point modified Likert scale: (1) The device responded with adequate information, (2) The device responded but did not provide adequate information, (3) The device did not know the response to the question, and (4) The device recognized but did not provide adequate information and led to a series of web pages.RESULTSThe order of efficiency was Google Assistant, Cortana, Siri, and Alexa. A significant difference between the examiners was observed with Cortana in anatomy, dental anatomy, differential diagnosis, and pathology. There was a significant difference between the examiners with Google Assistant for queries about medication.DISCUSSION/CONCLUSIONSThere is a significant gap in retrieving simple to multilevel queries on oral radiology topics for all of the virtual assistants evaluated in this study. Artificial Intelligence (AI) encompasses an array of devices, solutions, and codes ranging from a simple calculator to complex machine learning devices. A simple and successful AI system that is being routinely used is a voice-activated virtual assistant. There are several such devices in the market, including Alexa, Siri, Cortana, and Google Assistant. These devices offer a very simple voice-activated front end powered by a database at the back end. There are several instances where radiologists refer to a textbook or visit a website to look for disease-specific features, genetic mutations, and differential diagnoses. The objective of this study was to evaluate the role and status of 4 voice-based virtual assistants in responding to radiology related queries. A questionnaire consisting of 100 questions was queried to 4 commercially available voice-based virtual assistants: Alexa, Siri, Cortana, and Google Assistant. The questions were categorized based on 5 categories. The categorization was based on the frequency and reason for a radiologist to refer either to a textbook or an online resource before diagnosing and finalizing a radiology report. The evaluators queried the devices and rated them on a 4-point modified Likert scale: (1) The device responded with adequate information, (2) The device responded but did not provide adequate information, (3) The device did not know the response to the question, and (4) The device recognized but did not provide adequate information and led to a series of web pages. The order of efficiency was Google Assistant, Cortana, Siri, and Alexa. A significant difference between the examiners was observed with Cortana in anatomy, dental anatomy, differential diagnosis, and pathology. There was a significant difference between the examiners with Google Assistant for queries about medication. There is a significant gap in retrieving simple to multilevel queries on oral radiology topics for all of the virtual assistants evaluated in this study.