A scoping review of AI, speech and natural language processing methods for assessment of clinician-patient communication

IntroductionThere is growing research interest in applying Artificial Intelligence (AI) methods to medicine and healthcare. Analysis of communication in healthcare has become a target for AI research, particularly in the field of analysis of medical consultations, an area that so far has been dominated by manual rating using measures. This opens new perspectives for automation and large scale appraisal of clinicians communication skills. In this scoping review we summarised existing methods and systems for the assessment of patient doctor communication in consultations. MethodsWe searched EMBASE, MEDLINE/PubMed, the Cochrane Central Register of Controlled Trials, and the ACM digital library for papers describing methods or systems that employ artificial intelligence or speech and natural language processing (NLP) techniques with a view to automating the assessment of patient-clinician communication, in full or in part. The search covered three main concepts: dyadic communication, clinician-patient interaction, and systematic assessment. ResultsWe found that while much work has been done which employs AI and machine learning methods in the analysis of patient-clinician communication in medical encounters, this evolving research field is uneven and presents significant challenges to researchers, developers and prospective users. Most of the studies reviewed focused on linguistic analysis of transcribed consultations. Research on non-verbal aspects of these encounters are fewer, and often hindered by lack of methodological standardisation. This is true especially of studies that investigate the effects of acoustic (paralinguistic) features of speech in communication but also affects studies of visual aspects of interaction (gestures, facial expressions, gaze, etc). We also found that most studies employed small data sets, often consisting of interactions with simulated patients (actors). ConclusionsWhile our results point to promising opportunities for the use of AI, more work is needed for collecting larger, standardised, and more easily available data sets, as well as on better documentation and sharing of methods, protocols and code to improve reproducibility of research in this area.