The aim of this review was to explore the current evidence for conversational agents or chatbots in the field of psychiatry and their role in screening, diagnosis, and treatment of mental illnesses. Methods: A systematic literature search in June 2018 was conducted in PubMed, EmBase, PsycINFO, Cochrane, Web of Science, and IEEE Xplore. Studies were included that involved a chatbot in a mental health setting focusing on populations with or at high risk of developing depression, anxiety, schizophrenia, bipolar, and substance abuse disorders. Results: From the selected databases, 1466 records were retrieved and 8 studies met the inclusion criteria. Two additional studies were included from reference list screening for a total of 10 included studies. Overall, potential for conversational agents in psychiatric use was reported to be high across all studies. In particular, conversational agents showed potential for benefit in psychoeducation and self-adherence. In addition, satisfaction rating of chatbots was high across all studies, suggesting that they would be an effective and enjoyable tool in psychiatric treatment. Conclusion: Preliminary evidence for psychiatric use of chatbots is favourable. However, given the heterogeneity of the reviewed studies, further research with standardized outcomes reporting is required to more thoroughly examine the effectiveness of conversational agents. Regardless, early evidence shows that with the proper approach and research, the mental health field could use conversational agents in psychiatric treatment. Abré gé Objectif : Cette revue visait à explorer les données probantes actuelles sur les agents conversationnels ou les « chatbots » (robots parleurs) dans le domaine de la psychiatrie et le rô le que jouent ceux-ci dans le dépistage, le diagnostic, et le traitement des maladies mentales. Mé thode : Une recherche systématique de la littérature a été menée en juin 2018 dans PubMed, EmBase, PsycINFO, Cochrane, Web of Science, et IEEE Xplore. Les études incluses portaient sur un « chatbot » dans un milieu de santé mentale axé sur les populations souffrant de dépression, d'anxiété, de schizophrénie, du trouble bipolaire et des troubles d'abus de substances ou qui étaient à risque élevé de développer un de ces troubles. Ré sultats : Dans les bases de données choisies, 1466 dossiers ont été extraits et 8 études satisfaisaient aux critères d'inclusion. Deux études additionnelles ont été ajoutées après une sélection dans la liste de références, pour un total de 10 études incluses. En général, le potentiel de l'utilisation d'agents conversationnels en psychiatrie était estimé élevé dans toutes

As the potential of smartphone apps and sensors for healthcare and clinical research continues to expand, there is a concomitant need for open, accessible, and scalable digital tools. While many current app platforms offer useful solutions for either clinicians or patients, fewer seek to serve both and support the therapeutic relationship between them. Thus, we aimed to create a novel smartphone platform at the intersection of patient demands for trust, control, and community and clinician demands for transparent, data driven, and translational tools. The resulting LAMP platform has evolved through numerous iterations and with much feedback from patients, designers, sociologists, advocates, clinicians, researchers, app developers, and philanthropists. As an open and free tool, the LAMP platform continues to evolve as reflected in its current diverse use cases across research and clinical care in psychiatry, neurology, anesthesia, and psychology. In this paper, we explore the motivation, features, current progress, and next steps to pair the platform for use in a new digital psychiatry clinic, to advance digital interventions for youth mental health, and to bridge gaps in available mental health care for underserved patient groups. The code for the LAMP platform is freely shared with this paper to encourage others to adapt and improve on our team's efforts.

ObjectiveThis study aimed to understand the attributes of popular apps for mental health and comorbid medical conditions, and how these qualities relate to consumer ratings, app quality and classification by the WHO health app classification framework.MethodsWe selected the 10 apps from the Apple iTunes store and the US Android Google Play store on 20 July 2018 from six disease states: depression, anxiety, schizophrenia, addiction, diabetes and hypertension. Each app was downloaded by two authors who provided information on the apps’ attributes, functionality, interventions, popularity, scientific backing and WHO app classification rating.ResultsA total of 120 apps were examined. Although none of these apps had Food and Drug Administration marketing approval, nearly 50% made claims that appeared medical. Most apps offered a similar type of services with 87.5% assigned WHO classification 1.4.2 ‘self-monitoring of health or diagnostic data by a client’ or 1.6.1 ‘client look-up of health information’. The ‘last updated’ attribute was highly correlated with a quality rating of the app although no apps features (eg, uses Global Positioning System, reminders and so on) were.ConclusionDue to the heterogeneity of the apps, we were unable to define a core set of features that would accurately assess app quality. The number of apps making unsupported claims combined with the number of apps offering questionable content warrants a cautious approach by both patients and clinicians in selecting safe and effective ones.Clinical Implications‘Days since last updated’ offers a useful and easy clinical screening test for health apps, regardless of the condition being examined.