Background Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction. Aims Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician–patient interaction. Method Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback. Results All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician–patient interaction. Conclusions The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician–patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.

The guidance cue receptor DCC controls mesocortical dopamine development in adolescence. Repeated exposure to an amphetamine regimen of 4 mg/kg during early adolescence induces, in male mice, downregulation of DCC expression in dopamine neurons by recruiting the Dcc microRNA repressor, microRNA‐218 (miR‐218). This adolescent amphetamine regimen also disrupts mesocortical dopamine connectivity and behavioral control in adulthood. Whether low doses of amphetamine in adolescence induce similar molecular and developmental effects needs to be established. Here, we quantified plasma amphetamine concentrations in early adolescent mice following a 4 or 0.5 mg/kg dose and found peak levels corresponding to those seen in humans following recreational and therapeutic settings, respectively. In contrast to the high doses, the low amphetamine regimen does not alter Dcc mRNA or miR‐218 expression; instead, it upregulates DCC protein levels. Furthermore, high, but not low, drug doses downregulate the expression of the DCC receptor ligand, Netrin‐1, in the nucleus accumbens and prefrontal cortex. Exposure to the low‐dose regimen did not alter the expanse of mesocortical dopamine axons or their number/density of presynaptic sites in adulthood. Strikingly, adolescent exposure to the low‐dose drug regimen does not impair behavioral inhibition in adulthood; instead, it induces an overall increase in performance in a go/no‐go task. These results show that developmental consequences of exposure to therapeutic‐ versus abused‐like doses of amphetamine in adolescence have dissimilar molecular signatures and opposite behavioral effects. These findings have important clinical relevance since amphetamines are widely used for therapeutic purposes in youth.

Background Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence–powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows. Objective This study aims to examine the feasibility of an artificial intelligence–powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network–based individualized treatment remission prediction. Methods Owing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Patients completed a minimum of one visit without the CDSS (baseline) and 2 subsequent visits where the CDSS was used by the physician (visits 1 and 2). The primary outcome of interest was change in appointment length after the introduction of the CDSS as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semistructured interviews. Results Data were collected between January and November 2020. A total of 17 patients were enrolled in the study; of the 17 patients, 14 (82%) completed the study. There was no significant difference in appointment length between visits (introduction of the tool did not increase appointment length; F2,24=0.805; mean squared error 58.08; P=.46). In total, 92% (12/13) of patients and 71% (5/7) of physicians felt that the tool was easy to use; 62% (8/13) of patients and 71% (5/7) of physicians rated that they trusted the CDSS. Of the 13 patients, 6 (46%) felt that the patient-clinician relationship significantly or somewhat improved, whereas 7 (54%) felt that it did not change. Conclusions Our findings confirm that the integration of the tool does not significantly increase appointment length and suggest that the CDSS is easy to use and may have positive effects on the patient-physician relationship for some patients. The CDSS is feasible and ready for effectiveness studies. Trial Registration ClinicalTrials.gov NCT04061642; http://clinicaltrials.gov/ct2/show/NCT04061642

No abstract