Primary care physicians' perceptions of artificial intelligence systems in the care of adolescents' mental health.

BACKGROUND: Given that mental health problems in adolescence may have lifelong impacts, the role of primary care physicians (PCPs) in identifying and managing these issues is important. Artificial Intelligence (AI) may offer solutions to the current challenges involved in mental health care. We therefore explored PCPs' challenges in addressing adolescents' mental health, along with their attitudes towards using AI to assist them in their tasks. METHODS: We used purposeful sampling to recruit PCPs for a virtual Focus Group (FG). The virtual FG lasted 75 minutes and was moderated by two facilitators. A life transcription was produced by an online meeting software. Transcribed data was cleaned, followed by a priori and inductive coding and thematic analysis. RESULTS: We reached out to 35 potential participants via email. Seven agreed to participate, and ultimately four took part in the FG. PCPs perceived that AI systems have the potential to be cost-effective, credible, and useful in collecting large amounts of patients' data, and relatively credible. They envisioned AI assisting with tasks such as diagnoses and establishing treatment plans. However, they feared that reliance on AI might result in a loss of clinical competency. PCPs wanted AI systems to be user-friendly, and they were willing to assist in achieving this goal if it was within their scope of practice and they were compensated for their contribution. They stressed a need for regulatory bodies to deal with medicolegal and ethical aspects of AI and clear guidelines to reduce or eliminate the potential of patient harm. CONCLUSION: This study provides the groundwork for assessing PCPs' perceptions of AI systems' features and characteristics, potential applications, possible negative aspects, and requirements for using them. A future study of adolescents' perspectives on integrating AI into mental healthcare might contribute a fuller understanding of the potential of AI for this population.

Application of Artificial Intelligence in Shared Decision Making: Scoping Review.

BACKGROUND: Artificial intelligence (AI) has shown promising results in various fields of medicine. It has the potential to facilitate shared decision making (SDM). However, there is no comprehensive mapping of how AI may be used for SDM. OBJECTIVE: We aimed to identify and evaluate published studies that have tested or implemented AI to facilitate SDM. METHODS: We performed a scoping review informed by the methodological framework proposed by Levac et al, modifications to the original Arksey and O'Malley framework of a scoping review, and the Joanna Briggs Institute scoping review framework. We reported our results based on the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) reporting guideline. At the identification stage, an information specialist performed a comprehensive search of 6 electronic databases from their inception to May 2021. The inclusion criteria were: all populations; all AI interventions that were used to facilitate SDM, and if the AI intervention was not used for the decision-making point in SDM, it was excluded; any outcome related to patients, health care providers, or health care systems; studies in any health care setting, only studies published in the English language, and all study types. Overall, 2 reviewers independently performed the study selection process and extracted data. Any disagreements were resolved by a third reviewer. A descriptive analysis was performed. RESULTS: The search process yielded 1445 records. After removing duplicates, 894 documents were screened, and 6 peer-reviewed publications met our inclusion criteria. Overall, 2 of them were conducted in North America, 2 in Europe, 1 in Australia, and 1 in Asia. Most articles were published after 2017. Overall, 3 articles focused on primary care, and 3 articles focused on secondary care. All studies used machine learning methods. Moreover, 3 articles included health care providers in the validation stage of the AI intervention, and 1 article included both health care providers and patients in clinical validation, but none of the articles included health care providers or patients in the design and development of the AI intervention. All used AI to support SDM by providing clinical recommendations or predictions. CONCLUSIONS: Evidence of the use of AI in SDM is in its infancy. We found AI supporting SDM in similar ways across the included articles. We observed a lack of emphasis on patients' values and preferences, as well as poor reporting of AI interventions, resulting in a lack of clarity about different aspects. Little effort was made to address the topics of explainability of AI interventions and to include end-users in the design and development of the interventions. Further efforts are required to strengthen and standardize the use of AI in different steps of SDM and to evaluate its impact on various decisions, populations, and settings.

Antimicrobial activity of seven essential oils from Iranian aromatic plants against common causes of oral infections.

BACKGROUND: Over the past two decades, there has been a growing trend in using oral hygienic products originating from natural resources such as essential oils (EOs) and plant extracts. Seven aromatic plants used in this study are among popular traditional Iranian medicinal plants with potential application in modern medicine as anti-oral infectious diseases. OBJECTIVES: This study was conducted to determine the chemical composition and antimicrobial activities of essential oils from seven medicinal plants against pathogens causing oral infections. MATERIALS AND METHODS: The chemical compositions of EOs distilled from seven plants were analyzed by gas chromatography/mass spectrometry (GC/MS). These plants included Satureja khuzestanica, S. bachtiarica, Ocimum sanctum, Artemisia sieberi, Zataria multiflora, Carum copticum and Oliveria decumbens. The antimicrobial activity of the essential oils was evaluated by broth micro-dilution in 96 well plates as recommended by the Clinical and Laboratory Standards Institute (CLSI) methods. RESULTS: The tested EOs inhibited the growth of examined oral pathogens at concentrations of 0.015-16 µL/mL. Among the examined oral pathogens, Enterococcus faecalis had the highest Minimum Inhibitory Concentrations (MICs) and Minimum Microbicidal Concentrations (MMCs). Of the examined EOs, S. khuzestanica, Z. multiflora and S. bachtiarica, showed the highest antimicrobial activities, respectively, while Artemisia sieberi exhibited the lowest antimicrobial activity. CONCLUSIONS: The excellent antimicrobial activities of the tested EOs might be due to their major phenolic or alcoholic monoterpenes with known antimicrobial activities. Hence, these EOs can be possibly used as an antimicrobial agent in treatment and control of oral pathogens.