Pragmatic AI-augmentation in mental healthcare: Key technologies, potential benefits, and real-world challenges and solutions for frontline clinicians

The integration of artificial intelligence (AI) technologies into mental health holds the promise of increasing patient access, engagement, and quality of care, and of improving clinician quality of work life. However, to date, studies of AI technologies in mental health have focused primarily on challenges that policymakers, clinical leaders, and data and computer scientists face, rather than on challenges that frontline mental health clinicians are likely to face as they attempt to integrate AI-based technologies into their everyday clinical practice. In this Perspective, we describe a framework for "pragmatic AI-augmentation" that addresses these issues by describing three categories of emerging AI-based mental health technologies which frontline clinicians can leverage in their clinical practice-automation, engagement, and clinical decision support technologies. We elaborate the potential benefits offered by these technologies, the likely day-to-day challenges they may raise for mental health clinicians, and some solutions that clinical leaders and technology developers can use to address these challenges, based on emerging experience with the integration of AI technologies into clinician daily practice in other healthcare disciplines.

Current monitoring and innovative predictive modeling to improve care in the pediatric cardiac intensive care unit

The objectives of this review are (I) to describe the challenges associated with monitoring patients in the pediatric cardiac intensive care unit (PCICU) and (II) to discuss the use of innovative statistical and artificial intelligence (AI) software programs to attempt to predict significant clinical events. Patients cared for in the PCICU are clinically fragile and at risk for fatal decompensation. Current monitoring modalities are often ineffective, sometimes inaccurate, and fail to detect a deteriorating clinical status in a timely manner. Predictive models created by AI and machine learning may lead to earlier detection of patients at risk for clinical decompensation and thereby improve care for critically ill pediatric cardiac patients.