Conotruncal Heart Defects: A Narrative Review of Molecular Genetics, Genomics Research and Innovation.

Congenital heart defects (CHDs) are most prevalent cardiac defects that occur at birth, leading to significant neonatal mortality and morbidity, especially in the developing nations. Among the CHDs, conotruncal heart defects (CTDs) are particularly noteworthy, comprising a significant portion of congenital cardiac anomalies. While advances in imaging and surgical techniques have improved the diagnosis, prognosis, and management of CTDs, their molecular genetics and genomic substrates remain incompletely understood. This expert review covers the recent advances from January 2016 onward and examines the complexities surrounding the genetic etiologies, prevalence, embryology, diagnosis, and clinical management of CTDs. We also emphasize the known copy number variants and single nucleotide variants associated with CTDs, along with the current planetary health research efforts aimed at CTDs in large cohort studies. In all, this comprehensive narrative review of molecular genetics and genomics research and innovation on CTDs draws from and highlights selected works from around the world and offers new ideas for advances in CTD diagnosis, precision medicine interventions, and accurate assessment of prognosis and recurrence risks.

Correction: Using Conversational AI to Facilitate Mental Health Assessments and Improve Clinical Efficiency Within Psychotherapy Services: Real-World Observational Study.

[This corrects the article DOI: 10.2196/44358.].

Closing the accessibility gap to mental health treatment with a personalized self-referral chatbot.

Inequality in treatment access is a pressing issue in most healthcare systems across many medical disciplines. In mental healthcare, reduced treatment access for minorities is ubiquitous but remedies are sparse. Here we demonstrate that digital tools can reduce the accessibility gap by addressing several key barriers. In a multisite observational study of 129,400 patients within England's NHS services, we evaluated the impact of a personalized artificial intelligence-enabled self-referral chatbot on patient referral volume and diversity in ethnicity, gender and sexual orientation. We found that services that used this digital solution identified substantially increased referrals (15% increase versus 6% increase in control services). Critically, this increase was particularly pronounced in minorities, such as nonbinary (179% increase) and ethnic minority individuals (29% increase). Using natural language processing to analyze qualitative feedback from 42,332 individuals, we found that the chatbot's human-free nature and the patients' self-realization of their need for treatment were potential drivers for the observed improvement in the diversity of access. This provides strong evidence that digital tools may help overcome the pervasive inequality in mental healthcare.

Using Conversational AI to Facilitate Mental Health Assessments and Improve Clinical Efficiency Within Psychotherapy Services: Real-World Observational Study.

BACKGROUND: Most mental health care providers face the challenge of increased demand for psychotherapy in the absence of increased funding or staffing. To overcome this supply-demand imbalance, care providers must increase the efficiency of service delivery. OBJECTIVE: In this study, we examined whether artificial intelligence (AI)-enabled digital solutions can help mental health care practitioners to use their time more efficiently, and thus reduce strain on services and improve patient outcomes. METHODS: In this study, we focused on the use of an AI solution (Limbic Access) to support initial patient referral and clinical assessment within the UK's National Health Service. Data were collected from 9 Talking Therapies services across England, comprising 64,862 patients. RESULTS: We showed that the use of this AI solution improves clinical efficiency by reducing the time clinicians spend on mental health assessments. Furthermore, we found improved outcomes for patients using the AI solution in several key metrics, such as reduced wait times, reduced dropout rates, improved allocation to appropriate treatment pathways, and, most importantly, improved recovery rates. When investigating the mechanism by which the AI solution achieved these improvements, we found that the provision of clinically relevant information ahead of clinical assessment was critical for these observed effects. CONCLUSIONS: Our results emphasize the utility of using AI solutions to support the mental health workforce, further highlighting the potential of AI solutions to increase the efficiency of care delivery and improve clinical outcomes for patients.