Extended reality therapies for anxiety disorders : a systematic review of patients’ and healthcare professionals’ perspectives

1) Background: Anxiety disorders are among the most common psychiatric conditions and have a rising prevalence. Patients with anxiety disorders can, however, be deterred from seeking treatment due to associated stigmas and medication side effects. Evidence indicates that promising digital health solutions to address those concerns reside in the growing field of extended reality (XR). The limited literature synthesis from the perspectives of patients and healthcare professionals (HCPs) regarding the experiences and effectiveness of XR-based anxiety disorder therapies motivated the undertaking of this systematic review. (2) Methods: A systematic search of the literature was conducted according to the PRISMA 2020 guidelines on the following databases: CINAHL, APA PsycNet and PubMed. The search was completed on 23 January 2024 with no restriction on the time of publication. Studies were screened based on a predetermined selection criteria relevant to the research aims. (3) Results: Five studies fulfilled the inclusion requirements. The majority investigated the use of XR tools for individual therapy and indicated that they can be as effective for patients as traditional methods and can aid in HCPs’ therapeutic tasks. (4) Conclusions: XR-based anxiety disorder therapies are generally perceived as immersive and with minimal side effects by patients, while HCPs mostly consider XR tools as practical and assistive. However, refinements with the XR setup could further improve the experience. Such modalities represent potent drug-free alternatives or supplements to traditional therapy and could be considered for remote, individual care. The findings’ generalisability requires further research into more conditions within the anxiety disorder group, as well as larger sample sizes.Peer reviewe

The state of artificial intelligence-based FDA-approved medical devices and algorithms:an online database

At the beginning of the artificial intelligence (AI)/machine learning (ML) era, the expectations are high, and experts foresee that AI/ML shows potential for diagnosing, managing and treating a wide variety of medical conditions. However, the obstacles for implementation of AI/ML in daily clinical practice are numerous, especially regarding the regulation of these technologies. Therefore, we provide an insight into the currently available AI/ML-based medical devices and algorithms that have been approved by the US Food & Drugs Administration (FDA). We aimed to raise awareness of the importance of regulatory bodies, clearly stating whether a medical device is AI/ML based or not. Cross-checking and validating all approvals, we identified 64 AI/ML based, FDA approved medical devices and algorithms. Out of those, only 29 (45%) mentioned any AI/ML-related expressions in the official FDA announcement. The majority (85.9%) was approved by the FDA with a 510(k) clearance, while 8 (12.5%) received de novo pathway clearance and one (1.6%) premarket approval (PMA) clearance. Most of these technologies, notably 30 (46.9%), 16 (25.0%), and 10 (15.6%) were developed for the fields of Radiology, Cardiology and Internal Medicine/General Practice respectively. We have launched the first comprehensive and open access database of strictly AI/ML-based medical technologies that have been approved by the FDA. The database will be constantly updated

Forecasting Artificial Intelligence Trends in Health Care: Systematic International Patent Analysis

BackgroundArtificial intelligence (AI)– and machine learning (ML)–based medical devices and algorithms are rapidly changing the medical field. To provide an insight into the trends in AI and ML in health care, we conducted an international patent analysis. ObjectiveIt is pivotal to obtain a clear overview on upcoming AI and MLtrends in health care to provide regulators with a better position to foresee what technologies they will have to create regulations for, which are not yet available on the market. Therefore, in this study, we provide insights and forecasts into the trends in AI and ML in health care by conducting an international patent analysis. MethodsA systematic patent analysis, focusing on AI- and ML-based patents in health care, was performed using the Espacenet database (from January 2012 until July 2022). This database includes patents from the China National Intellectual Property Administration, European Patent Office, Japan Patent Office, Korean Intellectual Property Office, and the United States Patent and Trademark Office. ResultsWe identified 10,967 patents: 7332 (66.9%) from the China National Intellectual Property Administration, 191 (1.7%) from the European Patent Office, 163 (1.5%) from the Japan Patent Office, 513 (4.7%) from the Korean Intellectual Property Office, and 2768 (25.2%) from the United States Patent and Trademark Office. The number of published patents showed a yearly doubling from 2015 until 2021. Five international companies that had the greatest impact on this increase were Ping An Medical and Healthcare Management Co Ltd with 568 (5.2%) patents, Siemens Healthineers with 273 (2.5%) patents, IBM Corp with 226 (2.1%) patents, Philips Healthcare with 150 (1.4%) patents, and Shanghai United Imaging Healthcare Co Ltd with 144 (1.3%) patents. ConclusionsThis international patent analysis showed a linear increase in patents published by the 5 largest patent offices. An open access database with interactive search options was launched for AI- and ML-based patents in health care

Health outcomes, attitudes, and improvements of synchronous virtual consultations for non-malignant chronic conditions: a scoping review

Background: While virtual consultations have experienced a rise in adoption in recent years, retention remains challenging and aspects around the associated experiences and outcomes remain unclear.Objective: The need to further investigate those aspects were motivating factors for conducting a scoping review with a focus on synchronous virtual consultations for non-malignant chronic illnesses, and the associated health outcomes, attitudes and potentials for technological improvements.Methods: The Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guided the scoping review process. An inclusion criteria based on the Population, Concept and Context (PCC) framework was designed. A search strategy, informed by the PCC framework, was applied to PubMed (including MEDLINE), CINAHL Complete, APA PsycNet, Web of Science, IEEE and ACM Digital. Screening of articles and data extraction of included articles were performed in parallel and independently by two researchers who corroborated their findings and resolved any conflicts.Results: 4,167 unique articles were identified from the databases searched. Following multi-layer filtration, 19 studies fulfilled the inclusion criteria and relevant data were extracted from their full texts.Conclusions: For patients with non-communicable chronic conditions, virtual consultations are generally associated with positive health outcomes that are either directly or indirectly related to their ailment; but sustained improvements remain unclear. These modalities also indicate the potential to empower such patients to better manage their condition. HCPs and patients tend to be satisfied with remote care experience and most are receptive to the modality as an option. Assistance from supplemental technologies mostly reside in addressing technical issues and additional modules could be integrated to address challenges relevant to patients and HCPs. However, positive outcomes and attitudes towards the modality might not apply to all cases, indicating that virtual consultations are more appropriate as options rather than replacements of in-person visits