About this Research Topic

Background

Digital Therapeutics (DTx) is a medical software device designed to track health information, collect or measure health data, and change treatment. It needs to be scientifically and clinically validated according to the mechanism of therapeutic action to prevent, manage and treat diseases. Over the past few years, two major issues have been plaguing the lives of humans, especially patients, on a global scale. One is the COVID-19 pandemic, and the other is the aging population increase. Both challenges have greatly accelerated the digital revolution in healthcare and facilitated the development of family-based medical practices. As a powerful tool of family medicine, DTx has played an important role in primary care. DTx has the potential to improve primary care practices by helping physicians personalize care and by promoting patient efforts to improve compliance, track treatment responses, and make timely and accurate clinical diagnosis and patient care decisions. Achieving these goals requires multidisciplinary exploration. Improving DTx effects by internalizing computational methods and artificial intelligence has recently been studied and reported.

In fact, more and more AI and computing power are being integrated into DTx. The industry has invested heavily in advanced AI technologies that are more accurate and interpretable, and DTx has become more intuitive to use and provides valuable feedback to patients and physicians. Wearables are also developing or expanding their pathways to help DTx pick up more signals. Healthcare providers have invested to enable AI to promote the use of DTx in Primary Care. Big data advocates also hope that these sophisticated AI models can leverage data to improve the therapeutic effects of DTx.

The goal of the Research Topic is to provide a comprehensive overview on the current trends, original research, advanced technique and challenges of computational methods and artificial intelligence in digital therapeutics, including enablement, treatment, prevention, management, clinical trial, and regulation perspectives. We welcome contributions from practitioners in different disciplines, such as the practical experience of family medicine and primary care, the design experience of engineers, the technical improvements from algorithmic scientists, efforts to the promotion of the user experience, the collection of open datasets to optimize AI models, and the contributions of the regulatory community to the standard setting.

Original Research, Reviews, Brief Research Reports, and Opinion article types are welcome. Topics of interest include, but are not limited to:

- AI for precision medicine and preventive healthcare
- AI to understand human behavior and well-being
- Cognitive computing for healthcare and data intelligence
- Computing and AI for diseases diagnosis
- Computing and AI for DTx clinical trial and research
- Computing and AI for smart health records collection
- Computing and AI for smarter DTx
- Computing and AI-enabled medical computer vision
- Computing architecture of DTx system
- Computing techniques for enable physiological signal access
- Enable personal data security and effective use in DTx
- Natural language processing for semantic and sentiment analysis
- Patient experience and value-based care optimized by computing and AI
- Preventive DTx through AI and computing
- Robotic Process Automation in DTx
- The design and implementation of AI-based DTx
- Virtual coaching and AI chatbots powered by AI and computing

Topic Editor Dr. P.H. is employed at Merck KGaA. Topic Editor Dr. J.M is employed at Ping An Healthcare Technology. The other Topic Editors declare no competing interest in respect to the Research Topic subject.

Keywords: Digital Therapeutics, Artificial Intelligence, Machine Learning, Computing Approach, Health Interventions, Digital Medicine, Computer aided detection/diagnosis systems, Explainable AI in eHealth, Telemedicine, Digital Biomarkers

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