Good mental health enables individuals to cope with the normal stresses of life. In Germany, approximately one-quarter of the adult population is affected by mental illnesses. Teletherapy and digital health applications are available to bridge gaps in care and relieve healthcare professionals. The acceptance of these tools is a strongly influencing factor for their effectiveness, which also needs to be evaluated for AI-based conversational agents (CAs) (e. g. ChatGPT, Siri) to assess the risks and potential for integration into therapeutic practice. This study investigates the perspectives of both the general population and healthcare professionals with the following questions: 1. How frequently are CAs used for mental health? 2. How high is the acceptance of CAs in the field of mental health? 3. To what extent is the use of CAs in counselling, diagnosis, and treatment acceptable? To address these questions, two quantitative online surveys were conducted with 444 participants from the general population and 351 healthcare professionals. Statistical analyses show that 27 % of the surveyed population already confide their concerns to CAs. Not only experience with this technology but also experience with telemedicine shows a higher acceptance among both groups for using CAs for mental health. Additionally, participants from the general population were more likely to support CAs as companions controlled by healthcare professionals rather than as additional experts for the professionals. CAs have the potential to support mental health, particularly in counselling. Future research should examine the influence of different communication media and further possibilities of augmented intelligence. With the right balance between technology and human care, integration into patient-professional interaction can be achieved.

Digital health, including telemedicine, has increased access to abortion care. The convenience, flexibility of appointment times, and ensured privacy to abortion users may make abortion services via telemedicine preferable. This scoping review systematically mapped studies conducted on abortion services via telemedicine, including their effectiveness and acceptability for abortion users and providers. All published papers included abortion services via telemedicine in the United States were considered. Articles were searched in PubMed, CINAHL, and Google Scholar databases in September 2022. The findings were synthesized narratively, and the PRISMA-ScR guidelines were used to report this study. Out of 757 retrieved articles, 33 articles were selected based on the inclusion criteria. These studies were published between 2011 and 2022, with 24 published in the last 3 years. The study found that telemedicine increased access to abortion care in the United States, especially for people in remote areas or those worried about stigma from in-person visits. The effectiveness of abortion services via telemedicine was comparable to in-clinic visits, with 6% or fewer abortions requiring surgical intervention. Both care providers and abortion seekers expressed positive perceptions of telemedicine-based abortion services. However, abortion users reported mixed emotions, with some preferring in-person visits. The most common reasons for choosing telemedicine included the distance to the abortion clinic, convenience, privacy, cost, flexibility of appointment times, and state laws imposing waiting periods or restrictive policies. Telemedicine offered a preferable option for abortion seekers and providers. The feasibility of accessing abortion services via telemedicine in low-resource settings needs further investigation.

The future of healthcare is a rapidly evolving field that holds great promise for improving the health and well-being of people around the world. Advances in technology and medicine are making it possible to diagnose and treat diseases more effectively and efficiently, while new approaches to healthcare delivery are making it more accessible to underserved communities. One of the most promising areas of development in the healthcare field is the use of artificial intelligence (AI) and machine learning to improve the diagnosis and treatment of diseases. Another area of growth is the use of telemedicine, which allows doctors and patients to communicate remotely using technology like videoconferencing. This has become increasingly important during the COVID-19 pandemic, and it's expected that telemedicine will continue to grow in popularity as a way to provide care to patients in remote or underserved areas.

The healthcare industry is facing increasing demand due to population growth, aging, and the rise of chronic diseases. According to the World Health Organization, the global demand for healthcare services is expected to increase by 15% by 2030. The healthcare industry is also one of the largest and fastest-growing sectors of the global economy, with spending expected to reach $10 trillion by 2022. To meet this demand and improve patient outcomes, healthcare providers are turning to technology. Telemedicine refers to the use of telecommunications and digital technologies to remotely diagnose and treat patients.

Medical dialogue generation aims to generate responses according to a history of dialogue turns between doctors and patients. Unlike open-domain dialogue generation, this requires background knowledge specific to the medical domain. Existing generative frameworks for medical dialogue generation fall short of incorporating domain-specific knowledge, especially with regard to medical terminology. In this paper, we propose a novel framework to improve medical dialogue generation by considering features centered on domain-specific terminology. We leverage an attention mechanism to incorporate terminologically centred features, and fill in the semantic gap between medical background knowledge and common utterances by enforcing language models to learn terminology representations with an auxiliary terminology recognition task. Experimental results demonstrate the effectiveness of our approach, in which our proposed framework outperforms SOTA language models. Additionally, we provide a new dataset with medical terminology annotations to support the research on medical dialogue generation. Our dataset and code are available at https://github.com/tangg555/meddialog.

Digital transformation in healthcare refers to the use of digital technologies to modernize and improve the efficiency of healthcare systems, processes, and practices. This can include everything from electronic health records and telemedicine to wearable devices and AI-powered tools for diagnosis and treatment. The goal of digital transformation in healthcare is to improve patient outcomes, reduce costs, and increase access to healthcare services. Some specific examples of digital transformation in healthcare include the use of electronic health records to track patient histories and treatment plans, the use of telemedicine to provide remote consultations and follow-ups, and the use of wearable devices to monitor patient health and provide real-time data to healthcare providers. There are a wide variety of technologies that are being used in the healthcare industry as part of digital transformation.

A fascinating session at the EASD 2022 conference on emerging technologies shed light on where we are with AID and telemedicine, and what leading researchers in diabetes believe is coming next in diabetes management. Healthcare is rapidly evolving, and now more than ever, robots and artificial intelligence have gone from science fiction to critical components of diabetes management. At the EASD 2022 conference in Stockholm, Sweden, researchers further explored this concept in a session titled, "A New Hope or Strange New Worlds: Submerging diabetes into emerging technologies." Dr. Moshe Phillip, head of the Institute of Endocrinology and Diabetes at Schneider Children's Medical Center of Israel, began by demonstrating how continuous glucose monitors (CGMs) represent a paradigm shift in diabetes technology. "CGM is the most important tool in the last 20 years," he said.

We are facing a global healthcare crisis today as the healthcare cost is ever climbing, but with the aging population, government fiscal revenue is ever dropping. To create a more efficient and effective healthcare system, three technical challenges immediately present themselves: healthcare access, healthcare equity, and healthcare efficiency. An autonomous mobile clinic solves the healthcare access problem by bringing healthcare services to the patient by the order of the patient's fingertips. Nevertheless, to enable a universal autonomous mobile clinic network, a three-stage technical roadmap needs to be achieved: In stage one, we focus on solving the inequity challenge in the existing healthcare system by combining autonomous mobility and telemedicine. In stage two, we develop an AI doctor for primary care, which we foster from infancy to adulthood with clean healthcare data. With the AI doctor, we can solve the inefficiency problem. In stage three, after we have proven that the autonomous mobile clinic network can truly solve the target clinical use cases, we shall open up the platform for all medical verticals, thus enabling universal healthcare through this whole new system.

Artificial Intelligence (AI) backed telemedicine services are no longer restricted to research laboratories only as they now contribute much to the ongoing efforts aimed to improve healthcare services. Telehealth (or Telemedicine) is referred to as the practical application of electronic data and broadcast communication technologies to help support long-distance clinical medicinal services, patient and expert health-related education, public health, and healthcare organization. In recent times, a surge has been observed in mobile health, e-diagnosis, and medication. Tata Consultancy Services recently observed in the latest study featuring 56 driving healthcare organizations that said that 86% of them have already embraced AI. According to the Global Observatory survey for eHealth conducted by World Health Organization, AI in the telemedicine field is directly contributing to innovations in areas including teleradiology, telepathology, teledermatology, and telepsychiatry.

Investors have honed in on artificial intelligence as the next big thing in health care, with billions flowing into AI-enabled digital health startups in recent years. But the technology has yet to transform medicine in the way many predicted, Ben and Ruth report. "Companies come in promising the world and often don't deliver," Bob Wachter, head of the department of medicine at the University of California, San Francisco, told Future Pulse. "When I look for examples of … true AI and machine learning that's really making a difference, they're pretty few and far between. Administrators say that algorithms from third-party firms often don't work seamlessly because every health system has its own tech system, so hospitals are developing their own in-house AI.