Journal of Medical Internet Research

Dementia has become a major public health concern due to its heavy disease burden. Mild cognitive impairment (MCI) is a transitional stage between healthy aging and dementia. Early identification of MCI is an essential step in dementia prevention.

Increased digital health and social care services are generally considered to improve people’s access to services. However, not everyone can equally access and use these resources. Health and social care professionals should assess clients’ suitability for digital solutions, but to succeed, they need information about what to evaluate and how.

Users increasingly use social networking services (SNSs) to share their feelings and emotions. For those with mental disorders, SNSs can also be used to seek advice on mental health issues. One available SNS is Reddit, in which users can freely discuss such matters on relevant health diagnostic subreddits.

Metabolic syndrome (MetS) is a common public health challenge. Health-promoting behaviors such as diet and physical activity are central to preventing and controlling MetS. However, the adoption of diet and physical activity behaviors has always been challenging. An individualized mobile health (mHealth)–based intervention using the Behavior Change Wheel is promising in promoting health behavior change and reducing atherosclerotic cardiovascular disease (ASCVD) risk. However, the effects of this intervention are not well understood among people with MetS in mainland China.

Several systematic reviews have addressed digital technology use for treatment and monitoring of chronic obstructive pulmonary disease (COPD).

Although previous research has made substantial progress in developing high-performance artificial intelligence (AI)–based computer-aided diagnosis (AI-CAD) systems in various medical domains, little attention has been paid to developing and evaluating AI-CAD system in ophthalmology, particularly for diagnosing retinal diseases using optical coherence tomography (OCT) images.

Patient medication reviews on social networking sites provide valuable insights into the experiences and sentiments of individuals taking specific medications. Understanding the emotional spectrum expressed by patients can shed light on their overall satisfaction with medication treatment. This study aims to explore the emotions expressed by patients taking phosphodiesterase type 5 (PDE5) inhibitors and their impact on sentiment.

As digital health services advance, digital health equity has become a significant concern. However, people with disability and older adults still face health management limitations, particularly in the COVID-19 pandemic. An essential area of investigation is proposing a patient-centered design strategy that uses patient-generated health data (PGHD) to facilitate optimal communication with caregivers and health care service providers.

Rapid digitalization in health care has led to the adoption of digital technologies; however, limited trust in internet-based health decisions and the need for technical personnel hinder the use of smartphones and machine learning applications. To address this, automated machine learning (AutoML) is a promising tool that can empower health care professionals to enhance the effectiveness of mobile health apps.

The routine measurement of patient-reported outcomes in cancer clinical care using electronic patient-reported outcome measures (ePROMs) is gaining momentum worldwide. However, a deep understanding of the mechanisms underpinning ePROM interventions that could inform their optimal design to improve health outcomes is needed.

Health technology innovation is increasingly supported by a bottom-up approach to priority setting, aiming to better reflect the concerns of its intended beneficiaries. Web-based forums provide parents with an outlet to share concerns, advice, and information related to parenting and the health and well-being of their children. They provide a rich source of data on parenting concerns and priorities that could inform future child health research and innovation.

Artificial intelligence (AI) chatbots like ChatGPT and Google Bard are computer programs that use AI and natural language processing to understand customer questions and generate natural, fluid, dialogue-like responses to their inputs. ChatGPT, an AI chatbot created by OpenAI, has rapidly become a widely used tool on the internet. AI chatbots have the potential to improve patient care and public health. However, they are trained on massive amounts of people’s data, which may include sensitive patient data and business information. The increased use of chatbots introduces data security issues, which should be handled yet remain understudied. This paper aims to identify the most important security problems of AI chatbots and propose guidelines for protecting sensitive health information. It explores the impact of using ChatGPT in health care. It also identifies the principal security risks of ChatGPT and suggests key considerations for security risk mitigation. It concludes by discussing the policy implications of using AI chatbots in health care.