Journal of Medical Internet Research

Accurate classification of patient complaints is crucial for enhancing patient satisfaction management in health care settings. Traditional manual methods for categorizing complaints often lack efficiency and precision. Thus, there is a growing demand for advanced and automated approaches to streamline the classification process.

e-Consultations, defined as asynchronous text-based messaging, have transformed how patients interact with their general practitioner (GP). While e-consultations can improve patient access to GP care, concerns about increased workload for GPs are raised.

The health care industry must align with new digital technologies to respond to existing and new challenges. Digital twins (DTs) are an emerging technology for digital transformation and applied intelligence that is rapidly attracting attention. DTs are virtual representations of products, systems, or processes that interact bidirectionally in real time with their actual counterparts. Although DTs have diverse applications from personalized care to treatment optimization, misconceptions persist regarding their definition and the extent of their implementation within health systems.

The widespread availability of internet-based pornography has led to growing concerns about its impact on mental health, particularly among young adults. Despite increasing recognition of problematic pornography use, standardized diagnostic criteria for pornography addiction are lacking.

Extended reality (XR) technologies are increasingly being used to reduce health and procedural anxieties. The global effectiveness of these interventions is uncertain, and there is a lack of understanding of how patient outcomes might vary between different contexts and modalities.

Virtual reality (VR) training uses computer-generated simulations that enable users to engage with immersive virtual environments, simulating real-world activities or therapeutic exercises. This technology is increasingly recognized as a promising intervention to address the physical and psychological challenges faced by dialysis patients, who frequently experience diminished physical function, social isolation, and emotional distress associated with prolonged treatment regimens. Given the increasing prevalence of dialysis patients and the limitations of conventional rehabilitation approaches, VR presents a novel, interactive method that has the potential to enhance patient well-being and improve quality of life.

Artificial intelligence (AI) predictive models in primary health care have the potential to enhance population health by rapidly and accurately identifying individuals who should receive care and health services. However, these models also carry the risk of perpetuating or amplifying existing biases toward diverse groups. We identified a gap in the current understanding of strategies used to assess and mitigate bias in primary health care algorithms related to individuals’ personal or protected attributes.

Nowadays, optimal patient care should be based on data-driven decisions. In the course of digitization, hospitals, in particular, are becoming complex organizations with an enormously high density of digital information. Ensuring information security is, therefore, essential and has become a major challenge. Researchers have shown that—in addition to technological and regulatory measures—it is also necessary for all employees to follow security policies and consciously use information technology (compliance), because noncompliance can lead to security breaches with far-reaching consequences for the organization. There is little empirical research on information security–related behavior in hospitals and its organizational antecedents.

Patients undergoing surgery often experience stress and anxiety, which can increase complications and hinder recovery. Effective management of these psychological factors is key to improving outcomes. Preoperative anxiety is inversely correlated with the amount of information patients receive, but accessible, personalized support remains limited, especially in preoperative settings. Face-to-face education is often impractical due to resource constraints. Digital health (DH) interventions offer a promising alternative, enhancing patient engagement and empowerment. However, most current tools focus on providing information, overlooking the importance of personalization and psychological support.

Artificial intelligence–driven clinical decision support systems (AI-CDSSs) are pivotal tools for doctors to improve diagnostic and treatment processes, as well as improve the efficiency and quality of health care services. However, not all doctors trust artificial intelligence (AI) technology, and many remain skeptical and unwilling to adopt these systems.

Recruiting and retaining participants in pediatric research has always been challenging, particularly in healthy populations and remote areas, leading to selection bias and increased health disparities. In the digital age, medical research has been transformed by digital tools, offering new opportunities to enhance engagement in clinical research. However, public perspectives on digitalizing pediatric research and potential differences between urban and suburban areas remain unclear.

Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel in natural language processing (NLP), enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs, like GPT-4 and BERT (Bidirectional Encoder Representations from Transformer), demonstrate LLMs’ evolution through improved computing power and data. However, their high hardware requirements are being addressed through technological advancements. LLMs are unique in processing multimodal data, thereby improving emergency, elder care, and digital medical procedures. Challenges include ensuring their empirical reliability, addressing ethical and societal implications, especially data privacy, and mitigating biases while maintaining privacy and accountability. The paper emphasizes the need for human-centric, bias-free LLMs for personalized medicine and advocates for equitable development and access. LLMs hold promise for transformative impacts in health care.