Journal of Participatory Medicine

The use of artificial intelligence (AI) in healthcare has significant implications for patient-clinician interactions. Practical and ethical challenges have emerged with the adoption of large language models (LLMs) that respond to prompts from clinicians, patients and caregivers. With an emphasis on patient experience, this paper examines the potential of LLMs to act as facilitators, interrupters, or both in patient-clinician relationships. Drawing on our experiences as patient advocates, computer scientists, and physician informaticists working to improve data exchange and patient experience, we examine how LLMs might enhance patient engagement, support triage, and inform clinical decision-making. While affirming LLMs as a tool enabling the rise of the “AI patient,” we also explore concerns surrounding data privacy, algorithmic bias, moral injury, and the erosion of human connection. To help navigate these tensions, we outline a conceptual framework that anticipates the role and impact of LLMs in patient-clinician dynamics and propose key areas for future inquiry. Realizing the potential of LLMs requires careful consideration of which aspects of the patient-clinician relationship must remain distinctly human and why, even when LLMs offer plausible substitutes. This inquiry should draw on ethics and philosophy, aligned with AI imperatives such as patient-centered design and transparency, and shaped through collaboration between technologists, healthcare providers, and patient communities.

More than a few concepts have been presented in rehabilitation clinics that implement aspects of modern information technology in the arrangement of augmented reality or virtual rehabilitation aiming to enhance cognitive or motor learning and rehabilitation motivation. Despite their scientific success, it is currently unknown whether rehabilitants will accept rehabilitation concepts that integrate modern information technologies.

Humanity stands at the threshold of a new era in biological understanding, disease treatment, and overall wellness. The convergence of evolving patient and caregiver (consumer) behaviors, increased data collection, advancements in health technology and standards, federal policies, and the rise of artificial intelligence (AI) is driving one of the most significant transformations in human history. To achieve transformative healthcare insights, AI must have access to comprehensive longitudinal health records (LHRs) that span clinical, genomic, non-clinical, wearable and patient generated data. Despite the extensive use of electronic medical records (EMR) and widespread interoperability efforts, current healthcare organizations, EMR vendors, and public agencies are not incentivized to develop and maintain complete LHRs. This paper explores the new paradigm of consumers as the common provenance and singular custodian of LHRs. With fully aligned intentions and ample time to dedicate to optimizing their health outcomes, patients and caregivers must assume the sole responsibility to manage or delegate aggregation of complete, accurate, and real-time LHRs. Significant gaps persist in empowering consumers to act as primary custodians of their health data and to aggregate their complete LHRs, a foundational requirement for the effective application of AI. Rare disease communities – leaders in participatory care – offer a compelling model for demonstrating how consumer-driven data aggregation can be achieved and underscore the need for improved policy frameworks and technological tools. The convergence of AI and LHRs promises to transform medicine by enhancing clinical decision-making, accelerating accurate diagnoses, and dramatically advancing our ability to understand and treat disease at an unprecedented pace.

Most definitions of therapeutic empathy are based on practitioners’ perspectives and few account for patients’ views. We therefore do not understand what therapeutic empathy means to patients. Given that therapeutic empathy involves a relationship between patients and practitioners, the under-representation of the patient voice threatens to undermine the validity of therapeutic empathy definitions and subsequently, how the concept is measured, taught, and practiced.

Improving mental health treatment within the Collaborative Care Model (CoCM) may be achieved by using e-mental health (e-MH) tools and addressing the challenges pertaining to their integration.

Artificial intelligence (AI) is reshaping medical imaging with the promise of improved diagnostic accuracy and efficiency. Yet, its ethical and effective adoption depends not only on technical excellence but also on aligning implementation with patient perspectives. This commentary synthesizes emerging research on how patients perceive AI in radiology, expressing cautious optimism, a desire for transparency, and a strong preference for human oversight. Patients consistently view AI as a supportive tool rather than a replacement for clinicians. We argue that centering patient voices is essential to sustaining trust, preserving the human connection in care, and ensuring that AI serves as a truly patient-centered innovation. The path forward requires participatory approaches, ethical safeguards, and transparent communication to ensure that AI enhances, rather than diminishes, the values patients hold most dear.

Artificial intelligence (AI) Is rapidly transforming healthcare, offering potential benefits in diagnosis, treatment, and workflow efficiency. However, limited research explores patient perspectives on AI, especially in its role in diagnosis and communication. This study examines patient perceptions of various AI applications, focusing on the diagnostic process and communication.

Major Depressive Disorders (MDD) significantly impacts individuals’ lives, with varied treatment responses necessitating personalized approaches. Shared decision-making (SDM) enhances patient-centred care by involving patients in treatment choices. To date, instruments facilitating SDM in depression treatment are limited, particularly those that incorporate personalized information alongside general patient data and in co-creation with patients.

Surgical ward rounds (SWRs) are often unstructured and deprioritized compared to traditional surgical tasks, leading to limited interdisciplinary collaboration, unprepared patients, and low family attendance.

Patient portals demonstrate significant potential for improving healthcare engagement but face critical adoption challenges. Disparities persist across different demographic groups, creating a digital divide in healthcare access. Targeted training strategies, particularly personalized and one-on-one approaches, show promise in increasing portal utilization. Innovative solutions, like community health workers specializing in digital navigation, offer a potential pathway to reduce enrollment barriers. The key challenge remains developing a scalable, cost-effective training model.

Patient engagement in research represents an evolution in how new knowledge is being created. Individuals and teams seeking to conduct research in this way want to learn how to best approach this work. Specialized training is required to ensure that these individuals and groups have the knowledge and skills to engage with and accomplish these goals. We developed an on-line training program, called Patient-Oriented Research Training & Learning - Primary Health Care (PORTL-PHC), to address this need.

This paper will view the rise of the e-patient, who is “equipped, enabled, empowered and engaged” through the lens of the evolution of successive digital technology innovations, each building on its predecessors, creating new tools for patient empowerment. We begin with the dawn of the Web and the proliferation of health websites and discuss the use of digital communication tools. We then discuss the adoption of electronic health records which enabled the rise of patient portals. This digitization of health data, along with the rapid adoption of mobile internet access and the proliferation of health-related smartphone apps, in turn, provided a platform for patients to co-produce healthcare by contributing their own health data to their self-care and healthcare. The exchange of health information between patients and providers has also been facilitated by telehealth/telemedicine technology which enables direct care delivery. The use of social networks in health, in use since the early days of the Web, has expanded since COVID, when public health authorities worldwide, as well as patients, sought the use of social media channels to get connected and share information. Most recently, artificial intelligence and large language models have emerged with yet untapped potential to provide patients with the information that could improve their understanding of their conditions and treatment options. We conclude that innovations in digital health technology have symbiotically evolved with the ascendance of the e-patient, enabling improved communication, collaboration, and coordination between patients and clinicians and forging a healthcare system that is safer and more responsive to patient needs.