Background:The medical black bag is synonymous with physicians, especially general practitioners, who are expected to be ready to provide care across settings. The content of the devices they use will likely expand due to the proliferation of digital tools. As portable diagnostics diversify, guidance is increasingly needed on which tools clinicians should choose and what this shift may mean for the physical examination and point-of-care assessment. Objective:This study aimed to map the current, the possible, and the future content of the medical black bag using anticipatory methods, and to provide a general, practice-oriented outline of how portable diagnostic technologies may evolve in primary care. Methods:National equipment lists and the World Health Organization's MeDevIS database were compiled and filtered to define a contemporary reference set of reusable portable diagnostic instruments relevant to generalist practice. A 1-year trend analysis using major professional and medical technology news sources was conducted to identify possible additions, screening for devices with diagnostic relevance, portability, digital capability, market presence, and evidence visibility. To extend the outlook to the next decade, we performed a horizon-scanning exercise using the same review period. These devices were grouped into thematic categories. Results:National equipment recommendations and World Health Organization lists yielded a stable core set of diagnostic tools used in routine primary care practice. Trend analysis and horizon scanning expanded this set by identifying possible and future additions of portable medical devices that can be used at the point of care. Overall, the identified technologies were increasingly digital, diverse, connected, and in some cases, AI-supported, reflecting a trajectory toward more integrated and data-enabled diagnostics. Conclusions:The medical black bag is likely to evolve from a stable set of familiar instruments toward a broader toolbox of portable and connected diagnostic devices. While these tools may expand the scope of bedside assessment and enable more reproducible and shareable clinical signs, their value depends on appropriate validation, usability, workflow integration, training, and supportive financial and organizational conditions. Regular evidence-informed updates of equipment recommendations, alongside practical implementation support, may help primary care systems adopt useful innovations while preserving the human dimensions of clinical care.

Medical students are increasingly engaging with various digitally-based educational settings, such as online platforms, social media, and generative AI systems. Among these, generative AI and medically trained influencers stand out as rapidly expanding yet underexplored sources of educational and professional content, which may impact students’ mental health. Although existing research notes both advantages and risks, the precise ways in which these digital exposures affect mental health remain poorly understood. This study aimed to examine how digital exposure impacts the mental health of medical students and to develop a future-oriented, system-level framework for mitigating associated risks through a foresight-based approach. This study employed a qualitative backcasting approach informed by two previously conducted scenario analyses. The first examined the mental health implications of generative AI in medical education through a literature-informed foresight process, while the second explored the influence of medically trained influencers, drawing on qualitative data from medical students, educators, health care professionals, and content creators. Findings from these studies were synthesized to define a preferred future state, after which backcasting was used to identify the conditions, milestones, and actions required to move toward this future between 2026 and 2031. The backcasting analysis outlines three key phases: early emergence (2027–2028), system integration (2029–2030), and full stabilization (2031). Achieving these phases requires developing AI curricula, providing interpretive training, fostering a digital culture, offering mental health support, creating mentorship systems, and aligning policies. Many interventions focus on reorganizing existing practices instead of implementing costly reforms. The findings indicate that promoting medical students’ mental health in digital environments requires moving beyond just managing exposure towards fostering a better understanding. Viewing digital transformation as an interpretive and developmental process, this study may offer a clear, practical guide to systemic change. The promoted framework could serve as a foundation for educators, institutions, and policymakers to create targeted and scalable interventions. Many of these proposed actions might be integrated into current educational systems, but their practicality and success need to be confirmed through empirical studies. -

In response to the growing demands of chronic conditions, artificial intelligence (AI)-enhanced remote consultations indicate a promising avenue. However, reports detailing the co-design of assistive AI tools for remote chronic care remain limited. In addition, digital tools that fail to integrate end-user perspectives have been associated with inefficient adoption. As such, the development of AI-assisted virtual consultation tools would benefit from end-user involvement, such as through co-design. To report on the co-design of CARA (Consultation Analysis & Response Assistant), a novel, evidence-based AI prototype to assist in remote chronic care, and explore end-users’ perceptions of the tool. Four participants, including two patients and two healthcare professionals (HCPs), with remote chronic care experience from the public health sector in rural Northwest Ireland, were involved in three iterative co-design workshops. These included self-paced, hands-on sessions with CARA, and interactive group feedback and brainstorming. Discussions were analysed using reflexive thematic analysis to identify themes around usability and acceptability perspectives. The first author led the development of CARA, which relied on natural language processing techniques and incorporated participant feedback during iterative development cycles. The prototype's backend was developed in Python, interfacing with large language models analysing synthetic but representative virtual consultation transcripts, and this AI-generated output would be parsed onto an HTML frontend. Incremental changes were made to both the frontend and backend of CARA, based on participants’ feedback. This study has shared the evidence-based features of this bespoke AI prototype, which include patient- and HCP-facing consultation summaries, patient access to additional resources relevant to their condition, familiarising HCPs with a patient’s medical history with a view to improving rapport building, and analysing patients’ sentiments to provide insights for HCPs to improve the interaction quality. This study also provided practical details around the co-design process structure for future reference. Five themes emerged from the discussions, namely: System-level challenges, Remote assessment and interaction assistance, Trust and human oversight over powerful AI assistance, Holistic patient view with emotional dimension, and Usability and accessibility of outputs. There was a general acceptance of CARA, despite concerns over the systemic requirements for its implementation in practice. The agile, user-centred development resulted in software that addresses the pertinent needs and preferences of end-users, ensuring that the AI meaningfully supports - rather than undermines - both patient experience and clinical practice. The findings lay the groundwork for subsequent research and eventual practical uses.

Background: Generative artificial intelligence (AI) is quickly changing medical education, even as medical students still face high levels of stress, anxiety, and burnout. These simultaneous trends-technological upheaval and ongoing mental health issues-bring up important questions about how future doctors will be trained and supported. Understanding how these factors might influence each other is crucial for developing resilient, future-ready medical education systems. Objective: We carried out a foresight study using scenario analysis to examine potential futures at the crossroads of generative AI adoption and medical students' mental health. An initial environmental scan of the literature was conducted to pinpoint emerging trends and weak signals related to AI in medical education and well-being. These phenomena were categorized within a macro-meso-micro framework and analyzed through a multilevel sociotechnical change perspective. The study focused on 2 principal factors: the extent of generative AI integration into medical curricula and the availability of mental health support, as key drivers and critical uncertainties influencing future trajectories. Methods: These dimensions resulted in 4 distinct scenarios: Analog Happiness (high support and low AI integration), Gen AI Paradise (high support and high integration), Disconnected Struggles (low support and low integration), and Gen AI Takeover (low support and high integration). Each scenario demonstrates how various institutional responses can impact students' digital readiness, psychological well-being, and professional growth. For each one, we identified the main systemic risks and suggested immediate institutional measures to address them. Results: The findings suggest that technological innovation and mental health support must coevolve in medical education. Prioritizing one without the other risks producing either digitally unprepared or emotionally fragile physicians. Faculty readiness, ethical frameworks, and participatory curriculum design are critical to ensuring balanced integration. We formulated practical recommendations tailored to students, educators, and other stakeholders to guide balanced adaptation. Conclusions: Generative AI is more than just an additional tool in medical education; it is a systemic force that redefines how future physicians learn and operate. If technological change and student mental health are tackled separately, medical education risks creating graduates who are either unprepared for digital demands or mentally overwhelmed. This study highlights key systemic risks and suggests initial institutional steps to address them, providing a foresight-driven framework to assist educators and policymakers in responsible AI integration while safeguarding the well-being of future doctors.

Background: In recent years, virtual consultations have emerged as a crucial approach for continuity of chronic care provision, indicating a promising avenue for the future of smart healthcare systems. However, reversions to in-person care highlight persistent limitations, despite notable advantages of remote modalities. In parallel, recent developments in artificial intelligence (AI) indicate the potential to enhance remote chronic care, but user perceptions of such assistance and the corresponding human factors remain underexplored. Objective: This mixed methods study aims to better understand the virtual consultation experiences and attitudes toward AI-assisted tools in remote care among patients with noncommunicable chronic conditions and their healthcare professionals (HCPs). It conducts an in-depth examination of the associated human–computer interaction and usability elements of virtual consultations and of potential AI assistance. Methods: Public and Patient Involvement was integrated to run pilots and refine documentations. Semi-structured interviews with patients (n = 10), focus groups with HCPs (n = 15), and an online survey (n = 83) were conducted. Qualitative data was analysed through a reflexive thematic approach. The survey comprised the Telehealth Usability Questionnaire (TUQ) and bespoke items on user AI views, and the data was used to triangulate the qualitative findings. Nonparametric Kruskal–Wallis tests and ε2 effect sizes compared TUQ and AI views scores between current and former virtual consultation user groups. Results: Seven themes emerged from the qualitative data, which were supported by the quantitative findings. The statistical analyses resulted in a mean TUQ total score of 90.6 (SD = 15.0), which indicates high usability and user satisfaction; however, they failed to detect a difference between groups (p > 0.05; ε2 = 0.002–0.032). There was a clear preference for hybrid models, while a lack of empathy was identified during remote interactions. While a notable proportion of users indicated a literacy gap towards AI use in healthcare settings, they expressed cautious openness towards AI assistance, contingent upon transparency, human oversight, and data integrity; indicating a potential gap between competence to judge the technology and willingness to use it. Significant differences in views on AI assistance across groups failed to be detected (p > 0.05; ε2 = 0.005–0.065). Conclusions: Virtual consultations for chronic conditions are widely usable and acceptable, particularly through hybrid approaches. Addressing empathic engagement, holistic patient status, and transparent AI integration can enhance clinical quality and user experiences during remote interactions. However, the low statistical power and failure to detect a difference between groups (likely due to the small sample size) indicate the need for caution when interpreting the quantitative findings. There is also the implicit need to address potential AI literacy gap among users, indicating the need for robust safeguard measures. This study has also identified evidence-based assistive AI features that can potentially enhance virtual consultations. These insights can inform the co-design of evidence-based virtual care platforms, policies and supportive AI tools to sustain remote chronic care delivery.

Abstract Healthcare systems face escalating long-term uncertainty due to technological advancement, demographic shifts, and heightened social demands, yet foresight is still insufficiently employed within mission-critical healthcare institutions. This article contributes to futures studies by providing a comprehensive empirical case of applied, participatory, and normative foresight conducted within a national ambulance service. Drawing on the ‘National Ambulance Service 2040’ project in Hungary, the findings indicate how classic foresight methods – STEEP analysis, scenario development, futures wheel, and visioning – can be systematically combined and embedded in organizational decision-making under real-world constraints. The case demonstrates how foresight enhances anticipatory capacity building, strategic sensemaking, and normative alignment within a high-pressure public service context. Methodologically, the article advances applied futures studies by evidencing the complementarity of foresight tools and by highlighting the performative role of visioning as a governance and mobilization mechanism. Substantively, the findings show that foresight can be both cost-effective and impactful in publicly funded, resource-constrained healthcare systems. By extending healthcare foresight into the largely unexplored domain of emergency medical services, the article positions healthcare as a fertile site for methodological advancement in futures studies. The article also offers policy-relevant insights into anticipatory governance, workforce readiness, and the ethical incorporation of artificial intelligence in healthcare.

Introduction: The increasing prominence of influencers with medical backgrounds on social media not only changes how medical students shape their professional identities, learn, and handle mental health, but also prompts important questions about the future path of these processes. Although current research points to both positive and negative effects, the long-term consequences and the growing impact of medical influencers on students remain poorly understood. Methods: A qualitative foresight method integrated environmental scanning with inductive analysis of 45 questionnaire responses from medical students, educators, and influencers. Data analysis included open coding, thematic synthesis, and pinpointing key driving forces and critical uncertainties. Using these insights, a scenario analysis was conducted to create four plausible future scenarios along two axes: the impact of medical influencers positive versus negative) and the degree of institutional response (low versus high). Results: The four scenarios show that the same mechanisms, like relatability, visibility, and peer communication, can produce supportive or harmful results depending on how institutions engage. Without structured interpretive frameworks, there was a higher risk of psychological stress, whereas guided integration helped foster normalization, a sense of belonging, and adaptive identity development. Discussion: Findings indicate that mental health outcomes are influenced more by how influencer content is interpreted and contextualized than by mere exposure. The institutional response plays a key role in determining whether this leads to the normalization of mental health issues and help-seeking behaviors, promoting support, or to the acceptance of overwork, unrealistic expectations, and relentless self-optimization, which can heighten pressure and psychological stress. Medical influencers act as informal agents of professional socialization, not neutral sources. Without institutional oversight, they may reinforce overwork, perfectionism, and productivity, increasing students' psychological strain. However, when integrated critically, they can normalize vulnerability, reflective practice, and mental health awareness. The key is whether institutions passively observe or actively shape these narratives. -

Background:While digital health technologies promise to reshape the medical journey, their potential might not be realized due to unforeseen implementation challenges. Notably, the future impact of artificial intelligence (AI) in virtual consultations has been poorly investigated. Objective:This study aims to explore, across 8 areas, the future impacts of a bespoke, co-designed AI tool for remote chronic obstructive pulmonary disease care from the perspectives of patients and health care professionals (HCPs) with the Futures Wheel (FW) method. It provides practical recommendations for conducting FW activities involving novel digital health tools. Methods:A pilot FW workshop was conducted with public and patient involvement members to gather feedback on the process. Subsequently, an exploratory, in-person FW workshop was conducted with 2 patients with chronic obstructive pulmonary disease and 2 HCPs who had previously been involved in the co-design of the bespoke AI tool. The central statement was as follows: "The bespoke AI tool is used in every virtual consultation." Participants identified first- and second-order consequences across the following 8 areas of impact: HCP-patient relationship impact, psychological impact, social impact, educational impact, legal impact, ethical impact, health care delivery impact, and technology impact. Each participant discussed their individual input to provide additional context. Results:Regarding the HCP-patient relationship, patients foresee the tool's impact as redefining the remote care dynamic with enhanced patient involvement, while HCPs identify its meaningful communication assistance. On the psychological impact, patients expect an enhanced level of empowerment and confidence, and HCPs anticipate improved understanding of patients' emotional well-being with the AI tool's assistance. As for social impacts, patients view the AI support as beneficial for social patient-HCP interactions, and HCPs foresee their workflow being enhanced with flexibility and collaboration. The AI's educational impacts are expected to include, from patients' perspectives, better familiarization of HCPs with individual patient cases and, from HCPs' perspectives, improved support for training, upskilling, and administrative tasks. On the legal front, patients identify limited risks associated with the tool, and HCPs expect its features to lead to safer practices, contingent on regulatory compliance. Provided integrity and ethical use, the tool's ethical impact is not perceived as significant by patients, while HCPs see its personalized features as leading to fair, individual remote assessments. Patients envision the AI tool's impact on health care delivery as fostering patient-centricity, and HCPs anticipate strengthened remote care processes. Technologically, patients forecast a significant improvement to the current system, requiring adequate investment and resources, while HCPs expect complementarity between human input, AI, and the current system. Conclusions:The plausible AI-driven future of remote chronic care is a nuanced one. The FW method indicated that a bespoke, co-designed AI tool can positively support virtual care delivery and remote interactions while indicating potential risks. These insights can inform strategies related to early planning, governance, and implementation considerations.

Anticipating from the start the various ways a research programme can succeed or fail can avoid problems later. Here’s how to do it. Anticipating from the start the various ways a research programme can succeed or fail can avoid problems later. Here’s how to do it.

Background:Primary health care (PHC) is critical for delivering accessible and continuous care but faces persistent challenges such as workforce shortages, administrative burden, and rising multimorbidity. Artificial intelligence (AI) has the potential to support PHC by enhancing diagnosis, workflow efficiency, and clinical decision-making. However, existing research often overlooks how AI tools function within the complex realities of primary care and how clinicians and patients experience them. Objective:This scoping review maps the landscape of AI applications in PHC, with a focus on empirical studies involving direct engagement from PHC stakeholders. The review emphasizes real-world settings, clinical workflows, and the alignment of AI tools with the values and complexity of generalist care. Methods:Following Joanna Briggs Institute methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, we searched PubMed, Web of Science, and Scopus databases up to April 13, 2024. Inclusion criteria were empirical, peer-reviewed studies published in English between January 2010 and April 2024, involving direct stakeholder interaction (general practitioners, nurses, or patients) in real-world PHC settings, evaluating AI applications (eg, diagnostics, workflow optimization, and documentation). Exclusions included algorithm-only validations, pediatric populations, secondary or tertiary care contexts not explicitly addressing PHC workflows, nonempirical research (eg, editorials or protocols), and non-English studies. We used thematic analysis to synthesize findings related to study aims, AI applications, and stakeholder roles. Results:Of 5224 identified records, 73 studies met the inclusion criteria. Studies were grouped into four main themes: (1) early intervention and decision support (n=21; 29%), (2) chronic disease management (n=16; 22%), (3) operations and patient management (n=12; 16%), and (4) acceptance and implementation experiences (n=24; 33%). AI tools frequently demonstrated strong technical accuracy, particularly in diagnostic decision support. However, implementation in routine practice was often limited by usability barriers, workflow misalignment, trust concerns, equity gaps, and financial constraints. Conclusions:Overall, AI holds significant potential to support PHC, especially when aligned with clinical reasoning, workflow needs, and relational care models. However, persistent implementation barriers such as usability challenges, training gaps, and workflow integration issues must be addressed. The evidence included in this review is limited by heterogeneity in study design and the predominance of small-scale feasibility studies. Future research should prioritize pragmatic trials, co-design with PHC professionals, and anticipatory planning using future methods to ensure responsible and equitable implementation.

Introduction: Generative artificial intelligence (AI) is rapidly transforming medical education, while medical students continue to face high levels of stress, anxiety, and burnout. This dual pressure stemming from technological disruption and psychological vulnerability raises urgent questions about the future of training physicians. Introduction: Generative artificial intelligence (AI) is rapidly transforming medical education, while medical students continue to face high levels of stress, anxiety, and burnout. This dual pressure stemming from technological disruption and psychological vulnerability raises urgent questions about the future of training physicians. Methods: We used scenario analysis, a foresight methodology, to explore possible futures at the intersection of generative AI and medical students’ mental health. The exploratory scanning identified weak signals and emerging trends, which were clustered using a macro-meso-micro framework and interpreted through a multi-level perspective of socio-technical change. Scenarios were analyzed along two key dimensions: the extent of generative AI integration into medical curricula and the availability of mental health support. Results: Four distinct scenarios emerged: Analogue Happiness (high support, low AI integration), Gen AI Paradise (high support, high integration), Disconnected Struggles (low support, low integration), and Gen AI Takeover (low support, high integration). Each illustrates different risks and opportunities for students’ digital readiness and psychological well-being. Discussion: The findings suggest that technological innovation and mental health support must co-evolve in medical education. Prioritizing one without the other risks producing either digitally unprepared or emotionally fragile physicians. Faculty readiness, ethical frameworks, and participatory curriculum design are critical to ensuring balanced integration. We formulated practical recommendations tailored to students, educators, and other stakeholders to guide balanced adaptation. Conclusions: Generative AI is not an optional add-on but a transformative force, while mental health support is a prerequisite for competence. Institutions must rise to the dual responsibility of preparing students to become both digitally fluent and emotionally resilient. Failing to integrate these pillars constitutes not only a missed opportunity but an ethical failure with consequences for students, educators, healthcare professionals, and ultimately, patients.

Researchers and health care institutions have increasingly applied structured futures methods—such as the futures wheel, scenario analysis, forecasting, and horizon scanning—to systematically explore, generate, and prepare for multiple possible futures. However, discussions around the future of medicine, specialties, or therapeutic areas have often relied on the subjective opinions or perspectives of key opinion leaders rather than on future strategies, policies, visions, and scenarios that are grounded in rigorous and established methods. This underscores the need for futures methods to be widely adopted and effectively incorporated into both medical practice and health care policymaking. Integrating structured foresight techniques into strategic planning enables clinicians and policymakers to transition from reactive decision-making to proactive, plausible approaches that shape a more resilient and adaptive health care system. Our goal with this paper is to provide a methodological guide that is supported by case studies, demonstrating how futures methods can be systematically applied in health care. By offering practical examples, we intend to empower medical professionals, health care leaders, researchers, patients, and policymakers with the tools to anticipate and navigate future challenges and opportunities more effectively.

In the 21st century, health care has been going through a paradigm shift called digital health. Due to major advances and breakthroughs in information technologies, most recently artificial intelligence, the patriarchy of the doctor-patient relationship has started evolving toward an equal-level partnership with initial signs of patient autonomy. Being an underused resource for centuries, patients have started to contribute to their care with information, data, insights, preferences, and knowledge. It is important to recognize that at its core, digital health represents a cultural transformation, where patient empowerment has likely played the most significant role in driving these changes. This viewpoint paper traces the remarkable journey of patient empowerment from its nascent stages to its current prominence in shaping health care’s future. Spanning over two and a half decades, we explore pivotal moments and technological advancements that have revolutionized the patient’s role in health care. We dive into a few historical milestones, mainly in the United States, that have challenged and redefined societal norms around agency, drawing parallels between patient empowerment and broader social movements, such as the women’s suffrage and civil rights movements. Through these lenses, we argue that patient empowerment is not solely a function of knowledge or technology but requires a fundamental shift in societal attitudes, policies, health care culture, and practices. As we look to the future, we posit that the continued empowerment of patients will play a pivotal role in the development of more equitable, effective, and personalized health care systems. This paper calls for an ongoing commitment to fostering environments that support patient agency, access to resources, and the realization of patient potential in navigating and contributing to their health outcomes with an emphasis on the emerging significance of patient design.

Digital health technologies such as artificial intelligence (AI) and remote care have led to a reshaping of the medical journey, potentially benefiting every stakeholder in this landscape. However, the potential of these technologies might not be realised due to unforeseen challenges ranging from human factors to technical limitations. This highlights the importance of developing an anticipatory mindset to prepare stakeholders for the adoption of digital health technologies. In particular, the future impacts of AI assistance in remote chronic care remain underexplored. Structured foresight methods such as the Futures Wheel (FW) can aid stakeholders in better anticipating the direct and indirect impacts of an AI-enhanced future of chronic care. This study explores, across eight areas, the future impacts of a bespoke, co-designed AI tool for remote chronic care from the perspectives of patients and healthcare professionals (HCPs). It also shares practical recommendations on conducting FW activities involving novel digital health tools. An exploratory, in-person FW workshop was conducted with four participants (two patients and two HCPs) who had previously been involved in the co-design of the bespoke AI tool. The central statement was as follows: “The bespoke AI tool is used in every virtual consultation.”. The participants identified first- and second-order consequences across the following eight areas of impact: HCP-patient relationship impact, Psychological Impact, Social Impact, Educational Impact, Legal Impact, Ethical Impact, Healthcare delivery Impact, and Technology Impact. Each participant discussed their individual input to provide additional context. Participants anticipated that the co-designed AI tool could positively reimagine the therapeutic relationship in remote care settings. Patients emphasised that its assistance could act as a source of emotional reassurance and empowerment. HCPs foresaw that the tool could add efficiency to their workflow. However, the participants identified potential data integrity and regulatory compliance challenges, highlighting the need for adequate safeguards for implementing such tools in practice. The plausible AI-driven future of remote chronic care is a nuanced one. The FW method indicated that a bespoke, co-designed AI tool can positively support virtual care delivery and remote interactions while indicating potential risks. These insights can inform strategies around early planning, governance, and implementation considerations.

BackgroundThe rapid progress in the development of artificial intelligence (AI) is having a substantial impact on health care (HC) delivery and the physician-patient interaction. ObjectiveThis scoping review aims to offer a thorough analysis of the current status of integrating AI into medical practice as well as the apprehensions expressed by HC professionals (HCPs) over its application. MethodsThis scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines to examine articles that investigated the apprehensions of HCPs about medical AI. Following the application of inclusion and exclusion criteria, 32 of an initial 217 studies (14.7%) were selected for the final analysis. We aimed to develop an attitude range that accurately captured the unfavorable emotions of HCPs toward medical AI. We achieved this by selecting attitudes and ranking them on a scale that represented the degree of aversion, ranging from mild skepticism to intense fear. The ultimate depiction of the scale was as follows: skepticism, reluctance, anxiety, resistance, and fear. ResultsIn total, 3 themes were identified through the process of thematic analysis. National surveys performed among HCPs aimed to comprehensively analyze their current emotions, worries, and attitudes regarding the integration of AI in the medical industry. Research on technostress primarily focused on the psychological dimensions of adopting AI, examining the emotional reactions, fears, and difficulties experienced by HCPs when they encountered AI-powered technology. The high-level perspective category included studies that took a broad and comprehensive approach to evaluating overarching themes, trends, and implications related to the integration of AI technology in HC. We discovered 15 sources of attitudes, which we classified into 2 distinct groups: intrinsic and extrinsic. The intrinsic group focused on HCPs’ inherent professional identity, encompassing their tasks and capacities. Conversely, the extrinsic group pertained to their patients and the influence of AI on patient care. Next, we examined the shared themes and made suggestions to potentially tackle the problems discovered. Ultimately, we analyzed the results in relation to the attitude scale, assessing the degree to which each attitude was portrayed. ConclusionsThe solution to addressing resistance toward medical AI appears to be centered on comprehensive education, the implementation of suitable legislation, and the delineation of roles. Addressing these issues may foster acceptance and optimize AI integration, enhancing HC delivery while maintaining ethical standards. Due to the current prominence and extensive research on regulation, we suggest that further research could be dedicated to education.

For chronic condition management, virtual consultations are increasingly becoming expected means of care provision for their accessibility and improved convenience. Despite their rising adoption, challenges persist in maintaining effective interactions, encouraging patient engagement and supporting comprehensive clinical assessments. Emerging evidence highlights the transformative potential of artificial intelligence (AI) to support virtual care and enhance the user experience. This study presents the preliminary findings of an ongoing, multi-stage doctoral research investigating the integration of AI technologies in virtual consultations for chronic conditions with an outlook on future practices. It discusses the findings of a published scoping review, conducted in the first stage that informed the second stage, a mixed-methods investigation into the user experience of virtual consultations and their views on AI assistance. This involved semi-structured interviews (n = 10), focus groups (n = 15), and an ongoing survey (n = 83) in the Northwest region of Ireland. Emerging themes from the qualitative component indicate suboptimal quality of virtual interactions, a preference for hybrid care models, the requirement for empathy in remote care delivery, the need for holistic remote care with technological optimization and cautious AI support. These themes have informed potential evidence-based AI solutions such as personalized conversation support, insights from wearable data and personalized consultation summaries. An early proof-of-concept incorporating these features has been developed and this study outlines the subsequent steps for co-design workshops with scenario analyses to refine the prototype and explore its impact in current and future practice. By integrating rigorous mixed-methods research with novel medical futures methodology, this study aims to uncover the nuanced factors of current virtual consultations, address the relevant challenges with an evidence-based AI tool and provide actionable insights for the integration of emerging technologies in chronic care.

Background Despite a recent rise in adoption, telemedicine consultations retention remains challenging, and aspects around the associated experiences and outcomes remain unclear. The need to further investigate these aspects was a motivating factor for conducting this scoping review. Objective With a focus on synchronous telemedicine consultations between patients with nonmalignant chronic illnesses and health care professionals (HCPs), this scoping review aimed to gain insights into (1) the available evidence on telemedicine consultations to improve health outcomes for patients, (2) the associated behaviors and attitudes of patients and HCPs, and (3) how supplemental technology can assist in remote consultations. Methods PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guided the scoping review process. Inclusion criteria were (1) involving adults with nonmalignant, noncommunicable chronic conditions as the study population; (2) focusing on health outcomes and experiences of and attitudes toward synchronous telemedicine consultations between patients and HCPs; and (3) conducting empirical research. A search strategy was applied to PubMed (including MEDLINE), CINAHL Complete, APA PsycNet, Web of Science, IEEE, and ACM Digital. Screening of articles and data extraction from included articles were performed in parallel and independently by 2 researchers, who corroborated their findings and resolved any conflicts. Results Overall, 4167 unique articles were identified from the databases searched. Following multilayer filtration, 19 (0.46%) studies fulfilled the inclusion criteria for data extraction. They investigated 6 nonmalignant chronic conditions, namely chronic obstructive pulmonary disease, diabetes, chronic kidney disease, ulcerative colitis, hypertension, and congestive heart failure, and the telemedicine consultation modality varied in each case. Most observed positive health outcomes for patients with chronic conditions using telemedicine consultations. Patients generally favored the modality’s convenience, but concerns were highlighted around cost, practical logistics, and thoroughness of clinical examinations. The majority of HCPs were also in favor of the technology, but a minority experienced reduced job satisfaction. Supplemental technological assistance was identified in relation to technical considerations, improved remote workflow, and training in remote care use. Conclusions For patients with noncommunicable chronic conditions, telemedicine consultations are generally associated with positive health outcomes that are either directly or indirectly related to their ailment, but sustained improvements remain unclear. These modalities also indicate the potential to empower such patients to better manage their condition. HCPs and patients tend to be satisfied with remote care experience, and most are receptive to the modality as an option. Assistance from supplemental technologies mostly resides in addressing technical issues, and additional modules could be integrated to address challenges relevant to patients and HCPs. However, positive outcomes and attitudes toward the modality might not apply to all cases, indicating that telemedicine consultations are more appropriate as options rather than replacements of in-person visits.

Introduction: Rapid advancements in artificial intelligence (AI) development are significantly impacting the medical field. Understanding AI's application in primary healthcare (PHC) is crucial for maximizing its benefits in this heavily overburdened branch of medicine. This review aims to synthesize the current state of AI implementation and integration in general practice (GP). Methods: This scoping review follows the PRISMA-ScR guidelines to screen articles that explore the intersection of AI and primary care. By employing a comprehensive approach, we captured the possible range of AI applications and their implications in PHC. After applying inclusion and exclusion criteria, 107 studies were included in the final analysis. Results: Thematic analysis revealed three main subtopics. The first theme involves practical and potential usage of this technology, covering administrative tasks, drug management, and disease screening and diagnosis across various fields such as cardiology, dermatology, and ophthalmology. The second theme examines the reactions of the members of healthcare to AI integration, highlighting both enthusiasm and concerns. The third theme addresses broader considerations, including equity, ethics, and research and implementation frameworks. Discussion: The envisioned uses of AI in PHC align with current efforts, including administrative support, diagnostics, and practice management. However, several key issues require resolution to ensure seamless implementation and equity in AI applications, such as incorporating AI into daily practice, preparing electronic health systems, and maintaining equity while enhancing GP. Conclusions: AI integration in PHC shows promising, albeit small-scale, findings. With further research and best practices, AI has the potential to significantly support and enhance primary care. Common research areas include administrative support via generative AI, triage, and diagnostic aid. Healthcare professionals often exhibit ambivalence towards AI in PHC, but with further studies, best practices, and active involvement, AI can substantially support this crucial branch of medicine.

Rising adoption of virtual consultations for patients with chronic conditions in recent years have highlighted their need to be explored and optimised with novel means. These are being undertaken in this on-going interdisciplinary PhD which combines human computer interaction (HCI), artificial intelligence (AI) development and innovation with a participatory approach. This study adopts a multi-stage design inferred by the Integrate Design Assess and Share (IDEAS) framework and includes Public and Patient Involvement (PPI) input to enhance the study design. Firstly, a scoping review was conducted based on the PRISMA-ScR guidelines. The on-going second stage adopts qualitative methodologies to gain insights into the lived experiences and challenges of patients and healthcare professionals when engaged in virtual consultations. These findings will inform the iterative development of an adequate AI tool in the third stage. Initial findings from the upcoming publication of the full scoping review generally indicate positive virtual consultation experiences and identified key areas of improvement that inform the focus of the qualitative study and the design of interview guides. Potential AI applications include rule-based conversational agents and decision support systems. While on-going, this timely study provides crucial insights into the HCI elements of virtual consultations and explores novel AI means to enhance such interactions.