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Purpose Several review articles have been published within the Artificial Intelligence (AI) literature that have explored a range of applications within the tourism and hospitality sectors. However, how efficiently the applied AI methods and algorithms have performed with respect to the type of applications and the multimodal sets of data domains have not yet been reviewed. Therefore, this paper aims to review and analyse the established AI methods in hospitality/tourism, ranging from data modelling for demand forecasting, tourism destination and behaviour pattern to enhanced customer service and experience. Design/methodology/approach The approach was to systematically review the relationship between AI methods and hospitality/tourism through a comprehensive literature review of papers published between 2010 and 2021. In total, 146 articles were identified and then critically analysed through content analysis into themes, including “AI methods” and “AI applications”. Findings The review discovered new knowledge in identifying AI methods concerning the settings and available multimodal data sets in hospitality and tourism. Moreover, AI applications fostering the tourism/hospitality industries were identified. It also proposes novel personalised AI modelling development for smart tourism platforms to precisely predict tourism choice behaviour patterns. Practical implications This review paper offers researchers and practitioners a broad understanding of the proper selection of AI methods that can potentially improve decision-making and decision-support in the tourism/hospitality industries. Originality/value This paper contributes to the tourism/hospitality literature with an interdisciplinary approach that reflects on theoretical/practical developments for data collection, data analysis and data modelling using AI-driven technology.

As he lay in a coma, neurosurgeon Eben Alexander explains that he \"journeyed beyond this world and encountered an angelic being who guided him into the deepest realms of super-physical existence [where] he met and spoke with the Divine source of the universe itself\"--P. [4] of cover.

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. 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. 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. 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. 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.

As oncology workflows integrate increasingly autonomous artificial intelligence (AI) agents, health systems face uncertainty regarding operational impacts. Traditional linear forecasting methods fail to capture second-order effects such as governance saturation, induced demand, and bottleneck migration. To navigate this complexity, the emerging field of medical futures studies requires methodologies that bridge qualitative strategic foresight with quantitative operational modeling. These system-level dynamics directly influence timely diagnosis, treatment delays, and overall health system resilience. This study aimed to develop a proof-of-concept framework coupling qualitative scenario planning with computational discrete-event simulation to stress-test oncology AI adoption strategies. We defined a strategic state space using 2 orthogonal axes, AI automation intensity and data interoperability, resulting in 4 distinct futures scenarios. We translated these qualitative narratives into a quantitative discrete-event simulation model of a 3-year operational horizon. The model quantified system performance (referral-to-treatment interval [RTTI] and throughput), volatility, and resource constraints across different adoption trajectories. The scenario-planning phase yielded 4 operational archetypes (analog oncology, automation islands, interconnected clinicians, and AI-orchestrated care) with distinct constraints, risks, and failure modes. In the simulation, the fully integrated scenario maximized capacity (1244, SD 21.4 patients per year) and halved the mean RTTI to 14.9 (SD 0.3) days, a magnitude comparable to major pathway redesign interventions. Isolated automation without data infrastructure led to reduced system performance, increasing RTTI by 26% (37.1, SD 1.3 days) and reducing throughput to 647 (SD 10.1) patients per year due to administrative governance saturation. The model illustrated a structural bottleneck migration: successful upstream AI adoption shifted binding constraints from diagnostic scanners to downstream chemotherapy infusion units, whereas missing data interoperability resulted in governance constraints. Pathway optimization analysis indicated that a coordinated strategy prioritizing early improvements in data interoperability reduced transition volatility compared to an automation-first approach. Integrating qualitative scenario planning with quantitative simulations enabled a systematic evaluation of oncology AI adoption strategies. As a proof of concept, it offers a replicable framework for health leaders to model future scenarios of digital transformation in times of high uncertainty. Subsequent work should expand this methodology to incorporate financial and health equity dimensions, establishing simulation-based scenario planning as an important tool in medical futures studies.

\"In the late 1960s the world was faced with impending disaster: the height of the Cold War, the end of oil and the decline of great cities throughout the world. Out of this crisis came a new generation that hoped to build a better future, influenced by visions of geodesic domes, walking cities and a meaningful connection with nature. In this brilliant work of cultural history, architect Douglas Murphy traces the lost archeology of the present day through the works of thinkers and designers such as Buckminster Fuller, the ecological pioneer Stewart Brand, the Archigram architects who envisioned the Plug-In City in the '60s, as well as co-operatives in Vienna, communes in the Californian desert and protesters on the streets of Paris. In this mind-bending account of the last avant-garde, we see not just the source of our current problems but also some powerful alternative futures\"-- Provided by publisher.

The integration of artificial intelligence (AI) into clinical medicine presents a persistent paradox: diagnostic models routinely demonstrate benchmark superiority over human experts, yet bedside adoption remains fragile, and clinician trust is low. Conventional forecasting approaches—projecting model performance along optimistic trend lines—are epistemologically insufficient because they cannot account for the nonlinear sociotechnical transitions that separate technical capability from institutional trust. This Viewpoint applies backcasting, a normative futures methodology with a 4-decade evidence base in energy policy and public governance, to the specific challenge of clinician adoption of AI diagnostics, with the aim of identifying the structural interventions required to achieve durable trust by 2040. Consistent with the tradition of single-expert normative foresight analysis, we applied backcasting as a structured reasoning framework using a STEEP (social, technological, economic, environmental, and political) analysis. Sources from PubMed, IEEE Xplore, Google Scholar, and policy repositories (the US Food and Drug Administration, World Health Organization, Organisation for Economic Co-Operation and Development, and European Commission) published between 2010 and 2025 were reviewed; barriers and enablers were coded across STEEP dimensions to identify pivot points representing convergent, time-bound structural changes. Working backward from a defined 2040 vision state—a health care ecosystem with risk-stratified clinician trust thresholds, semantic transparency of AI outputs, integrated AI governance, and futures literacy in medical education—we identified three temporal pivot points: (1) the 2030 standardization of dual-process AI architectures, in which large language models are verified in real time by locally deployed small language models, producing a calibrated confidence score; (2) the 2035 institutionalization of agentic AI orchestration governed by a formally designated chief AI officer; and (3) the 2040 integration of futures literacy and human-AI teaming competencies into standard medical curricula. The AI trust gap is an institutional design problem, not a technical inevitability. Backcasting reframes the central question from “when will AI be ready for medicine?” to “what must we build to make medicine ready for AI?” The 3 pivot points identified here—verifiable AI by 2030, agentic governance by 2035, and futures literacy by 2040—are structural commitments that clinicians, health system leaders, and policymakers can begin building today.

\"Two defense experts explore the collision of war, politics, and social media, where the most important battles are now only a click away\"-- Provided by publisher.

Medical education must evolve to prepare health care professionals for a rapidly changing world. Beyond digital literacy, clinicians must develop new competencies to navigate global megatrends, including shifting disease burden, technological advancements, climate change, and demographic shifts. The future job market will introduce novel roles, and educational institutions must remain adaptable to meet the evolving motivations and expectations of students. Megatrends, broad, transformative forces shaping societies, present both challenges and opportunities for health care education. The present work seeks to understand the implications of megatrends for medical education and explore the use of scenario analysis for curriculum design. A participatory design approach was employed to conduct a scenario analysis workshop at Trinity College Dublin's School of Medicine in October 2024. Digital connectivity and climate change were selected as key drivers. Participants included medical educators, policymakers, clinicians, and students. Interactive methods such as group discussions, structured boards, and physical cards were utilized to facilitate data collection. Insights were analyzed thematically to identify critical competencies, mindsets, and structural requirements for future medical education. The scenario analysis revealed key competencies and mindsets necessary for future health care professionals. Essential competencies included complex adaptive systems thinking, patient-centeredness, continuous learning, and participatory health, while essential mindsets encompassed sustainability, prevention-focused care, and technological adaptability. Cross-scenario reflections highlighted the increasing need for interdisciplinary collaboration, ethical leadership, and curriculum flexibility. Actionable steps were identified, including integrating sustainability and digital health into curricula, fostering emotional intelligence in student selection, and incorporating adaptive learning models. This study demonstrates the value of participatory design in shaping medical education to align with global megatrends. The findings align with existing foresight research by organizations such as the World Health Organization and the European Commission, emphasizing the need for health care professionals to balance technological proficiency with human-centered care. While the study was limited to a single institutional setting, its insights provide a framework for other medical schools to anticipate future challenges and proactively reform curricula. Future research should explore multi-institutional applications and longitudinal studies to validate these findings.