Search Results (98)

To provide a clinically oriented overview of ultrasound elastography in chronic liver disease, focusing on currently available techniques, their clinical applications, quality assurance, technical pitfalls, and emerging developments that may shape future liver imaging practice. A narrative review of the current literature was conducted using international guidelines, systematic reviews, and original studies retrieved from major scientific databases. Particular emphasis was placed on the clinical applications of ultrasound elastography, quality assurance procedures, interpretation of liver stiffness measurements, and future technological developments. Vibration-controlled transient elastography (VCTE) and shear wave elastography (SWE) have become established non-invasive techniques for liver fibrosis assessment, demonstrating excellent diagnostic performance, particularly for advanced fibrosis and cirrhosis. Beyond fibrosis staging, ultrasound elastography contributes to prognostic stratification, treatment planning, and longitudinal disease monitoring across a broad spectrum of chronic liver diseases. However, liver stiffness measurements may be influenced by technical and biological confounding factors, including inflammation, cholestasis, hepatic congestion, obesity, and postprandial status, highlighting the importance of standardised acquisition protocols and careful clinical interpretation. Emerging developments, including spleen stiffness assessment, multiparametric ultrasound, and artificial intelligence-assisted image analysis, are expected to further improve diagnostic accuracy and support precision hepatology. Ultrasound elastography has become an essential component of the non-invasive evaluation of chronic liver disease, substantially reducing the need for liver biopsy while improving fibrosis staging, prognostic stratification, and longitudinal patient monitoring. Its optimal clinical implementation requires appropriate patient selection, adherence to quality assurance procedures, and careful interpretation within the broader clinical context. Future advances in multiparametric ultrasound, artificial intelligence, and international standardisation are expected to further strengthen the role of ultrasound elastography within precision hepatology and personalised liver disease management. Ultrasound elastography should be integrated into routine liver imaging as part of a multimodal diagnostic approach. Standardised acquisition protocols, continuous quality assurance, and appropriate operator training are essential to ensure reliable and reproducible liver stiffness measurements. Radiographers play a key role in examination quality, protocol adherence, and multidisciplinary patient care, contributing to accurate diagnosis and improved clinical decision-making. Full article

The use of Artificial Intelligence (AI) in Formative and Shared Assessment (F&SA) processes refers to the application of AI-based technologies to support formative and continuous assessment in Higher Education (HE). F&SA systems involve the ongoing monitoring of students’ learning, the provision of feedback that enables them to regulate and improve their performance, and the collection of information that informs the continuous improvement of teaching practice. In this context, AI can serve a dual purpose: when orientated towards students, it enhances learning outcomes; when directed at educators, it supports the development of their pedagogical expertise through tools designed to assist in the creation of assessment instruments, the generation of automated feedback, the analysis of learning data, and the design of simulation environments that foster the development of professional competencies. The integration of AI into F&SA practices holds considerable potential to transform traditional assessment approaches by enabling more personalised, adaptive, and timely feedback for both students and educators. In this shared assessment framework, students may likewise draw on AI applications to support specific dimensions of their learning, including academic writing, knowledge organisation, and the generation of educational content, thereby becoming active participants in their own assessment processes. However, the incorporation of AI into F&SA also requires careful consideration of the pedagogical, ethical, and institutional challenges it entails, particularly those related to academic integrity, cognitive offloading, and the responsible use of AI tools. It is therefore essential to promote AI literacy in HE among both faculty members and students, fostering a critical and informed engagement with these technologies that ensures the pedagogical relationship, along with the shared, formative nature of assessment, remains at the core of meaningful learning processes. Full article

Background: Pre-travel health consultations require individualised risk assessment across itinerary, destination, traveller characteristics, vaccine and medication history, comorbidities, pregnancy and immune status, activities, and access to care. Artificial intelligence (AI), particularly large language models (LLMs), may support pre-consultation education, structured history collection, guideline retrieval, multilingual communication and post-consultation reinforcement, but unsafe use may introduce hallucinated, outdated or insufficiently personalised recommendations. Objectives: This scoping review maps the current evidence on AI tools relevant to pre-travel health consultations, characterises implementation gaps, identifies patient-safety risks and proposes a supervised implementation model for travel medicine clinics. Original contribution: Unlike previous reviews of clinical AI, patient-education LLMs or chatbots in chronic illness, this is the first scoping review focused specifically on AI in pre-travel consultations. It uniquely combines a five-tier evidence hierarchy that separates direct travel-medicine AI evidence from indirect clinical-AI safety and equity evidence, and provides a travel-medicine-specific clinical safety risk taxonomy and a supervised implementation framework anchored to authoritative travel-medicine guidance and current AI regulatory regimes. Methods: A scoping review was conducted following PRISMA-ScR reporting, using a Population–Concept–Context eligibility framework and a targeted retrieval in May 2026 covering January 2017 to May 2026. Sources were screened and charted by a single reviewer using a structured eligibility checklist. Quality and applicability were appraised conceptually using MMAT, AMSTAR 2 and JBI text-and-opinion criteria, with GRADE-informed certainty. Results: Of 70 records identified, 11 were included: four direct pre-travel AI sources, one adjacent travel-related decision-support study, four guideline and context sources and two clinical LLM safety sources. The only patient-level implementation involved 26 travellers using a GPT-4 Travel Clinic Assistant in Singapore, where physicians and travellers reported acceptability and workflow benefit but objective effectiveness outcomes were not measured. Broader clinical LLM evidence indicates heterogeneous evaluation methods, vulnerability to hallucinated guidelines, and accuracy that varies widely across model versions and specialties. Conclusions: Current evidence supports supervised AI augmentation of pre-travel consultations but does not support autonomous AI-led vaccine selection, malaria prophylaxis, contraindication screening or individualised travel-risk clearance. Near-term deployment should be restricted to clinician-supervised education, structured intake, source-grounded guideline retrieval, after-visit reinforcement and escalation-triggered workflow support. Priority research includes travel-medicine-specific hallucination audits; equity testing in visiting-friends-and-relatives, migrant, older-adult, First Nations Australian, and Pacific Islander travellers; and prospective trials reported under CONSORT-AI, SPIRIT-AI and TRIPOD + AI. Full article

Endometriosis is a chronic, debilitating condition affecting approximately 10–15% of reproductive-aged women and it is often associated with significant diagnostic delays due to its heterogeneity and unreliable non-invasive tests. Artificial intelligence (AI) offers innovative methods for improving endometriosis diagnosis, prognosis and research via advanced pattern recognition and data analysis capabilities. The integration of AI in diagnostic workflow has the potential to improve efficiency, accuracy, and patient outcomes. This review summarises current developments of AI—including machine learning, deep learning, and natural language processing—in the diagnostic workflow of endometriosis. It analyses different fields of diagnostics ranging from AI-assisted imaging in detection of pouch of Douglas to multi-omics biomarkers assisting the clinical decision process. AI can enhance accuracy, reducing diagnostic delays and supporting personalised treatment planning. However, there are multiple limitations, such as small datasets, overfitting, and lack of external validation and variability. Further research and evaluation are required before it can be implemented into healthcare systems. AI holds promise as a non-invasive, scalable adjunct to current diagnostics, potentially reducing the economic and personal burden endometriosis carries. Full article

Artificial intelligence systems increasingly mediate high-stakes human activities, yet urban navigation remains highly challenging for blind and visually impaired individuals. Although digital navigation technologies have significantly improved route planning and accessibility, many existing systems still rely on generic interaction paradigms that insufficiently account for cognitive load, contextual uncertainty, and the adaptive needs of vulnerable users. This challenge highlights the importance of Human-Centred AI approaches capable of supporting not only functional accessibility, but also cognitively sustainable and trustworthy interaction. This paper introduces LAZAR, a human-centred adaptive AI framework for accessible urban mobility grounded in a user-centred design methodology and formalised through a structured Software Requirements Specification. Rather than focusing exclusively on route optimisation, LAZAR approaches assistive navigation as an adaptive human–AI interaction problem in which instructional granularity, interaction frequency, and feedback mechanisms are designed to support user autonomy and situational awareness whilst limiting unnecessary cognitive burden. The proposed framework integrates high-fidelity prototyping, accessibility-oriented interaction modelling, and a modular multi-agent architecture intended to support adaptive and personalised guidance. Central to the approach is a cognitive load-aware interaction layer designed to regulate the presentation and timing of navigational assistance according to user needs and contextual conditions. The proposed multi-agent architecture is presented as a modular design framework whose interaction principles and interface logic were partially operationalised in the evaluated prototype. The complete integration of all adaptive coordination mechanisms, together with large-scale real-world validation, remains part of ongoing and future development work. This work contributes a structured methodology for the design of adaptive assistive AI systems that integrates accessibility requirements, human-centred interaction principles, and cognitively informed guidance strategies. A formative usability evaluation involving eleven visually impaired participants provides preliminary empirical evidence regarding usability, accessibility, and perceived usefulness of the proposed interaction model. The framework establishes a foundation for future research on inclusive and adaptive AI-based navigation systems in urban environments. Full article

Social robots are systems designed to assist people across different fields. During their operation, they have to interact with people with different characteristics and necessities. Consequently, correctly recognising the user interacting with the robot facilitates the generation of a personalised experience that satisfies the user’s needs. In robotics, user recognition is typically based on face recognition from image processing and datasets that require retraining the network to include new users. However, some robots, such as pet-like companions, often lack a camera due to reduced dimensions, limited computational resources, or privacy constraints. Additionally, robots can occasionally encounter new users, requiring online recognition to provide a personalised interaction experience. To address these limitations, this article presents a user recognition system based on voice biometrics and dynamic clustering for adaptive social robots. We evaluate a set of open-source models for voice biometric extraction using different clustering algorithms to identify the best combination for our application. The resulting system is implemented in a pet-like robot companion that is used for the affective support of older adults, demonstrating its capacities in a real-world scenario. The system achieves more than $73\%$ accuracy in recognising users who had previously spoken to the robot and more than $71\%$ success in recognising new users who had not previously interacted with the robot and creating a personal profile for them. However, the system still detects noise, especially when the speaker has never interacted with the robot. Full article

Massively Multiplayer Online Role-Playing Games (MMORPGs) increasingly rely on player-developed third-party tools to extend functionality and personalise gameplay, creating a complex software ecosystem that introduces both usability benefits and security risks. This study investigates whether such tools can be exploited as an attack vector for cybercrime by designing and implementing a proof-of-concept add-on within a widely deployed commercial MMORPG using its native scripting and application programming interface. The developed tool supports automated player discovery, chat capture, target inspection, and local data persistence, enabling a systematic evaluation of how cyber-assisted and cyber-dependent crimes could be facilitated within the game client. Empirical testing demonstrates that while the platform’s protected execution model and interface restrictions prevent direct credential theft and remote code execution, the add-on architecture allows extensive behavioural data collection and social-engineering-relevant monitoring, making several forms of cyber-enabled crime technically feasible. These findings show that MMORPG add-on frameworks represent a non-trivial socio-technical attack vector in next-generation online platforms, where security depends not only on code isolation, but also on how user-generated extensions interact with human behaviour. The results highlight the need for architecture-aware security controls and governance mechanisms to mitigate emerging threats in large-scale, extensible virtual environments. Full article

Objectives: Despite the widespread availability of digital clinical information, timely access to relevant biomedical evidence during routine consultations remains limited in practice. Primary care clinicians, in particular, face significant time constraints that make it difficult to integrate comprehensive literature searches into everyday workflows. This study evaluates whether an ontology-based recommender system can support routine clinical workflows by reducing information retrieval time while preserving the clinically acceptable usefulness of retrieved evidence. We assessed the performance of the HOPE (Health Operation for Personalised Evidence) system compared with realistic manual PubMed searches conducted by physicians. Materials and Methods: We conducted an observational evaluation involving 50 primary care physicians, who independently assessed 30 anonymised, rewritten clinical cases representative of common primary care scenarios. HOPE automatically extracted biomedical concepts from case descriptions using natural language processing and mapped them to Unified Medical Language System (UMLS) ontologies to generate ranked PubMed recommendations. A subset of 10 physicians also conducted manual PubMed searches in line with their usual clinical practice. Article relevance was assessed using a predefined binary criterion, and a reference relevance set was established by consensus among three senior physicians using a pooled document set. Retrieval performance was evaluated using Precision@k, relative Recall@k, and Normalised Discounted Cumulative Gain (NDCG@k). Manual search time was measured using a standardised stopwatch protocol, whereas HOPE response time was logged automatically by the system. Results: Inter-physician agreement in relevance assessment was substantial (Fleiss’ κ = 0.66; 95% CI: 0.61–0.70). HOPE achieved moderate-to-high precision within the top-ranked results (Precision@3 = 0.72), with relative recall increasing as additional documents were considered. Ranking metrics indicated that relevant articles were generally positioned early in the result lists. The mean total retrieval time for manual PubMed searches was 13.3 ± 1.7 min per case, compared with 17.4 ± 2.1 s for HOPE-assisted retrieval (p < 0.001). Conclusions: In a controlled, workflow-oriented evaluation using synthetic clinical cases, HOPE substantially reduced information retrieval time while maintaining clinically acceptable relevance in the retrieved literature. These findings support the use of ontology-based, AI-assisted systems as workflow-support tools to facilitate timely access to biomedical evidence, without replacing clinical judgment. Full article

Overweight and obesity during pregnancy are associated with increased maternal and neonatal risks, yet scalable interventions addressing the psychological processes underlying health behaviours remain limited. This study describes the development and formative evaluation of DEMETRA, a chatbot delivering an Acceptance and Commitment Therapy (ACT)-informed intervention to promote healthier lifestyles in pregnant women. In line with Phase 1 of the Obesity-Related Behavioral Intervention Trials framework, a multidisciplinary team developed a six-session digital program delivered via a rule-based virtual assistant. A mixed-methods design was employed to assess acceptability, usability, and perceived relevance among a heterogeneous stakeholder sample. Sixteen stakeholders (psychologists, communication experts, nutritionists, clinicians, and non-overweight, expectant women or those who had recently delivered) participated in iterative testing; 15 completed quantitative measures (Semantic Differential scales, uMARS, BUS-11) and 16 completed semi-structured interviews. Non-parametric analyses indicated significantly positive evaluations across most communication and content domains, particularly clarity and language appropriateness, whereas session duration and several engagement-related dimensions did not significantly differ from neutrality. Qualitative findings confirmed strengths in clarity, non-stigmatising tone, and multimedia support, while identifying limited personalisation and message pacing as key areas for refinement. Overall, findings provide formative evidence that ACT-informed principles can be translated into a chatbot-delivered antenatal program and highlight concrete priorities for optimisation (e.g., personalisation and message pacing). Because end-user testing did not include overweight/obese pregnant women and the sample was small and heterogeneous, conclusions regarding acceptability/feasibility in the intended clinical population remain preliminary; the results primarily support iterative refinement and subsequent proof-of-concept testing in the target group. Full article

This paper presents a low-cost, lightweight wearable sensing module for real-time multi-degree-of-freedom motion analysis, which is validated using ankle movements from a representative case study. The system is based on a compact inertial measurement unit integrated into a custom-made enclosure and employs Kalman filter-based sensor fusion to estimate three-dimensional joint orientation. An experimental campaign involving sixteen healthy participants was conducted, and measurements were compared against a gold-standard optical motion capture system, Optitrack V120 Trio. Ankle kinematics were analysed across all anatomical planes, including dorsiflexion/plantarflexion, inversion/eversion, and adduction/abduction. Quantitative metrics, including cosine similarity consistently above 0.98 across all movements and root mean square error within 4° on average, demonstrate strong agreement between the angular measuring device and motion capture data, with errors remaining within clinically acceptable limits. The results confirm the feasibility of the proposed system as a reliable, portable, and affordable alternative to laboratory-based measurement technologies. Beyond ankle assessment, the sensing approach is applicable to a wide range of motion-assistive and rehabilitation systems, supporting continuous monitoring, personalised therapy, and future integration into intelligent wearable devices. Full article

Background: Low-intake dehydration is frequent among institutionalised older adults and is associated with high morbidity–mortality and healthcare costs. Its prevention requires effective strategies and professional and institutional coordination. Objective: This study aims to explore the knowledge on the identification and prevention of dehydration, as well as the management of hydration by healthcare professionals and management in a nursing home. Methods: This exploratory qualitative study with a phenomenological approach convened two focus groups with 18 nurses and assistants alongside two semi-structured interviews with managers. The content analysis addressed five dimensions: knowledge; identification of dehydration; prevention of dehydration; barriers and facilitators; and actions proposed to improve hydration. Results: Participants recognised the importance of hydration but reported barriers including limited training, absence of specific protocols, and imprecise record systems. Facilitators included hydration reminders, improved accessibility to water, sensorial resources, promotion of independence, social activities, and institutional support for preventive strategies. Conclusions: These findings show that preventing and managing dehydration in nursing homes is complex and can be influenced by organisational and structural factors. The nursing team plays a central role in detecting dehydration early and implementing personalised strategies to promote fluid intake, while managerial support strengthens their effectiveness. Improving staff training, developing practical guidelines, and refining record systems may help address the identified barriers and enhance person-centred hydration management aligned with residents’ needs. Full article

Background: Total knee arthroplasty (TKA) is an effective procedure for symptomatic end-stage knee arthritis with good clinical and survivorship outcomes. However, up to 20% of patients report dissatisfaction following TKA. Recent studies have suggested that this may be at least partially due to suboptimal limb alignment or ligament imbalance. This study compared clinical outcomes at 1 year post-operatively (i.e., the 2011 Knee Society Score [KSS] and Forgotten Joint Score [FJS]) between two robotic-assisted personalised TKA techniques: functional alignment (FA) and an original technique combining restricted kinematic alignment (rKA) with a load sensor to achieve reliable ligament balancing (via bone re-cutting with a robotic arm). Methods: This single-centre, prospective, comparative study was performed at a robotic-assisted arthroplasty centre. The study population consisted of an FA group (43 patients) and rKA/sensor group (47 patients). Clinical outcomes were measured at 1 month post-operatively (visual analogue scale [VAS] pain score, flexion, range of motion [ROM], use of a mobility aid and stiffness) and at 1 year (2011 KSS, FJS, VAS, flexion and ROM). Results: There were no statistical significant differences in 2011 KSS or FJS at 1 year post-operatively between the two groups. Multivariate analysis showed no independent association of either technique with the 1-year follow-up KSS Objective Knee Indicators score (adjusted beta coefficient (aβ) = −2.371 [−7.380; 2.638], p = 0.357), KSS Patient Satisfaction score (aβ = −2.522 [−6.887; 1.842], p = 0.262), KSS Patient Expectations score (aβ = 0.629 [−0.928; 2.186], p = 0.431), KSS Functional Activities score (aβ = −3.399 [−10.881; 4.082], p = 0.377) or 1-year follow-up FJS (aβ = −5.168 [−19.887; 9.550], p = 0.494). Conclusions: There were no significant differences between the FA and rKA/load sensor groups in the 2011 KSS or FJS at 1 year post-operatively. To our knowledge, this is the first study to compare clinical outcomes between robotic-assisted FA TKA and rKA TKA. Clinical outcomes in the rKA/sensor group were similar to previous studies using rKA without robotic assistance or a load sensor. This was also the first report of the clinical outcomes of FA. The results need to be validated by larger scale studies to avoid potential type 2 errors. Full article

The use of technology in mobile devices and the integration of Artificial Intelligence offers a wide range of benefits and personalised services that help users perform countless activities that assist them in their daily lives, such as at work, school, and when communicating with friends and loved ones. However, this technological evolution poses significant challenges and risks in terms of user privacy, as the personal data and information that may be shared or stored must be taken into account in order to preserve the physical and psychological integrity of users. Balancing innovation and privacy is essential to maximise the benefits of AI on mobile devices while protecting the rights and security of users. This requires a comprehensive approach involving multiple stakeholders working together to create a secure, user-centred digital environment and implement security measures to preserve that data security. Full article

Background: Artificial intelligence (AI) is transforming medical practice, enhancing diagnostic accuracy, personalisation, and clinical efficiency. However, this transition raises complex ethical challenges related to transparency, accountability, fairness, and human oversight. This study examines how the literature conceptualises and distributes ethical responsibility in AI-assisted healthcare. Methods: This semi-systematic, theory-informed thematic review was conducted in accordance with the PRISMA 2020 guidelines. Publications from 2020 to 2025 were retrieved from PubMed, ScienceDirect, IEEE Xplore databases, and MDPI journals. A semi-quantitative keyword-based scoring model was applied to titles and abstracts to determine their relevance. High-relevance studies (n = 187) were analysed using an eight-category ethical framework: transparency and explainability, regulatory challenges, accountability, justice and equity, patient autonomy, beneficence–non-maleficence, data privacy, and the impact on the medical profession. Results: The analysis revealed a fragmented ethical landscape in which technological innovation frequently outperforms regulatory harmonisation and shared accountability structures. Transparency and explainability were the dominant concerns (34.8%). Significant gaps in organisational responsibility, equitable data practices, patient autonomy, and professional redefinition were reported. A multilevel ethical responsibility model was developed, integrating micro (clinical), meso (institutional), and macro (regulatory) dimensions, articulated through both ex ante and ex post perspectives. Conclusions: AI requires governance frameworks that integrate ethical principles, regulatory alignment, and epistemic justice in medicine. This review proposes a multidimensional model that bridges normative ethics and operational governance. Future research should explore empirical, longitudinal, and interdisciplinary approaches to assess the real impact of AI on clinical practice, equity, and trust. Full article

Background/Objectives: Parental mental health challenges are associated with parenting difficulties and child mental health issues. Parenting interventions can support families; however, parents with mental health challenges face barriers to accessing parenting support, which is not consistently offered within adult mental health settings. Embedding technology-assisted parenting programs into these settings could provide accessible, holistic support. Partners in Parenting Kids (PiP Kids) is a digital parenting program designed to prevent child anxiety and depression, yet its suitability for parents with mental health challenges and fit within mental health services remains unclear. This study aimed to co-design and adapt PiP Kids for future implementation in an Australian adult mental health service. Methods: Parents who recently sought mental health support (n = 8) and service providers (n = 7) participated in co-design workshops to explore needs and preferences for a technology-assisted parenting program and iteratively develop a prototype. Parents (n = 3) trialled the online component of the prototype and participated in qualitative interviews to assess acceptability. Results: The adapted clinician-supported program was designed to facilitate (1) parent and clinician readiness for parenting support; (2) emotional and social support for parents and clinicians; (3) practical, personalised parenting knowledge; (4) parent-led empowerment; and (5) accessible, integrated support. Prototype clinician training was developed to strengthen the clinician-support component. Parents indicated initial acceptability of the online prototype while reiterating the value of including face-to-face support. Conclusions: This study co-designed an online, clinician-supported parenting program for future embedding within adult mental health settings. The findings highlight key considerations for developing and implementing technology-assisted interventions that promote family-focused care for parents seeking mental health support. Full article