Faisal Binsar

BACKGROUND Implementing artificial intelligence (AI) in healthcare involves more than technological capability because adoption is shaped by trust, clinical workflows, governance, organisational conditions, and patient safety. However, longitudinal evidence from professional healthcare discussions remains limited. OBJECTIVE This study aimed to identify longitudinal patterns and implementation challenges related to trustworthy AI in healthcare and to synthesise these findings into a systems-oriented conceptual framework. METHODS A mixed-methods text mining study was conducted using longitudinal discussion data from an international healthcare quality learning community collected between September 2024 and July 2026. After data screening and preprocessing, 799 discussion posts were analysed using temporal analysis, term frequency–inverse document frequency, document-level keyword co-occurrence analysis, latent Dirichlet allocation (LDA), and automated Systems Engineering Initiative for Patient Safety (SEIPS) classification. The computational findings were integrated with contextual interpretation to develop a corpus-informed conceptual framework. RESULTS The analysis identified sustained discussion activity across the observation period and recurring attention to AI, healthcare, data, trust, bias, and patient safety. A four-topic LDA model identified four latent thematic structures within the discussion corpus. SEIPS classification showed that Tools and Technologies accounted for 390 of 799 classified comments (48.81%), while Tasks accounted for 321 (40.18%); together, these dimensions represented 88.99% of classified comments. Person, Organization, and Internal Environment were less frequently represented. Across the integrated analyses, trust was closely associated with concerns regarding transparency, governance, privacy, workflow integration, clinical decision-making, and patient safety. CONCLUSIONS AI implementation was discussed primarily in relation to technological characteristics and healthcare work processes, while human, organisational, and environmental considerations were less prominent in the automated lexical classification. These findings support a socio-technical interpretation of trustworthy AI implementation. The Trust-Centred Artificial Intelligence Implementation Framework provides an empirically informed conceptual synthesis of the patterns identified within the analysed discussion corpus. Further research is needed to test its transferability and validity across healthcare settings and implementation contexts. CLINICALTRIAL Not applicable.

BACKGROUND Implementing artificial intelligence (AI) in healthcare requires more than technological capability, as successful adoption depends on trust, organisational readiness, clinical workflows, governance, and patient safety. However, evidence describing how healthcare professionals collectively perceive these implementation challenges remains limited. OBJECTIVE This study aims to investigate the dominant implementation challenges of AI in healthcare through professional discourse and to develop a comprehensive, empirically grounded conceptual implementation framework. METHODS A mixed-methods study was conducted using longitudinal discussion data from an international healthcare quality learning community between September 2024 and July 2026. Following data cleaning, 799 discussion posts were analysed using descriptive text analytics, keyword co-occurrence analysis, latent topic modelling, and systems-based interpretation to identify core implementation barriers and synthesize a new framework. RESULTS Trust emerged as the primary implementation concern, associated with data governance, transparency, clinical decision-making, workflow integration, and safety. Topic modelling identified four principal domains: trustworthy implementation, patient safety, data governance, and clinical integration. Systems-based interpretation showed challenges concentrated within technological and workflow dimensions, with organisational factors receiving less attention. These insights formed the Global Trust-Centred Artificial Intelligence Implementation Framework (GTAIF), positioning trust as the core mechanism linking technology, readiness, governance, and quality. CONCLUSIONS Successful implementation of artificial intelligence should be understood as a sociotechnical transformation rather than a purely technological endeavour. The proposed framework provides an empirically grounded systems perspective that may support healthcare leaders and policymakers in implementing safe, trustworthy, and sustainable AI within healthcare organisations. CLINICALTRIAL Not applicable.

Artificial intelligence (AI) is transforming healthcare delivery, with telemedicine emerging as one of its most viable and socially impactful applications, raising important questions about how public trust is formed and sustained in AI-enabled healthcare environments. Yet, despite rapid technological adoption, public trust in AI-enabled telemedicine remains insufficiently understood, particularly across the diverse online environments where perceptions are collectively shaped. This study employs a sociotechnical, multi-platform big data analytics approach to quantify and interpret public trust in large-scale digital discourse. A total of 25,396 online mentions collected between 14 September and 14 October 2025 from news, social media, blogs, video, and web platforms were analyzed using a hybrid framework combining lexicon-based sentiment analysis ( VADER ), transformer-based contextual modeling ( RoBERTa ), and topic modeling ( BERTopic ). The results indicate a consistently positive sentiment orientation ( aggregate Trust Index = +0.84 ), suggesting a favorable public outlook toward AI-enabled telemedicine during the observation period. Affective trust is more prominent in visual and community-based media, while cognitive and structural trust are more salient in institutional and professional contexts. These results inform an integrated conceptual model of public trust formation in AI-enabled telemedicine, conceptualizing trust as a multidimensional sociotechnical construct. Rather than introducing a novel methodology, this study demonstrates the integrative application of established computational techniques to capture public sentiment at scale. These insights provide practical guidance for healthcare providers, policymakers, and AI developers to design more transparent, patient-centered, and trustworthy telemedicine systems.

Objectives This systematic review examines how patient-centered care (PCC) is conceptualized when mediated by artificial intelligence (AI), identifies governance mechanisms shaping AI-enabled PCC, and assesses implications for health equity at health system and population levels. Methods Following PRISMA 2020 guidelines, a theory-driven systematic review was conducted using Scopus and PubMed/Medline. Peer-reviewed studies published between 2020 and 2025 were screened based on relevance to AI-enabled PCC, governance, and equity. Thirty-two studies met the inclusion criteria and were synthesized through a theory-driven narrative approach integrating AI mediation, governance structures, and equity mechanisms. Results Findings indicate a shift in PCC from interpersonal clinical interactions toward a system-level capability embedded in digital infrastructures, algorithms, and organizational arrangements. Governance mechanisms, including regulatory oversight, accountability frameworks, and participatory design, emerged as mediators between AI technologies and patient-centered outcomes. Evidence suggests AI-enabled PCC may generate uneven equity effects, potentially mitigating or exacerbating disparities depending on governance capacity and system design. Conclusion AI reshapes PCC as a governance and equity challenge rather than a technological innovation, underscoring the need for stronger governance to ensure equitable, system-wide patient-centered outcomes.

Background: The Icarus Paradox in health care refers to the tension between the ambition to succeed as a specialist doctor and the limitations of the medical education system. Indonesia aspires to produce quality doctors, yet limited infrastructure and resources hinder the educational journey of prospective specialists. Objective: This study aimed to identify the Icarus Paradox in Indonesia's specialist medical education by examining prospective specialist medical students and the quality of health services and by analyzing how this paradox is reflected in society’s perspectives. Methods: Using a convergent mixed methods design, this study integrated quantitative content analysis of 5047 online reviews across multiple platforms with qualitative thematic and cognitive analysis using NVivo 14, combining sentiment classification and topic coding. Results: Twitter contributed 573 (11.3%) of 5047 reviews, with 218 (38%) negative, 251 (43.8%) neutral, and 104 (18.2%) positive entries. TikTok generated 282 (5.6%) reviews, the majority being neutral (n=225, 79.5%). YouTube produced 96 (1.9%) reviews, with 89 (92.7%) neutral entries. News platforms exhibited the largest volume (n=3040, 60.2%) of reviews, with 2885 (94.9%) neutral, 105 (3.5%) positive, and 50 (1.6%) negative entries. Blogs and websites contributed 353 (7%) and 692 (11.3%) reviews, respectively, with neutral sentiment dominating (n=329, 93.2%, for blogs and n=599, 86.6%, for websites). Three cognitive perspectives demonstrated the Icarus Paradox in the Indonesian medical education system: education system, society’s views of students, and health care services. Although there are aspirations to improve education and health care quality, these ambitions often collide with structural challenges, such as resource shortages, heavy workloads, and limited accessibility, which link directly to cognitive themes of stress, resilience, and ethical dilemmas. We proposed a conceptual model to illustrate these dynamics. Conclusions: Our findings offer insights into the Icarus Paradox in Indonesia’s medical education system, highlighting its complexity and reinforcing the need for systemic reform. Beyond academic relevance, the findings also emphasize the importance of strengthening student mental health support, ensuring equitable access to health care, and enhancing regulatory oversight of training. This was not a clinical trial. Although limited by reliance on online reviews, the results underscore the urgent need for targeted policy interventions in medical education and health care services. Trial Registration: ClinicalTrials.gov registration: NCT123456

Background/Objectives: Digital transformation is increasingly essential for healthcare organizations to improve operational efficiency and service quality. However, in developing countries such as Indonesia, many lower-middle-class hospitals lag due to limited financial, human, and infrastructural resources. This study examines how environmental dynamism—comprising regulatory changes, market pressures, and technological shifts—affects the digital capabilities of these hospitals. Methods: A quantitative, cross-sectional survey was conducted in Class C and D hospitals across Indonesia. Respondents included hospital directors, deputy directors, and IT heads. Data were collected through structured questionnaires measuring environmental dynamism and digital capability using a six-point Likert scale. Reliability testing yielded Cronbach’s alpha values above 0.96 for both constructs. Correlation analysis was performed to examine the relationship between environmental dynamism and digital capability. Results: Findings reveal a weak positive correlation (r = 0.1816) between environmental dynamism and digital capability. Although external factors such as policy regulations and technological competition encourage digital adoption, hospitals with limited internal resources struggle to translate these pressures into sustainable transformation. Key challenges include low ICT budgets, inconsistent staff training, and insufficient infrastructure. Conclusions: The results suggest that environmental change alone cannot drive digital readiness without internal capacity development. To foster resilient digital healthcare ecosystems, policy interventions should integrate regulatory frameworks with practical support programs that strengthen resources, leadership, and human capital in lower-middle-class hospitals.