The global rise in cancer incidence and mortality represents a major challenge for modern healthcare. Although current screening programs rely mainly on histological or immunological biomarkers, cancer is a multifactorial disease in which biological, psychological, and behavioural determinants interact. Psychological dimensions such as stress, anxiety, and depression may influence vulnerability and disease evolution through neuro-endocrine, immune, and behavioural pathways, especially by affecting adherence to therapeutic recommendations. However, these dimensions remain underexplored in current screening workflows. This review synthesizes current evidence on the integration of biological markers (tumor and inflammatory biomarkers), psychometric profiling (stress, depression, anxiety, personality traits), and behavioural digital phenotyping (facial micro-expressions, vocal tone, gait/posture metrics) for potential early cancer risk evaluation. We examine recent advances in computational sciences and artificial intelligence that could enable multimodal signal harmonization, structured representation, and hybrid data fusion models. We discuss how structured computational information management may improve interpretability and may support future AI-assisted screening paradigms. Finally, we highlight the relevance of digital health infrastructure and telemedical platforms in strengthening accessibility, continuity of monitoring, and population-level screening coverage. Further empirical research is required to determine the true predictive contribution of psychological and behavioural modalities beyond established biological markers.

Background/Objectives: Artificial intelligence (AI) is entering clinical dentistry more quickly than the professional, ethical, and regulatory frameworks intended to govern it. Responsible implementation depends not only on diagnostic performance but on trust, professional autonomy, and clear accountability—dimensions rarely examined across the different stakeholder groups who shape dental care. This study assessed perceptions of trustworthy AI, accountability, and regulatory preparedness among dental and non-dental stakeholders in Romania. Methods: A multidisciplinary cross-sectional survey using purposive, non-probability sampling, reported in accordance with the CROSS guideline, was administered to dental professionals, legal professionals, forensic physicians, and other healthcare stakeholders. The instrument addressed AI literacy, governance and accountability, ethics and transparency, and clinical implementation. Responses were compared across professional groups using chi-square and Kruskal–Wallis tests with Benjamini–Hochberg or Holm correction, and the questionnaire structure was examined through an exploratory psychometric assessment. Of 304 submissions, 287 were retained for analysis. Results: Agreement on ethical and governance safeguards was high (84–91%), including testing on diverse populations, independent auditing, and patient disclosure. Between-group differences concentrated in AI awareness, literacy, and educational exposure, as well as in governance and accountability, whereas ethics and transparency items showed broad consensus. Respondents attributed responsibility for AI-related outcomes to clinicians (77.4%), developers (49.5%), and healthcare institutions (28.3%). Only a minority reported AI Act awareness (32.8%) or previous participation in AI-related courses or conferences (25.2%). Conclusions: Romanian stakeholders included in this multidisciplinary sample share strong normative expectations for trustworthy clinical AI but report limited regulatory awareness and educational exposure. These findings support the development of structured AI preparedness initiatives and clearer governance frameworks reflecting stakeholders’ perceptions of responsibility as extending across clinicians, developers, and healthcare institutions. Professional certification before clinical AI use emerged as a specific stakeholder preference.

Background: Psychological distress has been frequently reported in individual autoimmune diseases, but evidence regarding shared psychological characteristics across heterogeneous autoimmune conditions remains limited. Objective: This study compared stress vulnerability, exposure to major life events, state and trait anxiety, and Type A behavioral characteristics between adults with autoimmune diseases and controls. Methods: This cross-sectional case–control study included 389 adults: 204 patients with autoimmune diseases and 185 controls. Participants completed the Stress Vulnerability Scale, Holmes–Rahe Life Stress Inventory, State–Trait Anxiety Inventory, and Jenkins Activity Survey. Between-group differences, effect sizes, and correlations among psychometric variables were examined. Results: Patients with autoimmune diseases had significantly higher stress vulnerability scores (Cohen’s d = 0.39) and Holmes–Rahe life-event burden (d = 0.54) than controls. State anxiety (d = 0.52) and trait anxiety (d = 0.68) were also higher (all p < 0.001). Type A behavioral characteristics did not differ significantly between groups. State and trait anxiety showed the strongest correlation (r = 0.755, p < 0.001). Disease duration was positively associated with Holmes–Rahe scores but not with stress vulnerability or anxiety. Conclusions: Adults with autoimmune diseases showed a higher psychological burden across several complementary psychometric dimensions. These findings support consideration of psychological screening within multidisciplinary autoimmune disease care. Longitudinal and disease-specific studies integrating biological, psychological, and social factors are required to clarify temporal and potentially bidirectional relationships.

Background/Objectives: Artificial intelligence (AI) is entering clinical dentistry more quickly than the professional, ethical, and regulatory frameworks intended to govern it. Responsible implementation depends not only on diagnostic performance but on trust, professional autonomy, and clear accountability—dimensions rarely examined across the different stakeholder groups who shape dental care. This study assessed perceptions of trustworthy AI, distributed professional responsibility, and regulatory preparedness among dental and non-dental stakeholders in Romania. Methods: A multidisciplinary cross-sectional survey, reported in accordance with the CROSS guideline, was administered to dental professionals, legal professionals, forensic physicians, and other healthcare stakeholders. The instrument addressed AI literacy, governance and accountability, ethics and transparency, and clinical implementation. Responses were compared across professional groups using chi-square and Kruskal–Wallis tests with Benjamini–Hochberg or Holm correction, and the questionnaire structure was evaluated by exploratory and confirmatory factor analysis. Of 304 submissions, 287 were retained for analysis. Results: Agreement on ethical and governance safeguards was high (84–91%), including testing on diverse populations, independent auditing, and patient disclosure. Between-group differences concentrated in AI awareness, literacy, and education, and in governance and accountability, whereas ethics and transparency items showed broad consensus. Accountability was distributed across clinicians (77.4%), developers (49.5%), and healthcare institutions (28.3%), and only a minority reported AI Act awareness (32.8%) or AI-specific training (25.2%). Conclusions: Romanian stakeholders share strong normative expectations for trustworthy clinical AI but remain underprepared in regulatory and educational terms. Bridging this gap requires structured training, certification before clinical use, and governance frameworks that recognize responsibility as shared across the professionals and institutions involved.

Background: The Mediterranean diet (MD) has well-established health benefits, yet modern food systems are driving a nutrition transition. Quantifying how Mediterranean countries have shifted relative to global trends is essential for evidence-based public health. Aim: Dietary changes between 1990 and 2018 were examined in Mediterranean coastline countries (MED) in comparison with high-income non-Mediterranean countries (HIC non-MED) and middle-/low-income non-Mediterranean countries (MLIC non-MED). Methods: Using the Global Dietary Database, within-country changes (1990 vs. 2018) for 13 food categories were analysed across 185 nations in three groups: MED countries (n = 21), HIC non-MED (n = 18), and MLIC non-MED (n = 146). MED countries were further grouped into Southern Europe (SE), Western Asia (WA), and North Africa (NA). Paired unweighted country-level analyses were the primary analyses; population-weighted analyses were sensitivity checks. Benjamini–Hochberg’s false-discovery-rate correction and centred log-ratio compositional analysis were applied. Results: MED seafood intake rose from 20.5 to 33.8 g/day (Δ = +13.3 g/day; 95% CI: +6.2 to +18.4; q = 0.003), and non-starchy vegetables increased from 135.5 to 173.6 g/day (Δ = +38.0 g/day; q = 0.626). Refined/starchy carbohydrate intake rose by 63.3 g/day in MED countries while declining by 33.6 g/day in HIC non-MED countries (between-group divergence +96.9 g/day; q = 0.006). Nuts and seeds rose across all groups; legumes declined modestly in MED countries. Saturated fatty acids + omega-6 polyunsaturated fatty acids (% kcal) rose by +0.6 percentage points in MED countries but fell in HIC non-MED countries. Monounsaturated fatty acids were stable. MED-WA showed the greatest overall divergence from the SE 1990 pattern. Conclusions: These findings highlight a public health paradox: the MD is globally promoted as a model of healthy eating, yet adherence within its region of origin is declining. Targeted, region-specific strategies are urgently needed to preserve beneficial MD elements while addressing emerging diet-related risks.