Carlos Martin Ardila

Background/Objectives: High-throughput molecular profiling identifies potential determinants of treatment resistance, but the extent to which these discoveries support reversal of acquired resistance or enhanced treatment sensitivity in oral squamous cell carcinoma (OSCC) remains unclear. This review evaluated evidence linking molecular discovery, target-directed intervention, and improved anticancer treatment response. Methods: PubMed/MEDLINE, Scopus, and Embase were searched, supplemented by Google Scholar and citation searching. Two reviewers independently screened reports, extracted data, and assessed methodological limitations. Eligible investigations connected integrated multi-omics (Category A) or single-omics/broad functional screening (Category B) with target-specific perturbation and a treatment-response comparison. Evidence was synthesized without meta-analysis. Results: Of 1091 database records, 816 unique records were screened and 40 full-text reports assessed; 29 were excluded. Eleven reports were included from databases and two from backward citation searching (total, 13; Category A, four; Category B, nine). Nine reports examined acquired-resistance reversal, whereas four demonstrated sensitization without acquired-resistance reversal. The highest therapeutic evaluation setting was cell culture (three reports), patient-derived organoids (one), or animal models (nine). Randomization was explicitly described in three of nine animal-treatment reports; allocation concealment and outcome blinding were generally unclear. Conclusions: Omics-informed perturbations were associated with improved treatment responses in preclinical OSCC models. Heterogeneous interventions, uneven methodological safeguards, and limited independent validation prevent conclusions about clinical efficacy. Treatment-specific causal experiments and validation in authentic patient-derived systems are needed.

Background/Objectives: Salivary-gland biopsy is central to Sjögren’s disease (SjD) assessment, but conventional measures incompletely capture glandular heterogeneity. This systematic review evaluated artificial intelligence (AI)/machine learning (ML) applications to SjD salivary-gland histopathology and molecular profiling, including biological and translational validation. Methods: PubMed, Scopus, and Embase were searched. Eligible studies used salivary-gland tissue as the primary analytical substrate or provided substantive glandular validation of systemic AI/ML findings. Evidence was synthesized functionally because of methodological heterogeneity. Results: Twenty-five studies were included. AI/ML identified histopathological and molecular phenotypes spanning immune/interferon activation, tissue architecture and immune-cell organization, epithelial stress and metabolic/cell-death remodeling, and translational response. Only a limited subset progressed beyond computational or tissue-level confirmation to functional perturbation, pharmacological testing, in vivo validation, or human treatment-response assessment. Recurrent public-dataset reuse, small cohorts, heterogeneous pipelines, and limited independent validation constrained reproducibility. Conclusions: AI/ML supports multidimensional SjD glandular phenotyping and shows emerging translational potential, but demonstrated clinical or therapeutic utility remains unestablished. Prospective multicenter validation is required before AI-derived glandular phenotypes can inform routine patient stratification or therapeutic decision-making.

Background/Objectives: Computational and artificial intelligence (AI)-enabled imaging biomarkers are increasingly being investigated for immunotherapy-response assessment in oral squamous cell carcinoma (OSCC), but the evidence is heterogeneous. This systematic review identified, critically appraised, and synthesized imaging biomarkers used to predict or assess observed immunotherapy response in OSCC. Methods: PubMed/MEDLINE, Embase, and Scopus were searched from inception to 31 July 2026, without language restrictions. The protocol was registered in PROSPERO. Eligible studies evaluated quantitative, radiomic, machine-learning, or related medical-imaging biomarkers against pathological or radiological response. Risk of bias was assessed using design-specific tools, supplemented by a radiomics-specific methodological appraisal. A structured Synthesis Without Meta-analysis was prespecified. Results: Seven reports were included, covering pretreatment prediction, early/on-treatment dynamic assessment, and post-neoadjuvant/preoperative response assessment. Pretreatment-computed tomography and magnetic resonance imaging radiomics showed discriminatory performance, whereas baseline positron emission tomography uptake alone was not consistently associated with pathological response. Longitudinal fibroblast activation protein inhibitor positron emission tomography changes were associated with a major pathological response, and magnetic resonance imaging habitat and longitudinal habitat models showed higher within-study discrimination when combined with clinical information. No computational model had independent external validation specifically in an OSCC population, and no three independent studies were sufficiently comparable for meta-analysis. Conclusions: Computational imaging is promising across several stages of immunotherapy-response assessment. However, based on current evidence, none of the reviewed imaging biomarkers or models can yet be considered ready for routine clinical use. Prospective multicenter OSCC-specific validation and standardized imaging and analytical pipelines are needed to establish generalizability and clinical utility.

Traumatic dental injuries require rapid, accurate, and context‐sensitive decisions, particularly during emergencies such as tooth avulsion, luxation injuries, and trauma involving the primary dentition. Artificial intelligence (AI) chatbots and large language models (LLMs) are increasingly used by patients, caregivers, students, and clinicians for immediate information and decision support, yet their reliability in dental trauma remains uncertain. This systematic review aimed to evaluate the accuracy, consistency, safety, guideline concordance, and clinical applicability of AI chatbots and LLMs for traumatic dental injury management and decision support. PubMed, Scopus, and Embase were searched through April 2026. Eligible studies evaluated AI‐generated responses against clinical guidelines, expert assessment, validated dental trauma scenarios, photographs, real cases, standardized questions, or other reproducible reference standards. Twenty‐eight studies met the eligibility criteria. A function‐based narrative synthesis classified the evidence into five translational functions: patient‐ and caregiver‐facing emergency guidance, guideline‐based diagnostic and management benchmarking, domain‐specific or document‐grounded workflows, multimodal and real‐case validation, and avulsion‐specific prognostic or advanced decision support. An exploratory meta‐analysis of nine study‐level estimates with comparable binary outcomes demonstrated a pooled proportion of accurate or guideline‐concordant responses of 81.7%, although heterogeneity was very high. Across studies, performance varied according to model, prompt format, reference standard, input type, and assessment methodology. Safety concerns included incomplete recommendations, misleading information, inconsistent responses, limited readability, unreliable references, and injury‐specific performance variability. Overall, current evidence indicates that AI chatbots and LLMs can support dental trauma information retrieval and supervised clinical decision support, particularly when constrained by curated guidelines or domain‐specific workflows. However, the available evidence remains limited by substantial methodological heterogeneity and limited real‐world validation and therefore does not support the autonomous use of these systems for emergency traumatic dental injury management. Trial Registration: PROSPERO; CRD420261394046

Background/Objectives: Calcium-silicate-based materials, many of which are commonly classified as bioceramics, are increasingly used for pulpotomy in vital primary teeth. This umbrella review synthesized systematic reviews and meta-analyses evaluating their clinical and radiographic outcomes. Methods: Eligible reports were systematic reviews of human primary-tooth pulpotomy involving calcium-silicate-based materials and reporting clinical and/or radiographic outcomes. MEDLINE/PubMed, Embase, and Scopus were searched from inception through June 2026. Methodological quality was appraised using AMSTAR-2, overlap was quantified using the Corrected Covered Area (CCA), and certainty was assessed using a GRADE-informed approach. Findings were integrated through structured narrative synthesis and review-level functional meta-synthesis. The protocol was registered in PROSPERO (CRD420261338396). Results: Eleven systematic reviews were included, comprising 78 unique primary-study reports and 180 study occurrences; aggregate participant totals were not calculated because of primary-study overlap. AMSTAR-2 confidence ranged from critically low to moderate, and overlap was high (CCA 13.1%). MTA and Biodentine showed broadly comparable clinical and radiographic outcomes through 24 months. Evidence for Biodentine versus formocresol was less consistent: one meta-analysis reported more favorable radiographic outcomes with Biodentine; whereas, another found no substantial difference; clinical superiority was not consistently demonstrated. Comparisons with ferric sulfate were generally inconclusive, and evidence for newer calcium-silicate-based formulations remained limited. No material demonstrated sustained long-term superiority. Certainty was predominantly low, with very low certainty for several sparsely studied comparisons. Conclusions: Contemporary calcium-silicate-based materials show high clinical and radiographic success in vital primary-tooth pulpotomy, but comparative conclusions remain constrained by methodological limitations, high overlap, and limited certainty.